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  • Why is the Metaverse attracting attention in the medical field? Introduction examples and future issues

    Why is the Metaverse attracting attention in the medical field? Introduction examples and future issues

    The use of the metaverse in the medical field is expanding. It is estimated that by utilizing 5G and new technologies, the growth rate of the Metaverse will be over 30% by 2030.
    Therefore, in this article, we will explain why the Metaverse is attracting attention in the medical field, what the challenges are in promoting it, and examples of what has been done to date.

     

     

    Metaverse is expected to grow by more than 30% by 2030 in the medical field

    The use of the metaverse is attracting attention in the medical field. This is because the use of virtual space is thought to have a wide range of uses, such as enabling medical procedures that would be difficult in the real world, and medical examinations using avatars.

    In 2021, the market size of Metaverse in healthcare was not very large. However, in the medical field, the metaverse is expected to grow significantly by 2030, as it is believed that it can be used in a wide variety of ways.
    The use of the Metaverse in the medical industry is expanding, with examples of surgeries being performed using AR smart glasses.

    Why metaverse medicine is attracting attention

    There are three reasons why metaverse medicine is attracting attention. Let’s look at them one by one.

    Introduction can eliminate regional disparities in medical quality

    In recent years, “medical disparities” have been occurring all over the world, including Japan.
    For example, medical institutions are concentrated only in urban areas, making it difficult to receive advanced medical care in rural areas. According to the Ministry of Health, Labor and Welfare’s “Measures to prevent uneven distribution of doctors”, the regional uneven distribution of doctors continues, and some regions are experiencing a shortage of doctors.

    Metaverse is also expected to solve these problems. This is because VR space not only allows us to connect with people around the world in real time, but also makes it easier to provide medical services to patients in remote areas. Therefore, we can expect to eliminate regional disparities in the quality of medical care.

    Advances in 5G and communication technology have made real-time remote surgery possible.

    In the field of digital medicine, it is expected that paper prescriptions will disappear in the next few decades and transition to digital treatment in the metaverse space.
    In order to perform digital medicine using this metaverse space without any problems, an environment is required that allows for high-speed and high-capacity communication without problems.

    What is expected of this is the communication technology called 5G. It has the characteristics of “high-capacity, high-speed communication,” “high reliability and low latency,” and “multiple simultaneous connections.”
    Even now, it has a proven track record of being used for real-time remote control of surgical support robots.
    5G, which is capable of faster speeds and higher capacity communications, is said to be able to be used in digital medicine, and is expected to become a new field of digital medicine.

    Metaverse medical care has become a system that is easy for patients to consult.

    In the Metaverse space, you create an “avatar” that is your alter ego and communicate with it.
    Communication using this avatar is attracting attention as a system that makes it easy for patients to consult.

    Kohei Yoshioka, a psychiatrist who runs Metaverse Clinic, a medical consultation community on the Metaverse, says the following.
    “I feel that in the metaverse space where we use alter egos called avatars, it is easier to self-disclose because there is no need to actually communicate face-to-face.”

    Currently, we are using the Metaverse space as a place for consultation rather than medical treatment, but I believe that by making it a place for communication, it will be easier for patients to seek advice.

    In this way, metaverse medicine is also beneficial to patients.

    Three medical fields aiming to utilize the metaverse

    The three specific medical fields that aim to utilize the metaverse include:

      • Medical (VR training)
      • Pharmaceuticals (sharing of medical product information)
      • Drug discovery (time and cost reduction)

    Medical (VR training)

    In the medical field, the metaverse of VR training is being utilized.
    Specifically, this includes training for surgeons regarding surgical procedures and social skills training.
    For example, in the United States, we prepare a real surgical scenario, create a virtual operating room in the metaverse space, and actually perform the procedure in that space.
    The virtual operating room also helps in training for complex procedures. Additionally, in Japan, Jolly Good Inc. is conducting social skills training on developmental disorders on Metaverse.
    We have progressed to the stage where we can promote lifestyle support and employment support for patients.

    Pharmaceuticals (sharing of medical product information)

    In the pharmaceutical industry , the Metaverse is being used as a place to share medical product information and support communication between doctors and patients.
    Specifically, we are using virtual MR to effectively share medical product information.

    For example, at Tsumura Co., Ltd., a major pharmaceutical company, AI presents content that matches the story to medical professionals in a 3D version of MR.
    Additionally, Astellas Pharma Inc. has begun experimenting with using virtual MR to project patients’ skeletal information in 3D into the real world. For example, it has the benefit of making it easier for patients to understand their own health status when they see their bone density decreasing.

    Drug discovery (time and cost reduction)

    Drug discovery is always time-consuming and costly. Metaverse is being used to reduce the time and cost of drug discovery.
    At Chugai Pharmaceutical Co., Ltd., researchers use the software “Nanomu” to discover drugs in the metaverse.
    Things that could only be confirmed in two dimensions can now be confirmed in 3D models down to the atomic level, making it possible to create new ideas and test whether they are actually effective in the metaverse space, something never seen before. It provides a sense of speed.

    How will the two types of telemedicine (including metaverse medicine) change with 5G?

     

    There are two types of telemedicine. The first is medical care in which doctors and patients in remote locations use tools to communicate in real time.
    The other is D to D (Doctor to Doctor), in which other doctors provide online support for the medical care of doctors in remote locations, and D to P (Doctor to Patient), in which doctors provide medical care directly to patients.
    In this chapter, we will explain how these telemedicine systems will change with 5G.

    D to D (Doctor to Doctor)

    D to D (Doctor to Doctor) is when doctors in remote locations use online technology to support doctors who are actually providing medical care.
    Real-time support is essential for doctors who are actually providing medical care. For example, when providing support to doctors providing medical care at a disaster site, every minute can mean the difference between life and death for a patient.
    Utilizing 5G will enable high-speed communication, making it possible to provide support without time lag. The result is faster medical care for patients.

    D to Patient (Doctor to Doctor)

    D to P (Doctor to Patient) is when a doctor in a remote location actually provides medical care to a patient. Currently, online medical consultations are still being conducted, but they are limited to interviewing and almost no actual medical care is provided.
    This is because patient data is exchanged when actually providing medical care. This patient data will be large in volume, so the use of 5G is essential. By utilizing 5G, data can be exchanged smoothly in real time and can be expected to be useful in actual medical care.

    There are two issues that need to be resolved in promoting metaverse medicine.

    Although it is certain that metaverse medicine will continue to expand in the future, there are still issues that need to be resolved. Let’s take a closer look at these two issues.

    Safety issues

    First, there is the issue of safety. When actually performing medical treatment, a multi-layered check system is in place, leading to peace of mind for patients.
    Even when providing medical care in the metaverse, safety must be ensured just as in real life. Creating a safe environment for patients can be said to be a major challenge for metaverse medicine.

    In order to solve safety issues, it is essential not only to collaborate with medical professionals, but also with businesses that provide metaverse space.
    No matter how wonderful a Metaverse space is created, if there are doubts about its safety, it will be difficult to promote it.

    Security issues

    Metaverse space is a virtual space provided online. Therefore, addressing cyber security is essential.
    If the security of the Metaverse is weak, it may be infiltrated by malicious hackers. If such hackers are allowed to infiltrate, medical practices may come to a halt, and patients’ personal information may also be leaked.
    In order to solve these security issues, it is necessary not only to have a strong environmental infrastructure that does not allow intrusion from outsiders, but also to have laws in place to deal with incidents.

    Five examples of using the Metaverse in medical care

    In this chapter, we will introduce five examples of how the Metaverse is actually used in medical care.

    Juntendo Virtual Hospital (virtual space imitating a hospital)

    Juntendo University is collaborating with IBM Japan, Ltd. to construct the “Juntendo Virtual Hospital” within the Metaverse space.
    The Juntendo Virtual Hospital is a place for medical professionals and patients to interact, allowing patients to experience a simulated medical procedure before coming to the hospital. The purpose is to alleviate patients’ anxiety through simulated experiences.

    VR vaccine injection simulator (VR of intramuscular injection)

    As the name suggests, a VR vaccine injection simulator allows you to simulate an injection in VR. Imma Create Co., Ltd. has released a service for training purposes, which allows you to intuitively learn how to give intramuscular injections in VR space. In addition, simulators have been released for training purposes when learning the procedures for vaccination against the new coronavirus, and are expected to be more effective than learning through classroom lectures.

    Mediverse OCD (virtual space such as seminars)

    MediVerse OCD aims to create new services by building a platform that connects medical professionals or medical professionals and companies in a metaverse space.
    It can be used for a variety of purposes depending on the user, including sharing case information, gathering research knowledge from medical professionals, and holding simple study sessions and study groups.

    Holoeyes (VR software for medical education)

    Holoeyes is VR software for learning medical education or clinical practice.
    Since the purpose is medical education, 3D information of the human body can be accurately reproduced in VR, making it possible to learn visually. Furthermore, it can also be used to simulate actual procedures. You can also communicate with other users in remote locations in real time via avatars.

    Introducing 5 papers that will help you understand future metaverse medicine

    In this chapter, we will introduce five papers that will be useful in deepening our knowledge of metaverse medicine, which will be promoted in the future.

    Papers on cardiology

    The use of the metaverse in cardiology is called the CardioVerse.
    In cardiology, by utilizing this Cardioverse, it is possible to receive specialized services in 3D space even in remote locations, and the quality of medical care can be improved by integrating surgical robots and navigation systems. It has been reported that the effect of “increases the

    Papers on ophthalmology

    In ophthalmology, there are many things related to AR and VR.
    For example, “Using AR and VR to help patients notice visual abnormalities” and “Making it easier to train medical professionals in optometry and surgery.”
    The use of AR and VR can help patients in remote locations decide whether to travel to the hospital for testing. Other training simulators introduced include the EyeSi simulator (VR Magic) and MicroVisTouch (Immersive Touch).

    Papers on medical education

    In the field of medical education, we classify the metaverse into four types and discuss how to utilize each type. The four types of medical education are ” Augmented Reality ,” “Lifelog,” “Mirror World,” and “Virtual Reality.”
    Each type is discussed in the paper as follows.

      • Augmented reality uses augmented reality t-shirts to explore inside the human body
      • Lifelog is introducing a service that can utilize accumulated biometric information in the medical field.
    • Mirror World is developing a platform that contributes to scientific research through games.
    • In virtual reality, use of avatar services and platforms

    Papers on gastroenterology

    In gastroenterology, papers have been published on the clinical results of endoscopy training actually conducted in the metaverse space.
    Specifically, this includes surgical training using endoscopic equipment in the Metaverse, treatment by senior doctors in the Metaverse, and discussion of surgical procedures by experts in the Metaverse via avatars.
    Training and discussions held in the Metaverse space have led to results such as improved experience for doctors, shorter surgical times, and lower complication rates.

    Papers on body transformation research cases

    Body transformation is a phenomenon in which emotions, cognition, and behavior change by transforming from one’s own body to another body in VR space. Research on this body transformation is underway in Japan.
    Specifically, the following cases have been reported.

      • When white people use black avatars, their sense of discrimination is reduced.
      • Using a child avatar leads to overestimation of object size and childish behavior.
    • Being a superhero and helping others makes you more likely to act altruistically.

    In other words, using an avatar with a different personality in VR space will have an impact on your body and way of thinking in the real world.

    summary

     

    It is no exaggeration to say that the market for the use of the metaverse in medical care is growing, and there is no doubt that it will continue to develop. With the use of 5G, the impact of the Metaverse on medical care will increase. The range of applications will continue to expand in a variety of medical fields, including telemedicine and real-time communication between doctors and patients.

     

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  • Why is the Metaverse attracting attention in the medical field? Introduction examples and future issues

    Why is the Metaverse attracting attention in the medical field? Introduction examples and future issues

    The use of the metaverse in the medical field is expanding. It is estimated that by utilizing 5G and new technologies, the growth rate of the Metaverse will be over 30% by 2030.
    Therefore, in this article, we will explain why the Metaverse is attracting attention in the medical field, what the challenges are in promoting it, and examples of what has been done to date.

     

     

    Metaverse is expected to grow by more than 30% by 2030 in the medical field

    The use of the metaverse is attracting attention in the medical field. This is because the use of virtual space is thought to have a wide range of uses, such as enabling medical procedures that would be difficult in the real world, and medical examinations using avatars.

    In 2021, the market size of Metaverse in healthcare was not very large. However, in the medical field, the metaverse is expected to grow significantly by 2030, as it is believed that it can be used in a wide variety of ways.
    The use of the Metaverse in the medical industry is expanding, with examples of surgeries being performed using AR smart glasses.

    Why metaverse medicine is attracting attention

    There are three reasons why metaverse medicine is attracting attention. Let’s look at them one by one.

    Introduction can eliminate regional disparities in medical quality

    In recent years, “medical disparities” have been occurring all over the world, including Japan.
    For example, medical institutions are concentrated only in urban areas, making it difficult to receive advanced medical care in rural areas. According to the Ministry of Health, Labor and Welfare’s “Measures to prevent uneven distribution of doctors”, the regional uneven distribution of doctors continues, and some regions are experiencing a shortage of doctors.

    Metaverse is also expected to solve these problems. This is because VR space not only allows us to connect with people around the world in real time, but also makes it easier to provide medical services to patients in remote areas. Therefore, we can expect to eliminate regional disparities in the quality of medical care.

    Advances in 5G and communication technology have made real-time remote surgery possible.

    In the field of digital medicine, it is expected that paper prescriptions will disappear in the next few decades and transition to digital treatment in the metaverse space.
    In order to perform digital medicine using this metaverse space without any problems, an environment is required that allows for high-speed and high-capacity communication without problems.

    What is expected of this is the communication technology called 5G. It has the characteristics of “high-capacity, high-speed communication,” “high reliability and low latency,” and “multiple simultaneous connections.”
    Even now, it has a proven track record of being used for real-time remote control of surgical support robots.
    5G, which is capable of faster speeds and higher capacity communications, is said to be able to be used in digital medicine, and is expected to become a new field of digital medicine.

    Metaverse medical care has become a system that is easy for patients to consult.

    In the Metaverse space, you create an “avatar” that is your alter ego and communicate with it.
    Communication using this avatar is attracting attention as a system that makes it easy for patients to consult.

    Kohei Yoshioka, a psychiatrist who runs Metaverse Clinic, a medical consultation community on the Metaverse, says the following.
    “I feel that in the metaverse space where we use alter egos called avatars, it is easier to self-disclose because there is no need to actually communicate face-to-face.”

    Currently, we are using the Metaverse space as a place for consultation rather than medical treatment, but I believe that by making it a place for communication, it will be easier for patients to seek advice.

    In this way, metaverse medicine is also beneficial to patients.

    Three medical fields aiming to utilize the metaverse

    The three specific medical fields that aim to utilize the metaverse include:

      • Medical (VR training)
      • Pharmaceuticals (sharing of medical product information)
      • Drug discovery (time and cost reduction)

    Medical (VR training)

    In the medical field, the metaverse of VR training is being utilized.
    Specifically, this includes training for surgeons regarding surgical procedures and social skills training.
    For example, in the United States, we prepare a real surgical scenario, create a virtual operating room in the metaverse space, and actually perform the procedure in that space.
    The virtual operating room also helps in training for complex procedures. Additionally, in Japan, Jolly Good Inc. is conducting social skills training on developmental disorders on Metaverse.
    We have progressed to the stage where we can promote lifestyle support and employment support for patients.

    Pharmaceuticals (sharing of medical product information)

    In the pharmaceutical industry , the Metaverse is being used as a place to share medical product information and support communication between doctors and patients.
    Specifically, we are using virtual MR to effectively share medical product information.

    For example, at Tsumura Co., Ltd., a major pharmaceutical company, AI presents content that matches the story to medical professionals in a 3D version of MR.
    Additionally, Astellas Pharma Inc. has begun experimenting with using virtual MR to project patients’ skeletal information in 3D into the real world. For example, it has the benefit of making it easier for patients to understand their own health status when they see their bone density decreasing.

    Drug discovery (time and cost reduction)

    Drug discovery is always time-consuming and costly. Metaverse is being used to reduce the time and cost of drug discovery.
    At Chugai Pharmaceutical Co., Ltd., researchers use the software “Nanomu” to discover drugs in the metaverse.
    Things that could only be confirmed in two dimensions can now be confirmed in 3D models down to the atomic level, making it possible to create new ideas and test whether they are actually effective in the metaverse space, something never seen before. It provides a sense of speed.

    How will the two types of telemedicine (including metaverse medicine) change with 5G?

     

    There are two types of telemedicine. The first is medical care in which doctors and patients in remote locations use tools to communicate in real time.
    The other is D to D (Doctor to Doctor), in which other doctors provide online support for the medical care of doctors in remote locations, and D to P (Doctor to Patient), in which doctors provide medical care directly to patients.
    In this chapter, we will explain how these telemedicine systems will change with 5G.

    D to D (Doctor to Doctor)

    D to D (Doctor to Doctor) is when doctors in remote locations use online technology to support doctors who are actually providing medical care.
    Real-time support is essential for doctors who are actually providing medical care. For example, when providing support to doctors providing medical care at a disaster site, every minute can mean the difference between life and death for a patient.
    Utilizing 5G will enable high-speed communication, making it possible to provide support without time lag. The result is faster medical care for patients.

    D to Patient (Doctor to Doctor)

    D to P (Doctor to Patient) is when a doctor in a remote location actually provides medical care to a patient. Currently, online medical consultations are still being conducted, but they are limited to interviewing and almost no actual medical care is provided.
    This is because patient data is exchanged when actually providing medical care. This patient data will be large in volume, so the use of 5G is essential. By utilizing 5G, data can be exchanged smoothly in real time and can be expected to be useful in actual medical care.

    There are two issues that need to be resolved in promoting metaverse medicine.

    Although it is certain that metaverse medicine will continue to expand in the future, there are still issues that need to be resolved. Let’s take a closer look at these two issues.

    Safety issues

    First, there is the issue of safety. When actually performing medical treatment, a multi-layered check system is in place, leading to peace of mind for patients.
    Even when providing medical care in the metaverse, safety must be ensured just as in real life. Creating a safe environment for patients can be said to be a major challenge for metaverse medicine.

    In order to solve safety issues, it is essential not only to collaborate with medical professionals, but also with businesses that provide metaverse space.
    No matter how wonderful a Metaverse space is created, if there are doubts about its safety, it will be difficult to promote it.

    Security issues

    Metaverse space is a virtual space provided online. Therefore, addressing cyber security is essential.
    If the security of the Metaverse is weak, it may be infiltrated by malicious hackers. If such hackers are allowed to infiltrate, medical practices may come to a halt, and patients’ personal information may also be leaked.
    In order to solve these security issues, it is necessary not only to have a strong environmental infrastructure that does not allow intrusion from outsiders, but also to have laws in place to deal with incidents.

    Five examples of using the Metaverse in medical care

    In this chapter, we will introduce five examples of how the Metaverse is actually used in medical care.

    Juntendo Virtual Hospital (virtual space imitating a hospital)

    Juntendo University is collaborating with IBM Japan, Ltd. to construct the “Juntendo Virtual Hospital” within the Metaverse space.
    The Juntendo Virtual Hospital is a place for medical professionals and patients to interact, allowing patients to experience a simulated medical procedure before coming to the hospital. The purpose is to alleviate patients’ anxiety through simulated experiences.

    VR vaccine injection simulator (VR of intramuscular injection)

    As the name suggests, a VR vaccine injection simulator allows you to simulate an injection in VR. Imma Create Co., Ltd. has released a service for training purposes, which allows you to intuitively learn how to give intramuscular injections in VR space. In addition, simulators have been released for training purposes when learning the procedures for vaccination against the new coronavirus, and are expected to be more effective than learning through classroom lectures.

    Mediverse OCD (virtual space such as seminars)

    MediVerse OCD aims to create new services by building a platform that connects medical professionals or medical professionals and companies in a metaverse space.
    It can be used for a variety of purposes depending on the user, including sharing case information, gathering research knowledge from medical professionals, and holding simple study sessions and study groups.

    Holoeyes (VR software for medical education)

    Holoeyes is VR software for learning medical education or clinical practice.
    Since the purpose is medical education, 3D information of the human body can be accurately reproduced in VR, making it possible to learn visually. Furthermore, it can also be used to simulate actual procedures. You can also communicate with other users in remote locations in real time via avatars.

    Introducing 5 papers that will help you understand future metaverse medicine

    In this chapter, we will introduce five papers that will be useful in deepening our knowledge of metaverse medicine, which will be promoted in the future.

    Papers on cardiology

    The use of the metaverse in cardiology is called the CardioVerse.
    In cardiology, by utilizing this Cardioverse, it is possible to receive specialized services in 3D space even in remote locations, and the quality of medical care can be improved by integrating surgical robots and navigation systems. It has been reported that the effect of “increases the

    Papers on ophthalmology

    In ophthalmology, there are many things related to AR and VR.
    For example, “Using AR and VR to help patients notice visual abnormalities” and “Making it easier to train medical professionals in optometry and surgery.”
    The use of AR and VR can help patients in remote locations decide whether to travel to the hospital for testing. Other training simulators introduced include the EyeSi simulator (VR Magic) and MicroVisTouch (Immersive Touch).

    Papers on medical education

    In the field of medical education, we classify the metaverse into four types and discuss how to utilize each type. The four types of medical education are ” Augmented Reality ,” “Lifelog,” “Mirror World,” and “Virtual Reality.”
    Each type is discussed in the paper as follows.

      • Augmented reality uses augmented reality t-shirts to explore inside the human body
      • Lifelog is introducing a service that can utilize accumulated biometric information in the medical field.
    • Mirror World is developing a platform that contributes to scientific research through games.
    • In virtual reality, use of avatar services and platforms

    Papers on gastroenterology

    In gastroenterology, papers have been published on the clinical results of endoscopy training actually conducted in the metaverse space.
    Specifically, this includes surgical training using endoscopic equipment in the Metaverse, treatment by senior doctors in the Metaverse, and discussion of surgical procedures by experts in the Metaverse via avatars.
    Training and discussions held in the Metaverse space have led to results such as improved experience for doctors, shorter surgical times, and lower complication rates.

    Papers on body transformation research cases

    Body transformation is a phenomenon in which emotions, cognition, and behavior change by transforming from one’s own body to another body in VR space. Research on this body transformation is underway in Japan.
    Specifically, the following cases have been reported.

      • When white people use black avatars, their sense of discrimination is reduced.
      • Using a child avatar leads to overestimation of object size and childish behavior.
    • Being a superhero and helping others makes you more likely to act altruistically.

    In other words, using an avatar with a different personality in VR space will have an impact on your body and way of thinking in the real world.

    summary

     

    It is no exaggeration to say that the market for the use of the metaverse in medical care is growing, and there is no doubt that it will continue to develop. With the use of 5G, the impact of the Metaverse on medical care will increase. The range of applications will continue to expand in a variety of medical fields, including telemedicine and real-time communication between doctors and patients.

     

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  • What is an IoT system? Explaining the basic configuration, development flow, examples, etc.

    What is an IoT system? Explaining the basic configuration, development flow, examples, etc.

    “I hear that IoT is popular, but I don’t know how it works.” “I don’t know how to proceed with IoT system development.”

    If you are reading this article, you may have questions like the ones above. In this article, we will explain the configuration of the IoT system, what it can do, the development flow, and usage examples. We also introduce the “IoT System Technology Certification”, which is a test for those who want to learn about IoT systems, so please refer to it.

     

     

    IoT and IoT systems

    IoT and IoT systems

    IoT (Internet of Things) is a technology in which devices and sensors communicate via the Internet to collect and share data. Smart home appliances that have been released for home use in recent years are also products that use IoT technology.

    As the name suggests, an IoT system is a system that uses IoT technology. It is used not only in factories but also in vending machines and home appliances, and it is expected that collecting and analyzing data will make life more comfortable and make work more efficient.

     

    IoT system components

    The IoT system consists of the following elements:

    • device
    • network
    • application
    • storage

    Device

    Devices mainly refer to hardware such as actuators, temperature sensors, humidity sensors, cameras, and smart meters. The device’s role is to collect the following data.

    • temperature
    • humidity
    • Whether there are humans or pets
    • Power usage status

    It is also possible to operate on “things”. For example, perform the following actions:

    • make a sound
    • Operate the remote control
    • opening/closing the door
    • Discharging food given to pets

    Devices in IoT also have the role of connecting to the Internet and sending and receiving data.

    Network

    IoT systems require networks to send and receive data between devices and between devices and applications. The following are network technologies used in IoT systems.

    • Wifi
    • Bluetooth
    • 3G, 4G, 5G
    • M2M communication (device-to-device communication)

    In order for a device to process the information it collects, it must send and receive data. The wireless and mobile networks mentioned above are the constituent elements of the data transmission and reception path in the IoT system.

    Application

    In IoT systems, applications are used to process and analyze data. It may be easier to understand if you think of them as being in charge of utilizing the collected data.

    The application also has the role of issuing instructions to the device based on the analyzed information. In the case of an air conditioner IoT system, energy savings can be achieved by issuing an instruction such as “According to the data collected by the sensor, there is no one around at the moment, so let’s turn off the power.”

    Storage

    Storage is used as a place to store collected data. In many cases, cloud storage will be used. In IoT systems, the amount of data collected by devices increases as the usage time increases.

    With more data, applications are likely to perform more effective analysis. However, there is no numerical value such as “the optimal amount of data is XX GB”, nor does it mean that more data is better. Therefore, data needs to be collected and stored for a while. By using storage with a large upper limit that can be collected, it is possible to accumulate and analyze the necessary data.

    Background to the spread of IoT systems

    It can be said that IoT systems have spread rapidly in recent years.

    • Network acceleration
    • Popularization of smaller devices
    • Reducing manufacturing costs through the development of advanced technology

    Network acceleration

    One of the important factors behind the spread of IoT systems is the increase in network speed. Recently, high-speed and stable Internet connections have become widespread, and mobile communication technology continues to develop. These advances in network technology have enabled large numbers of devices to send and receive data simultaneously. By using a high-speed network, we are able to exchange information in real time with little communication delay.

    As mentioned earlier, networks are essential for IoT systems. As network technology has evolved and data transmission and reception within IoT systems has become faster, convenience has improved. As a result, it is thought that the number of people who want to use IoT systems continues to increase.

    Popularization of smaller devices

    One of the reasons behind the spread of IoT systems is the spread of small and power-saving devices. Among the components of IoT systems, this is the development of devices.

    Advances in industrial technology and science have made it possible to create small yet high-performance devices such as sensors and controllers. Small devices are more energy efficient than large devices, and have the advantage of being easier to use in household products. Advances in device miniaturization have made it possible to incorporate devices into a variety of products.

    Even small devices can collect data in real time. It can be said that the progress made in achieving smaller size and higher performance of IoT devices is directly linked to the spread of IoT systems.

    Manufacturing cost reduction through the development of advanced technology

    The reason behind the spread of IoT systems is the decline in manufacturing costs due to the development of advanced technology. This is also a story about devices among the components of an IoT system. IoT devices have also become cheaper to manufacture due to advances in semiconductor technology, microprocessors, energy efficient device designs, and many other related technologies.

    Furthermore, as an advanced technology, the application aspects of the components of IoT systems are also evolving. Advances in cloud computing and big data technology have also reduced the cost of data collection and processing.

    The above two technologies have made it possible to manufacture high-performance IoT devices at low cost, and the convenience of the IoT system as a whole has improved, which is why IoT systems are becoming more popular.

    What you can do with IoT systems

    What you can do with IoT systems

    The following are examples of what can be done with IoT systems.

    • Operations on things
    • Monitoring the status and operation of things
    • Communication between things

    Operations on things

    IoT systems allow users (humans) to operate things remotely.

    For example, in a smart house, you can use your smartphone or tablet to adjust the temperature of your home’s air conditioner, turn on/off lights, and feed your pet. Smart cars also use mobile apps to unlock the car’s doors and start the engine.

    By using IoT systems, it is possible to operate things via a network even when the user is not present.

    Monitoring the status and operation of things

    IoT systems can monitor the status and operation of things in real time. Devices such as sensors and actuators are embedded in products to collect and send data to applications and storage.

    For example, a refrigerator with a built-in temperature sensor monitors the temperature inside the refrigerator and notifies the user if the temperature is abnormal. This not only allows users to notice abnormalities inside the refrigerator, but also helps prevent food from deteriorating if noticed early.

    Let’s also consider the example of a smart meter installed as an electricity meter. Smart meters not only monitor electricity usage and notify the utility company, but also provide recommendations to users on how to improve their electricity usage based on the collected data and analysis results.

    By monitoring the status and operation of things through IoT systems, it is possible to improve efficiency and safety, and discover problems early.

    Communication between things

    IoT systems allow things (products) to communicate with each other.

    For example, in a smart home, sensors and smartphones communicate to share data and perform operations. In addition, in factories, manufacturing lines that use IoT systems allow machines to share data with each other. As a result, it will be possible to understand the details and progress of the preceding and succeeding processes, which will help improve operational efficiency and detect abnormalities.

    The use of IoT systems allows things to communicate with each other, making life and work more efficient and convenient.

     

    IoT system development flow

    IoT system development flow

    The general waterfall development flow for IoT systems is as follows.

    • Requirements definition and design
    • implementation
    • quality test
    • Release and maintenance operations

    Requirements definition and design

    Define requirements at the beginning of IoT system development. This is the process of clarifying the purpose and functionality of the system, working backwards from what users want, and understanding requirements and business needs.

    Requirements definition clarifies what devices and sensors are needed, how data will be acquired and processed, network connectivity, security requirements, etc. In addition, the necessary budget, number of development team members, period, etc. are determined at the requirements definition stage.

    Design is done after requirements definition. We provide technical details for the content documented in the requirements definition. Specific details include device architecture, data flow (network), user interface, etc.

    Implementation

    Once requirements definition and design are complete, implementation begins. We will actually create a pre-designed system as a program. The specific content to be implemented in an IoT system includes configuring devices and sensors, building network connections, and processing data. If you have any content that has been designed, such as cooperation with applications or cloud services, please implement it for those as well.

    During implementation, we select an appropriate programming language and framework in advance, and optimize the programming itself from the standpoint of safety and performance.

    Quality test

    After the implementation is complete, we will conduct a quality test. Specific examples of quality tests include functional tests, performance tests, unit tests, and coordination tests.

    Functional test: Check whether it can operate as required
    Performance test: Evaluate whether it can withstand a certain load, communication speed, processing speed, etc.
    Unit test: Check whether one function operates as expected
    Linkage testing: Not only does it meet the specifications, but it also confirms that there are no abnormalities in areas where there were no problems in the unit test when multiple functions are combined.

    If we determine that the quality does not meet the standards through quality testing, we will provide feedback to the design and implementation.

    Release and maintenance operations

    After passing the quality test, release your IoT system. At release, we will install and configure the system and create documentation for users.

    After release, system maintenance and operation will begin. The goals of maintenance operations are to maintain system stability and performance, and improve user satisfaction. Specifically, we carry out periodic bug fixes and troubleshooting for the entire system.

    It is also necessary to improve customer support. You may end up developing new features based on actual customer feedback.

     

    Examples of IoT system introduction

    Examples of IoT system introduction

    We will introduce three examples of introducing IoT systems.

    • Apartment that looks after seniors
    • You can check the status of the trash can

     

    Apartment that looks after seniors

    Families who live far away from an elderly person often find it burdensome to visit them regularly to check on their condition. This condominium collects data on usage frequency and status by having customers use home appliances that incorporate an IoT system.

    If an abnormality is detected in the data, a notification will be sent to the condominium management office, which will help prevent any accidents. By using the IoT system, we are contributing not only to families in need of nursing care, but also to the nursing care industry.

     

    You can check the status of the trash can

    Nippon Systemware has developed “BigBelly Solar,” a trash can that incorporates IoT. The collection status of garbage accumulated in the trash can is visualized, making garbage collection more efficient. It also has an automatic garbage compaction function, so you can check the accumulation status and have the advantage of preventing garbage from overflowing from the trash can.

    By visualizing the collection status of each trash can, you will be able to see which trash cans are used more frequently and which ones are less frequently used, and you will be able to more efficiently consider areas where you should increase or decrease the number of trash cans.
    This is an example that shows how using IoT devices can help solve familiar problems.

    Sunstar has developed G・U・M PLAY, a device that works with a smartphone to record tooth brushing status. By incorporating a sensor into your toothbrush, you can score your tooth brushing based on the movement of your toothbrush and the time you brush.

    Users can use a smartphone app to view the data collected by the toothbrush and understand how well they are brushing their teeth. The app analyzes your tooth brushing data and gives you advice on how to best brush your teeth.

    This is an example of using an IoT system to visualize something that you cannot understand even if you do it yourself.

    Introduction to IoT system certification

    Introduction to IoT system certification

    From here, we will explain the following two contents about the “IoT System Technology Certification Exam”, an exam that can check the development and knowledge of IoT systems.

    • Overview of IoT System Certification and Benefits of Taking the Test
    • Skills and knowledge gained through IoT system certification

     

    Overview of IoT System Certification and Benefits of Taking the Test

    The exam has three levels: advanced, intermediate, and basic. Only the advanced level requires exam qualification, and you must have passed the intermediate level or taken a certification program. The exam method is basic, with the intermediate level being a CBT exam (multiple choice questions), and the advanced level being an essay exam based on the course content and course content.

    If you are a beginner in IoT systems or are taking the test for the first time, it is recommended that you take the test from the basic level. The purpose of the IoT System Certification is to develop IoT engineers, and each exam requires a certain level of knowledge.

    The benefit of passing the test is that you will have more opportunities to work in businesses related to IoT system development (including sales, etc.). Having a qualification will show off that you have knowledge, which can also be used when looking for a job.

     

    Skills and knowledge gained through IoT system certification

    The skills gained through the IoT System Certification are the knowledge that is appropriate to the level of each exam. For example, at the basic level, the content would be as follows.

    • IoT system configuration and construction technology
    • Sensor/actuator technology and communication method
    • IoT data utilization technology (AI)
    • IoT information security measures technology
    • IoT system prototyping technology

    There are some parts that overlap with the content explained in this article. The exam will ask you about more detailed information, so it is a good idea to prepare for the exam using a question set.

    summary

    summary

    We provided an overview of the IoT system, its components, the background of its spread, what it can do, the development flow, implementation examples, and IoT system certification.

    The number of household appliances that use IoT systems is increasing every day, and this trend is expected to continue in the future. In order to coexist with IoT systems in your life, it is important to understand the basics explained in this article.

     

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  • What is an IoT system? Explaining the basic configuration, development flow, examples, etc.

    What is an IoT system? Explaining the basic configuration, development flow, examples, etc.

    “I hear that IoT is popular, but I don’t know how it works.” “I don’t know how to proceed with IoT system development.”

    If you are reading this article, you may have questions like the ones above. In this article, we will explain the configuration of the IoT system, what it can do, the development flow, and usage examples. We also introduce the “IoT System Technology Certification”, which is a test for those who want to learn about IoT systems, so please refer to it.

     

     

    IoT and IoT systems

    IoT and IoT systems

    IoT (Internet of Things) is a technology in which devices and sensors communicate via the Internet to collect and share data. Smart home appliances that have been released for home use in recent years are also products that use IoT technology.

    As the name suggests, an IoT system is a system that uses IoT technology. It is used not only in factories but also in vending machines and home appliances, and it is expected that collecting and analyzing data will make life more comfortable and make work more efficient.

     

    IoT system components

    The IoT system consists of the following elements:

    • device
    • network
    • application
    • storage

    Device

    Devices mainly refer to hardware such as actuators, temperature sensors, humidity sensors, cameras, and smart meters. The device’s role is to collect the following data.

    • temperature
    • humidity
    • Whether there are humans or pets
    • Power usage status

    It is also possible to operate on “things”. For example, perform the following actions:

    • make a sound
    • Operate the remote control
    • opening/closing the door
    • Discharging food given to pets

    Devices in IoT also have the role of connecting to the Internet and sending and receiving data.

    Network

    IoT systems require networks to send and receive data between devices and between devices and applications. The following are network technologies used in IoT systems.

    • Wifi
    • Bluetooth
    • 3G, 4G, 5G
    • M2M communication (device-to-device communication)

    In order for a device to process the information it collects, it must send and receive data. The wireless and mobile networks mentioned above are the constituent elements of the data transmission and reception path in the IoT system.

    Application

    In IoT systems, applications are used to process and analyze data. It may be easier to understand if you think of them as being in charge of utilizing the collected data.

    The application also has the role of issuing instructions to the device based on the analyzed information. In the case of an air conditioner IoT system, energy savings can be achieved by issuing an instruction such as “According to the data collected by the sensor, there is no one around at the moment, so let’s turn off the power.”

    Storage

    Storage is used as a place to store collected data. In many cases, cloud storage will be used. In IoT systems, the amount of data collected by devices increases as the usage time increases.

    With more data, applications are likely to perform more effective analysis. However, there is no numerical value such as “the optimal amount of data is XX GB”, nor does it mean that more data is better. Therefore, data needs to be collected and stored for a while. By using storage with a large upper limit that can be collected, it is possible to accumulate and analyze the necessary data.

    Background to the spread of IoT systems

    It can be said that IoT systems have spread rapidly in recent years.

    • Network acceleration
    • Popularization of smaller devices
    • Reducing manufacturing costs through the development of advanced technology

    Network acceleration

    One of the important factors behind the spread of IoT systems is the increase in network speed. Recently, high-speed and stable Internet connections have become widespread, and mobile communication technology continues to develop. These advances in network technology have enabled large numbers of devices to send and receive data simultaneously. By using a high-speed network, we are able to exchange information in real time with little communication delay.

    As mentioned earlier, networks are essential for IoT systems. As network technology has evolved and data transmission and reception within IoT systems has become faster, convenience has improved. As a result, it is thought that the number of people who want to use IoT systems continues to increase.

    Popularization of smaller devices

    One of the reasons behind the spread of IoT systems is the spread of small and power-saving devices. Among the components of IoT systems, this is the development of devices.

    Advances in industrial technology and science have made it possible to create small yet high-performance devices such as sensors and controllers. Small devices are more energy efficient than large devices, and have the advantage of being easier to use in household products. Advances in device miniaturization have made it possible to incorporate devices into a variety of products.

    Even small devices can collect data in real time. It can be said that the progress made in achieving smaller size and higher performance of IoT devices is directly linked to the spread of IoT systems.

    Manufacturing cost reduction through the development of advanced technology

    The reason behind the spread of IoT systems is the decline in manufacturing costs due to the development of advanced technology. This is also a story about devices among the components of an IoT system. IoT devices have also become cheaper to manufacture due to advances in semiconductor technology, microprocessors, energy efficient device designs, and many other related technologies.

    Furthermore, as an advanced technology, the application aspects of the components of IoT systems are also evolving. Advances in cloud computing and big data technology have also reduced the cost of data collection and processing.

    The above two technologies have made it possible to manufacture high-performance IoT devices at low cost, and the convenience of the IoT system as a whole has improved, which is why IoT systems are becoming more popular.

    What you can do with IoT systems

    What you can do with IoT systems

    The following are examples of what can be done with IoT systems.

    • Operations on things
    • Monitoring the status and operation of things
    • Communication between things

    Operations on things

    IoT systems allow users (humans) to operate things remotely.

    For example, in a smart house, you can use your smartphone or tablet to adjust the temperature of your home’s air conditioner, turn on/off lights, and feed your pet. Smart cars also use mobile apps to unlock the car’s doors and start the engine.

    By using IoT systems, it is possible to operate things via a network even when the user is not present.

    Monitoring the status and operation of things

    IoT systems can monitor the status and operation of things in real time. Devices such as sensors and actuators are embedded in products to collect and send data to applications and storage.

    For example, a refrigerator with a built-in temperature sensor monitors the temperature inside the refrigerator and notifies the user if the temperature is abnormal. This not only allows users to notice abnormalities inside the refrigerator, but also helps prevent food from deteriorating if noticed early.

    Let’s also consider the example of a smart meter installed as an electricity meter. Smart meters not only monitor electricity usage and notify the utility company, but also provide recommendations to users on how to improve their electricity usage based on the collected data and analysis results.

    By monitoring the status and operation of things through IoT systems, it is possible to improve efficiency and safety, and discover problems early.

    Communication between things

    IoT systems allow things (products) to communicate with each other.

    For example, in a smart home, sensors and smartphones communicate to share data and perform operations. In addition, in factories, manufacturing lines that use IoT systems allow machines to share data with each other. As a result, it will be possible to understand the details and progress of the preceding and succeeding processes, which will help improve operational efficiency and detect abnormalities.

    The use of IoT systems allows things to communicate with each other, making life and work more efficient and convenient.

     

    IoT system development flow

    IoT system development flow

    The general waterfall development flow for IoT systems is as follows.

    • Requirements definition and design
    • implementation
    • quality test
    • Release and maintenance operations

    Requirements definition and design

    Define requirements at the beginning of IoT system development. This is the process of clarifying the purpose and functionality of the system, working backwards from what users want, and understanding requirements and business needs.

    Requirements definition clarifies what devices and sensors are needed, how data will be acquired and processed, network connectivity, security requirements, etc. In addition, the necessary budget, number of development team members, period, etc. are determined at the requirements definition stage.

    Design is done after requirements definition. We provide technical details for the content documented in the requirements definition. Specific details include device architecture, data flow (network), user interface, etc.

    Implementation

    Once requirements definition and design are complete, implementation begins. We will actually create a pre-designed system as a program. The specific content to be implemented in an IoT system includes configuring devices and sensors, building network connections, and processing data. If you have any content that has been designed, such as cooperation with applications or cloud services, please implement it for those as well.

    During implementation, we select an appropriate programming language and framework in advance, and optimize the programming itself from the standpoint of safety and performance.

    Quality test

    After the implementation is complete, we will conduct a quality test. Specific examples of quality tests include functional tests, performance tests, unit tests, and coordination tests.

    Functional test: Check whether it can operate as required
    Performance test: Evaluate whether it can withstand a certain load, communication speed, processing speed, etc.
    Unit test: Check whether one function operates as expected
    Linkage testing: Not only does it meet the specifications, but it also confirms that there are no abnormalities in areas where there were no problems in the unit test when multiple functions are combined.

    If we determine that the quality does not meet the standards through quality testing, we will provide feedback to the design and implementation.

    Release and maintenance operations

    After passing the quality test, release your IoT system. At release, we will install and configure the system and create documentation for users.

    After release, system maintenance and operation will begin. The goals of maintenance operations are to maintain system stability and performance, and improve user satisfaction. Specifically, we carry out periodic bug fixes and troubleshooting for the entire system.

    It is also necessary to improve customer support. You may end up developing new features based on actual customer feedback.

     

    Examples of IoT system introduction

    Examples of IoT system introduction

    We will introduce three examples of introducing IoT systems.

    • Apartment that looks after seniors
    • You can check the status of the trash can

     

    Apartment that looks after seniors

    Families who live far away from an elderly person often find it burdensome to visit them regularly to check on their condition. This condominium collects data on usage frequency and status by having customers use home appliances that incorporate an IoT system.

    If an abnormality is detected in the data, a notification will be sent to the condominium management office, which will help prevent any accidents. By using the IoT system, we are contributing not only to families in need of nursing care, but also to the nursing care industry.

     

    You can check the status of the trash can

    Nippon Systemware has developed “BigBelly Solar,” a trash can that incorporates IoT. The collection status of garbage accumulated in the trash can is visualized, making garbage collection more efficient. It also has an automatic garbage compaction function, so you can check the accumulation status and have the advantage of preventing garbage from overflowing from the trash can.

    By visualizing the collection status of each trash can, you will be able to see which trash cans are used more frequently and which ones are less frequently used, and you will be able to more efficiently consider areas where you should increase or decrease the number of trash cans.
    This is an example that shows how using IoT devices can help solve familiar problems.

    Sunstar has developed G・U・M PLAY, a device that works with a smartphone to record tooth brushing status. By incorporating a sensor into your toothbrush, you can score your tooth brushing based on the movement of your toothbrush and the time you brush.

    Users can use a smartphone app to view the data collected by the toothbrush and understand how well they are brushing their teeth. The app analyzes your tooth brushing data and gives you advice on how to best brush your teeth.

    This is an example of using an IoT system to visualize something that you cannot understand even if you do it yourself.

    Introduction to IoT system certification

    Introduction to IoT system certification

    From here, we will explain the following two contents about the “IoT System Technology Certification Exam”, an exam that can check the development and knowledge of IoT systems.

    • Overview of IoT System Certification and Benefits of Taking the Test
    • Skills and knowledge gained through IoT system certification

     

    Overview of IoT System Certification and Benefits of Taking the Test

    The exam has three levels: advanced, intermediate, and basic. Only the advanced level requires exam qualification, and you must have passed the intermediate level or taken a certification program. The exam method is basic, with the intermediate level being a CBT exam (multiple choice questions), and the advanced level being an essay exam based on the course content and course content.

    If you are a beginner in IoT systems or are taking the test for the first time, it is recommended that you take the test from the basic level. The purpose of the IoT System Certification is to develop IoT engineers, and each exam requires a certain level of knowledge.

    The benefit of passing the test is that you will have more opportunities to work in businesses related to IoT system development (including sales, etc.). Having a qualification will show off that you have knowledge, which can also be used when looking for a job.

     

    Skills and knowledge gained through IoT system certification

    The skills gained through the IoT System Certification are the knowledge that is appropriate to the level of each exam. For example, at the basic level, the content would be as follows.

    • IoT system configuration and construction technology
    • Sensor/actuator technology and communication method
    • IoT data utilization technology (AI)
    • IoT information security measures technology
    • IoT system prototyping technology

    There are some parts that overlap with the content explained in this article. The exam will ask you about more detailed information, so it is a good idea to prepare for the exam using a question set.

    summary

    summary

    We provided an overview of the IoT system, its components, the background of its spread, what it can do, the development flow, implementation examples, and IoT system certification.

    The number of household appliances that use IoT systems is increasing every day, and this trend is expected to continue in the future. In order to coexist with IoT systems in your life, it is important to understand the basics explained in this article.

     

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  • What is Cloud AI?

    What is Cloud AI?

    Due to improvements in database technology and network technology, the use of cloud AI, which processes AI in the cloud, is progressing.

    With further technological innovations such as the development of 5G, the role played by cloud AI will become even greater.

    Recently, an increasing number of companies are recognising the importance of data and promoting data utilisation, and the cloud plays a major role in the processing of such data and its processing speed.

    This time, we will explain the advantages and disadvantages of cloud AI and the situations where it can be actually introduced and utilised with examples. And I will also explain the difference from edge AI, which has been a hot topic recently.

     

    What is cloud AI?

    Starting with Gmail, the cloud is something that many people are familiar with. The name cloud originated in 2006 when then-Google CEO Eric Schmidt called the service “cloud computing.”

    In the latter half of the 1990, Internet use spread rapidly as computer prices began to fall in the Internet age. At that time, many companies were overwhelmed with internet speeds, applications and data, and server sprawl. In addition, it was difficult to handle such a server because it was necessary to rent or prepare it yourself, and it was necessary to design the capacity in advance.

    In the 2000, the cloud was the solution to this problem. This technology has made it possible to easily use necessary services with software and data via the Internet.

    Furthermore, the cloud is indispensable for IoT technology, which is a hot topic these days. For example, services are being developed that allow users to operate and manage home appliances from their smartphones while away from home. The amount of data sent by these services is extremely large ( big data ), and cannot be covered by the servers owned by one company. Cloud services are used for this purpose. In addition to being able to expand freely, the cloud eliminates the need for server management and keeps costs low, so it can greatly reduce the burden on companies.

    Looking at the background to the birth of the cloud and its current use, I think you can understand why AI is being used in the cloud.

    Three benefits of cloud AI

    The benefits of using cloud AI are:

    • No load on your server
    • Complex and advanced processing possible
    • Easy management such as application of learned data

    I will explain in detail from now on.

    No load on your server

    Cloud AI is a mechanism that performs AI learning and data processing on the cloud, so there is no need to perform complex information processing on your own server or terminal, which can reduce the load.

    The computer that processes information on the cloud is also provided by the data server, so it can be said that there is no need to prepare a high-performance computer in-house.

    Complex and advanced processing possible

    Cloud AI accumulates data on a large-scale server and processes and analyzes the data, making it possible to perform complex and advanced processing.

    The great strength of cloud AI is that it can process a huge amount of information that cannot be processed by AI installed in small terminals such as personal computers.

    Easy management such as application of learned data

    One of the challenges in introducing AI is the preparation of data for learning.

    When using cloud AI, learning data is prepared in advance on the cloud, and highly accurate AI that has learned from that data can be used.

    Since there is no need to prepare highly reliable data or perform installation work such as building a learning model, AI can be used without advanced knowledge of AI.

    3 Disadvantages of Cloud AI

    Disadvantages of using cloud AI are as follows.

      • When sending and receiving a large amount of data, processing via the Internet lacks real-time performance.
    • Risk of information leakage
    • As the amount of data increases, the amount of communication increases

    I will explain in detail from now on.

    Sending and receiving huge amounts of data lacks real-time performance

    Cloud AI sends and receives information from the terminal at hand to the cloud and processes information on the cloud.

    If you need to send or receive a large amount of data, you may exceed your data bandwidth and experience delays.

    However, in recent years, the use of 5G lines, which are capable of transmitting and receiving more data, is increasing, so it is expected that cloud AI will be able to be used in real time in the future.

    Risk of information leakage

    When using cloud AI, all information must be sent to the cloud via the Internet, increasing the risk of information leakage.

    There is also a risk of information leakage while information is stored on the cloud.

    It can be said that confidential information such as internal secrets is not suitable for processing with cloud AI.

    As the amount of data increases, the amount of communication increases

    Internet connection is used to send data from the terminal to the cloud.

    As the amount of data increases, the amount of communication increases, which not only increases the risk of delays but also increases communication costs.

    There are costs that can be cut by using cloud AI, such as not having to manage AI in-house, so it is necessary to consider what kind of service to use in consideration of other costs.

    Differences between cloud AI and edge AI

    In recent years, there is a technology called “edge AI”.

    As the use of IoT is promoted and AI technology advances, the lack of real-time performance and privacy issues, which I mentioned earlier as the disadvantages of cloud AI, have come to the fore.

    That’s where edge AI comes in. In cloud AI, data accumulation, learning, and inference were all performed on the cloud, but in edge AI, inference can be performed without using the cloud by incorporating a learning model into a terminal (edge ​​device).

    Edge AI has the following features.

    • Real-time performance can be secured because the prediction is performed on the end device (edge ​​device)
    • Security is strong because prediction is performed without going through the Internet
    •  Communication cost savings

    Edge AI is attracting a great deal of attention, especially in research on industrial machinery and self-driving cars, as a technology that can successfully compensate for the disadvantages of cloud AI.

    Disadvantages of edge AI and recent technological developments

    However, Edge AI also has its disadvantages.

      • Incompetence of edge devices (compactness, power consumption in inference, etc.)
      • Advanced prediction is not possible because the environment for learning (cloud side) and the environment for inference (edge ​​device side) are (sometimes) different.
      • Security is not perfect as data is (may be) sent to the cloud to generate learning models

    Due to the above problems, edge AI alone has been considered insufficient.

    Therefore, in recent years, edge AI that combines high efficiency, high-speed processing, and small size has been developed, and there are great expectations for it as the IoT society advances.

    Led by the New Energy and Industrial Technology Development Organization (NEDO), private companies and universities are developing computer technology and AI chips that can achieve both high speed and ultra-low power consumption.

    Furthermore, edge AI startup AISing Inc. announced last December that it had developed an ultra-compact edge AI algorithm called “Memory Saving Tree (MST)” that can be placed on your fingertips.

     

    This memory-saving “MST” is expected to be introduced in a wide variety of fields such as home appliances, smartwatches, and automobiles.

    In this way, the development of edge AI is currently being actively carried out, and it is attracting attention as an important technology that supports the Quaternary industry.

    3 AI cloud services

    Here are three AI cloud services.

      1. Google Cloud Platform
    1. Amazon Web Services
    2. Microsoft Azure

    ①Google Cloud Platform

    Google Cloud Platform is an AI cloud service provided by Google, and it is a cloud service that implements various functions such as more than 20 free functions, paid functions, and business functions.

    It is an easy-to-use service for those who are thinking of using the AI ​​cloud service for the first time, such as new users can receive usage rights for $ 300.

    It provides a function to manage large amounts of data and a function to cluster images.

    (2) Amazon Web Services

    Amazon Web Services is an AI cloud service provided by Amazon, and is attractive for its diverse functions and rich free experience.

    There are three types of free functions: free for a short period of time, free for one year, and unlimited, and there are over 100 services that you can try for free.

    It is attractive that various services such as machine learning, log analysis, and relational database services can be used free of charge.

    We also have extensive support for start-up companies.

    ③Microsoft Azure

    Microsoft Azure is an AI cloud service provided by Microsoft, and you can experience popular services for free for 12 months.

    After the free trial period ends, we will move to a pay-as-you-go system, but you can continue to use more than 40 services for free.

    AI analyzes customer trends to build mobile experiences, supports the development of new apps, and efficiently manages websites.

    3 Case Studies of Cloud AI Introduction

    (1) Utilization of “XaaS (X as a service)”

    Businesses using cloud AI are currently seen in various places, and among them, the need for cloud computing services such as ” SaaS “, “MaaS”, and ” PaaS ” has been increasing in recent years.

    These various services are collectively called “XaaS”, and services are developed and operated by a wide range of companies, from large companies such as Google and Microsoft to venture companies.

    This time, we will introduce the latest examples of SaaS (Software as a Service), which is attracting particular attention among XaaS.

    ②Material informatics “TABRASA”

    The platform “TABRASA” is a materials informatics (MI) service ( SaaS ) jointly developed by NAGASE and IBM .

    *MI (Materials Informatics): A technology that uses AI to improve the efficiency of research and development by chemists. A search algorithm that uses past material experiments and simulation data makes it possible to develop and commercialize new materials more quickly.

    The feature of this service is that you can search for materials with two engines, “cognitive” and “analytics”.

    “Analytics” is an approach to learn the chemical structural formula and physical property values ​​and derive the chemical structural formula of the substance desired by the user. On the other hand, “cognitive” is an approach that reads papers, patents, encyclopedias, experimental data, etc. related to materials into AI, systematizes them, and makes new guesses and proposals.

    “Cognitive” is a highly customizable and unprecedented approach. The easy-to-understand and easy-to-operate UI makes it possible to use the service even if you are not a research specialist, so it can be applied to a wide range of fields.

    ③ Transcription service “Mojiko”

    “Mojiko” is a “transcription editor” service that uses AI speech recognition technology developed by TBS TV.

    In the TV and radio industry, a lot of “transcription” is done every day, but because it is a very laborious task, it is a heavy burden on the site of program production. In order to reduce the burden of such work as much as possible, TBS TV started developing “Mojiko”.

    As a result, after materials such as audio and video files collected are automatically converted into text by the latest AI speech recognition engines of IT companies, human beings can immediately “correct and edit” sentences that are not correctly recognized. became.

    Currently, TBS licenses “Mojiko” to Yoshizumi Information Co., Ltd. and sells it to general companies. Mojiko is expected to play a role in reforming the way people work in the media industry, where working hours are said to be long.

    Even in industries such as the media industry, where IT and DX are slow to progress, starting with services that are easy to implement will make it easier for XaaS to take root, and it may gradually bring about positive changes in the way work is done.

    In conclusion

    In this article, we have explained a wide range of things, from the background of the creation of the cloud to the technological development and introduction of cloud AI. Did you understand that the existence of this cloud is indispensable for the utilization and development of AI?

    If you follow popular XaaS services, you can see the current movement of the IT society. With the advent of the AI ​​era and the progress of the IoT society, there is a great possibility that cloud and cloud services will further advance in technological development and diversification of services. We will continue to pay attention to cloud technology, which will be the “unsung hero” that will create a new society.

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  • What is Cloud AI?

    What is Cloud AI?

    Due to improvements in database technology and network technology, the use of cloud AI, which processes AI in the cloud, is progressing.

    With further technological innovations such as the development of 5G, the role played by cloud AI will become even greater.

    Recently, an increasing number of companies are recognising the importance of data and promoting data utilisation, and the cloud plays a major role in the processing of such data and its processing speed.

    This time, we will explain the advantages and disadvantages of cloud AI and the situations where it can be actually introduced and utilised with examples. And I will also explain the difference from edge AI, which has been a hot topic recently.

     

    What is cloud AI?

    Starting with Gmail, the cloud is something that many people are familiar with. The name cloud originated in 2006 when then-Google CEO Eric Schmidt called the service “cloud computing.”

    In the latter half of the 1990, Internet use spread rapidly as computer prices began to fall in the Internet age. At that time, many companies were overwhelmed with internet speeds, applications and data, and server sprawl. In addition, it was difficult to handle such a server because it was necessary to rent or prepare it yourself, and it was necessary to design the capacity in advance.

    In the 2000, the cloud was the solution to this problem. This technology has made it possible to easily use necessary services with software and data via the Internet.

    Furthermore, the cloud is indispensable for IoT technology, which is a hot topic these days. For example, services are being developed that allow users to operate and manage home appliances from their smartphones while away from home. The amount of data sent by these services is extremely large ( big data ), and cannot be covered by the servers owned by one company. Cloud services are used for this purpose. In addition to being able to expand freely, the cloud eliminates the need for server management and keeps costs low, so it can greatly reduce the burden on companies.

    Looking at the background to the birth of the cloud and its current use, I think you can understand why AI is being used in the cloud.

    Three benefits of cloud AI

    The benefits of using cloud AI are:

    • No load on your server
    • Complex and advanced processing possible
    • Easy management such as application of learned data

    I will explain in detail from now on.

    No load on your server

    Cloud AI is a mechanism that performs AI learning and data processing on the cloud, so there is no need to perform complex information processing on your own server or terminal, which can reduce the load.

    The computer that processes information on the cloud is also provided by the data server, so it can be said that there is no need to prepare a high-performance computer in-house.

    Complex and advanced processing possible

    Cloud AI accumulates data on a large-scale server and processes and analyzes the data, making it possible to perform complex and advanced processing.

    The great strength of cloud AI is that it can process a huge amount of information that cannot be processed by AI installed in small terminals such as personal computers.

    Easy management such as application of learned data

    One of the challenges in introducing AI is the preparation of data for learning.

    When using cloud AI, learning data is prepared in advance on the cloud, and highly accurate AI that has learned from that data can be used.

    Since there is no need to prepare highly reliable data or perform installation work such as building a learning model, AI can be used without advanced knowledge of AI.

    3 Disadvantages of Cloud AI

    Disadvantages of using cloud AI are as follows.

      • When sending and receiving a large amount of data, processing via the Internet lacks real-time performance.
    • Risk of information leakage
    • As the amount of data increases, the amount of communication increases

    I will explain in detail from now on.

    Sending and receiving huge amounts of data lacks real-time performance

    Cloud AI sends and receives information from the terminal at hand to the cloud and processes information on the cloud.

    If you need to send or receive a large amount of data, you may exceed your data bandwidth and experience delays.

    However, in recent years, the use of 5G lines, which are capable of transmitting and receiving more data, is increasing, so it is expected that cloud AI will be able to be used in real time in the future.

    Risk of information leakage

    When using cloud AI, all information must be sent to the cloud via the Internet, increasing the risk of information leakage.

    There is also a risk of information leakage while information is stored on the cloud.

    It can be said that confidential information such as internal secrets is not suitable for processing with cloud AI.

    As the amount of data increases, the amount of communication increases

    Internet connection is used to send data from the terminal to the cloud.

    As the amount of data increases, the amount of communication increases, which not only increases the risk of delays but also increases communication costs.

    There are costs that can be cut by using cloud AI, such as not having to manage AI in-house, so it is necessary to consider what kind of service to use in consideration of other costs.

    Differences between cloud AI and edge AI

    In recent years, there is a technology called “edge AI”.

    As the use of IoT is promoted and AI technology advances, the lack of real-time performance and privacy issues, which I mentioned earlier as the disadvantages of cloud AI, have come to the fore.

    That’s where edge AI comes in. In cloud AI, data accumulation, learning, and inference were all performed on the cloud, but in edge AI, inference can be performed without using the cloud by incorporating a learning model into a terminal (edge ​​device).

    Edge AI has the following features.

    • Real-time performance can be secured because the prediction is performed on the end device (edge ​​device)
    • Security is strong because prediction is performed without going through the Internet
    •  Communication cost savings

    Edge AI is attracting a great deal of attention, especially in research on industrial machinery and self-driving cars, as a technology that can successfully compensate for the disadvantages of cloud AI.

    Disadvantages of edge AI and recent technological developments

    However, Edge AI also has its disadvantages.

      • Incompetence of edge devices (compactness, power consumption in inference, etc.)
      • Advanced prediction is not possible because the environment for learning (cloud side) and the environment for inference (edge ​​device side) are (sometimes) different.
      • Security is not perfect as data is (may be) sent to the cloud to generate learning models

    Due to the above problems, edge AI alone has been considered insufficient.

    Therefore, in recent years, edge AI that combines high efficiency, high-speed processing, and small size has been developed, and there are great expectations for it as the IoT society advances.

    Led by the New Energy and Industrial Technology Development Organization (NEDO), private companies and universities are developing computer technology and AI chips that can achieve both high speed and ultra-low power consumption.

    Furthermore, edge AI startup AISing Inc. announced last December that it had developed an ultra-compact edge AI algorithm called “Memory Saving Tree (MST)” that can be placed on your fingertips.

     

    This memory-saving “MST” is expected to be introduced in a wide variety of fields such as home appliances, smartwatches, and automobiles.

    In this way, the development of edge AI is currently being actively carried out, and it is attracting attention as an important technology that supports the Quaternary industry.

    3 AI cloud services

    Here are three AI cloud services.

      1. Google Cloud Platform
    1. Amazon Web Services
    2. Microsoft Azure

    ①Google Cloud Platform

    Google Cloud Platform is an AI cloud service provided by Google, and it is a cloud service that implements various functions such as more than 20 free functions, paid functions, and business functions.

    It is an easy-to-use service for those who are thinking of using the AI ​​cloud service for the first time, such as new users can receive usage rights for $ 300.

    It provides a function to manage large amounts of data and a function to cluster images.

    (2) Amazon Web Services

    Amazon Web Services is an AI cloud service provided by Amazon, and is attractive for its diverse functions and rich free experience.

    There are three types of free functions: free for a short period of time, free for one year, and unlimited, and there are over 100 services that you can try for free.

    It is attractive that various services such as machine learning, log analysis, and relational database services can be used free of charge.

    We also have extensive support for start-up companies.

    ③Microsoft Azure

    Microsoft Azure is an AI cloud service provided by Microsoft, and you can experience popular services for free for 12 months.

    After the free trial period ends, we will move to a pay-as-you-go system, but you can continue to use more than 40 services for free.

    AI analyzes customer trends to build mobile experiences, supports the development of new apps, and efficiently manages websites.

    3 Case Studies of Cloud AI Introduction

    (1) Utilization of “XaaS (X as a service)”

    Businesses using cloud AI are currently seen in various places, and among them, the need for cloud computing services such as ” SaaS “, “MaaS”, and ” PaaS ” has been increasing in recent years.

    These various services are collectively called “XaaS”, and services are developed and operated by a wide range of companies, from large companies such as Google and Microsoft to venture companies.

    This time, we will introduce the latest examples of SaaS (Software as a Service), which is attracting particular attention among XaaS.

    ②Material informatics “TABRASA”

    The platform “TABRASA” is a materials informatics (MI) service ( SaaS ) jointly developed by NAGASE and IBM .

    *MI (Materials Informatics): A technology that uses AI to improve the efficiency of research and development by chemists. A search algorithm that uses past material experiments and simulation data makes it possible to develop and commercialize new materials more quickly.

    The feature of this service is that you can search for materials with two engines, “cognitive” and “analytics”.

    “Analytics” is an approach to learn the chemical structural formula and physical property values ​​and derive the chemical structural formula of the substance desired by the user. On the other hand, “cognitive” is an approach that reads papers, patents, encyclopedias, experimental data, etc. related to materials into AI, systematizes them, and makes new guesses and proposals.

    “Cognitive” is a highly customizable and unprecedented approach. The easy-to-understand and easy-to-operate UI makes it possible to use the service even if you are not a research specialist, so it can be applied to a wide range of fields.

    ③ Transcription service “Mojiko”

    “Mojiko” is a “transcription editor” service that uses AI speech recognition technology developed by TBS TV.

    In the TV and radio industry, a lot of “transcription” is done every day, but because it is a very laborious task, it is a heavy burden on the site of program production. In order to reduce the burden of such work as much as possible, TBS TV started developing “Mojiko”.

    As a result, after materials such as audio and video files collected are automatically converted into text by the latest AI speech recognition engines of IT companies, human beings can immediately “correct and edit” sentences that are not correctly recognized. became.

    Currently, TBS licenses “Mojiko” to Yoshizumi Information Co., Ltd. and sells it to general companies. Mojiko is expected to play a role in reforming the way people work in the media industry, where working hours are said to be long.

    Even in industries such as the media industry, where IT and DX are slow to progress, starting with services that are easy to implement will make it easier for XaaS to take root, and it may gradually bring about positive changes in the way work is done.

    In conclusion

    In this article, we have explained a wide range of things, from the background of the creation of the cloud to the technological development and introduction of cloud AI. Did you understand that the existence of this cloud is indispensable for the utilization and development of AI?

    If you follow popular XaaS services, you can see the current movement of the IT society. With the advent of the AI ​​era and the progress of the IoT society, there is a great possibility that cloud and cloud services will further advance in technological development and diversification of services. We will continue to pay attention to cloud technology, which will be the “unsung hero” that will create a new society.

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

    What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

     

    Recent technological improvements have made it possible to handle image analysis to a high level.
    For example, human face authentication is at a level that is also used for immigration at airports.
    In the first place, what kind of technology can image analysis be? And we will introduce examples of how it is used in multiple industries.
    We will explain all the advantages and disadvantages that you should know when considering the introduction.

     

    What is image analysis?

    Image analysis is a system in which a computer makes various decisions after an image is acquired using a sensor such as a camera. It also uses the recognition technology installed in the computer to understand the contents of the image, enabling information extraction and data conversion. The image is to recognize the characteristics of an object in image data such as photographs and videos, and to “mechanically determine what kind of object it is.”

    For example, when a human sees an apple, he or she comprehensively looks at the shape, color, size, etc., and determines that this is an apple. The reason why we can make that distinction is that there are many judgment factors such as knowledge, perception, and memory that human beings have accumulated so far. Taking apples as an example, the following points will help you make a decision.

    • “It has a shape close to a sphere”
    • “The color is red or blue (green)”
    • “It’s about the size of a baseball ball or a shot put the ball.”
    • “There are dents at the top and bottom”
    • “It has a calyx”

    Image analysis performs this kind of information processing that humans naturally do on a computer. The computer that captured the image as data extracts the characteristics of the object and determines “what is reflected in the image?”. Technologies that are used in our daily lives include fingerprint recognition systems for smartphones and face recognition systems installed in digital cameras. Some digital cameras even have a smile detection function. With similar technology, “video analysis” that analyzes video is also advancing. Both refer to processing and analyzing images and videos with a computer to retrieve useful information.

     

    Why image analysis is needed

    There is a limit to the amount that can be visually diagnosed by the human eye, and it is impossible to analyze a large amount of image data instantly. On the other hand, continuing a lot of work is a specialty of computers. For image analysis, all you have to do is apply the captured image or video to the AI ​​analysis engine.

    In the modern age of Rewa, where the promotion of telework and paperless operations is remarkable, there are increasing opportunities to handle images and videos in many businesses. Being able to perform a large amount of image analysis faster than humans and draw conclusions is a great business advantage. Below, we will introduce the image analysis mechanism in an easy-to-understand manner.

    image analysis

    How is image analysis performed?

    Image discrimination on a computer is a very sophisticated and complex process. This is because mathematical methods are required from object extraction to data arithmetic processing and final discrimination, without relying on the feeling of appearance and smell like human beings. In short,

    • Extracting objects
    • Arithmetic processing of extracted pixel data
    • Discrimination based on calculation results

    It is a mechanism that the image is analyzed through the procedure such as.

    To explain these procedures in order, after acquiring the image data of the object, “image processing/extraction” is performed to make it easier to recognize mechanically. The procedure for image processing/extraction is as follows.

    • Remove noise and distortion from the image
    • Adjust brightness and color
    • Emphasize the outline of the object
    • Extract the area of ​​the object and distinguish it from the background
    • Extract image data of an object in pixel units

    Once you know what is in the image, the next step is to identify “what is the data of the extracted object”. This is the process of “judging an object from accumulated memories and experiences” when compared to humans. A computer is trained in advance with a large amount of “image data” and “labels (information about what the image data represents)”, and the object is identified from the information.

    Nowadays, by combining it with AI technology (deep learning), it has become possible to discriminate images with higher accuracy. Technologies that combine image discrimination and deep learning include:

    • Object recognition: Determines whether or not there is a specified object in space
    • Face recognition: Identify an individual from the faces in the image
    • Character recognition: Identifies characters are written on blog articles and YouTube thumbnails

    With the development of AI, the accumulation and analysis of vast amounts of image data have progressed, and these are widely used not only at manufacturing sites but also in the fields of medicine and agriculture.

     

    When is image analysis used?

    From here, we will introduce examples of image analyses that are already active in various industries and businesses.

     

    ● “Inspection of products flowing on the production line” in the manufacturing industry

    An image discrimination system can be used to label products flowing on conveyors. Labeling work performed manually has uneven accuracy and speed, and it tends to be difficult to allocate personnel according to the production volume. Therefore, by entrusting the process of checking the product with the robot and the image discrimination system and labeling the product in the correct position, it becomes easier to achieve quality averaging while greatly improving the work speed.

    In addition, the beer bottles for recycling collected at the beer factory are checked by human eyes for cracks, and this visual system can also reduce labor costs. The use of image analysis is expanding in the manufacturing industry, such as the adoption of a system that automatically repels bottles in poor condition by photographing and analyzing beer bottles.

     

    ● “Pathological examination” to analyze X-rays and CT scans

    At medical institutions and clinical laboratory centers, incorporating AI analysis into pathological diagnosis and examination will improve work efficiency and reduce the labor burden. The act of counting cells in a pathological test tends to be laborious. However, image analysis systems have evolved to complete within seconds. Complex analysis is possible, such as detecting objects that meet the conditions and not including unnecessary parts in the count results.

     

    ● “Detection of suspicious persons” at commercial facilities and event venues

    Image analysis and video analysis systems are also expanding into the field of security. It is now possible to identify and track people in a space where many people gather.

    For example, you can quickly narrow down similar people from camera images of a large number of people simply by specifying the characteristics (clothes, baggage) of the person to be monitored. Video analysis is better than human power to quickly check the accumulated camera images. In addition, you can track the behavior by identifying the position and time of the camera in which the target person was shown. Depending on the combination with an external system, it is possible to seamlessly receive and track alerts indicating that suspicious persons/objects have been detected.

    The “face recognition system” that utilizes the human face itself is also being used more and more. Face authentication is one of biometric authentication and is a system that identifies and authenticates the person by extracting the face part from the image and collating it with the database. Faces are especially difficult to duplicate and forge, so they have come to be used in the field of security. The use of face recognition systems is expanding to various fields, from the use of smartphone apps to the security of financial systems and anti-terrorism measures.

    The mechanism of the face recognition system and its advantages over conventional security will be explained in detail in the following articles.

    ■ Enhanced security with a face recognition system! Utilization of face path that spreads by improving accuracy

     

    In addition to this, it can also be used for technical purposes such as extracting photos of our products from SNS posts and using them for marketing. If the analysis technology is further advanced, the range of applications is likely to expand, and image analysis is a field with future potential.

    In addition, image analysis systems are increasingly being adopted from the perspective of crime prevention. A system is in operation that registers the data of a person with a criminal record in a computer in advance and constantly checks whether the person appears in the installed security camera. It is expected that this system will become more widespread to deter crime, such as strengthening anti-terrorism measures.

     

    Benefits of introducing image analysis

    Advantages and disadvantages of introducing image analysis into your business
    meritDemerit
    Leading to an improvement in the working environmentCosts are incurred for the introduction of equipment such as cameras and monitors
    It will be easier to improve work efficiency and reduce labor costs.There are few image analysis apps and software that can be used for business

    There are two advantages to introducing image analysis.

     

    ● It leads to improvement of working environment

    By combining image discrimination equipment with robots or systems, automation of the production process (FA) can be promoted. Speaking of inspection work, it is possible to inspect products without human hands by incorporating a program that removes or moves products using a robot arm in conjunction with an error in the image discrimination device. The system is free of fatigue, injury, and illness, so you can operate for long periods or work in dangerous locations without any problems. It will be easier for people to create a safe working environment, such as preventing long working hours and work-related accidents in dangerous areas.

     

    ● It will be easier to improve work efficiency and reduce labor costs.

    Image analysis using a computer can detect small scratches and shape differences that are overlooked by the human eye. The information that can be processed by the human eye is limited, and there is a limit to preventing quality fluctuations due to individual skill differences and fatigue, but AI’s image analysis system can solve these problems. It is also possible to prevent oversights and false positives due to human error and to support high-speed lines, improving the accuracy of inspection work. The labor and personnel required for the work can be reduced, resulting in cost reduction.

     

    Disadvantages of introducing image analysis

    On the other hand, the introduction of image analysis also has its disadvantages.

     

    ● Costs will be incurred for the introduction of equipment such as cameras and monitors

    With a high-performance image analysis system and high-resolution images, it is possible to find small things that cannot be found visually. However, it is costly to introduce such a high-precision system, so there is a disadvantage that it is not easy for companies with limited budgets.

     

    ● There are few image analysis apps and software that can be used for business.

    When introducing an image analysis system in a company, choosing a system with the performance required for the business can balance the cost and the result. However, image analysis systems are not diverse, and it is difficult to find apps and software that fit your business and needs. There is a possibility that various systems will become widespread in the future, but image analysis is still a developing field.

     

    Who is good at developing image analysis systems and software?

    Image analysis has a high affinity with the field of artificial intelligence, and there are many cases where companies that specialize in AI development are involved in image analysis system development. How much effective data can be extracted from images is important for improving work efficiency, so if you request system development, choose a company that can effectively incorporate artificial intelligence. The following pages introduce recommended companies for system development using such artificial intelligence (AI). We have compiled the latest information on companies that specialize in image analysis systems and software development, so please check it out if you are in charge of introducing image

     

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  • What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

    What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

     

    Recent technological improvements have made it possible to handle image analysis to a high level.
    For example, human face authentication is at a level that is also used for immigration at airports.
    In the first place, what kind of technology can image analysis be? And we will introduce examples of how it is used in multiple industries.
    We will explain all the advantages and disadvantages that you should know when considering the introduction.

     

    What is image analysis?

    Image analysis is a system in which a computer makes various decisions after an image is acquired using a sensor such as a camera. It also uses the recognition technology installed in the computer to understand the contents of the image, enabling information extraction and data conversion. The image is to recognize the characteristics of an object in image data such as photographs and videos, and to “mechanically determine what kind of object it is.”

    For example, when a human sees an apple, he or she comprehensively looks at the shape, color, size, etc., and determines that this is an apple. The reason why we can make that distinction is that there are many judgment factors such as knowledge, perception, and memory that human beings have accumulated so far. Taking apples as an example, the following points will help you make a decision.

    • “It has a shape close to a sphere”
    • “The color is red or blue (green)”
    • “It’s about the size of a baseball ball or a shot put the ball.”
    • “There are dents at the top and bottom”
    • “It has a calyx”

    Image analysis performs this kind of information processing that humans naturally do on a computer. The computer that captured the image as data extracts the characteristics of the object and determines “what is reflected in the image?”. Technologies that are used in our daily lives include fingerprint recognition systems for smartphones and face recognition systems installed in digital cameras. Some digital cameras even have a smile detection function. With similar technology, “video analysis” that analyzes video is also advancing. Both refer to processing and analyzing images and videos with a computer to retrieve useful information.

     

    Why image analysis is needed

    There is a limit to the amount that can be visually diagnosed by the human eye, and it is impossible to analyze a large amount of image data instantly. On the other hand, continuing a lot of work is a specialty of computers. For image analysis, all you have to do is apply the captured image or video to the AI ​​analysis engine.

    In the modern age of Rewa, where the promotion of telework and paperless operations is remarkable, there are increasing opportunities to handle images and videos in many businesses. Being able to perform a large amount of image analysis faster than humans and draw conclusions is a great business advantage. Below, we will introduce the image analysis mechanism in an easy-to-understand manner.

    image analysis

    How is image analysis performed?

    Image discrimination on a computer is a very sophisticated and complex process. This is because mathematical methods are required from object extraction to data arithmetic processing and final discrimination, without relying on the feeling of appearance and smell like human beings. In short,

    • Extracting objects
    • Arithmetic processing of extracted pixel data
    • Discrimination based on calculation results

    It is a mechanism that the image is analyzed through the procedure such as.

    To explain these procedures in order, after acquiring the image data of the object, “image processing/extraction” is performed to make it easier to recognize mechanically. The procedure for image processing/extraction is as follows.

    • Remove noise and distortion from the image
    • Adjust brightness and color
    • Emphasize the outline of the object
    • Extract the area of ​​the object and distinguish it from the background
    • Extract image data of an object in pixel units

    Once you know what is in the image, the next step is to identify “what is the data of the extracted object”. This is the process of “judging an object from accumulated memories and experiences” when compared to humans. A computer is trained in advance with a large amount of “image data” and “labels (information about what the image data represents)”, and the object is identified from the information.

    Nowadays, by combining it with AI technology (deep learning), it has become possible to discriminate images with higher accuracy. Technologies that combine image discrimination and deep learning include:

    • Object recognition: Determines whether or not there is a specified object in space
    • Face recognition: Identify an individual from the faces in the image
    • Character recognition: Identifies characters are written on blog articles and YouTube thumbnails

    With the development of AI, the accumulation and analysis of vast amounts of image data have progressed, and these are widely used not only at manufacturing sites but also in the fields of medicine and agriculture.

     

    When is image analysis used?

    From here, we will introduce examples of image analyses that are already active in various industries and businesses.

     

    ● “Inspection of products flowing on the production line” in the manufacturing industry

    An image discrimination system can be used to label products flowing on conveyors. Labeling work performed manually has uneven accuracy and speed, and it tends to be difficult to allocate personnel according to the production volume. Therefore, by entrusting the process of checking the product with the robot and the image discrimination system and labeling the product in the correct position, it becomes easier to achieve quality averaging while greatly improving the work speed.

    In addition, the beer bottles for recycling collected at the beer factory are checked by human eyes for cracks, and this visual system can also reduce labor costs. The use of image analysis is expanding in the manufacturing industry, such as the adoption of a system that automatically repels bottles in poor condition by photographing and analyzing beer bottles.

     

    ● “Pathological examination” to analyze X-rays and CT scans

    At medical institutions and clinical laboratory centers, incorporating AI analysis into pathological diagnosis and examination will improve work efficiency and reduce the labor burden. The act of counting cells in a pathological test tends to be laborious. However, image analysis systems have evolved to complete within seconds. Complex analysis is possible, such as detecting objects that meet the conditions and not including unnecessary parts in the count results.

     

    ● “Detection of suspicious persons” at commercial facilities and event venues

    Image analysis and video analysis systems are also expanding into the field of security. It is now possible to identify and track people in a space where many people gather.

    For example, you can quickly narrow down similar people from camera images of a large number of people simply by specifying the characteristics (clothes, baggage) of the person to be monitored. Video analysis is better than human power to quickly check the accumulated camera images. In addition, you can track the behavior by identifying the position and time of the camera in which the target person was shown. Depending on the combination with an external system, it is possible to seamlessly receive and track alerts indicating that suspicious persons/objects have been detected.

    The “face recognition system” that utilizes the human face itself is also being used more and more. Face authentication is one of biometric authentication and is a system that identifies and authenticates the person by extracting the face part from the image and collating it with the database. Faces are especially difficult to duplicate and forge, so they have come to be used in the field of security. The use of face recognition systems is expanding to various fields, from the use of smartphone apps to the security of financial systems and anti-terrorism measures.

    The mechanism of the face recognition system and its advantages over conventional security will be explained in detail in the following articles.

    ■ Enhanced security with a face recognition system! Utilization of face path that spreads by improving accuracy

     

    In addition to this, it can also be used for technical purposes such as extracting photos of our products from SNS posts and using them for marketing. If the analysis technology is further advanced, the range of applications is likely to expand, and image analysis is a field with future potential.

    In addition, image analysis systems are increasingly being adopted from the perspective of crime prevention. A system is in operation that registers the data of a person with a criminal record in a computer in advance and constantly checks whether the person appears in the installed security camera. It is expected that this system will become more widespread to deter crime, such as strengthening anti-terrorism measures.

     

    Benefits of introducing image analysis

    Advantages and disadvantages of introducing image analysis into your business
    meritDemerit
    Leading to an improvement in the working environmentCosts are incurred for the introduction of equipment such as cameras and monitors
    It will be easier to improve work efficiency and reduce labor costs.There are few image analysis apps and software that can be used for business

    There are two advantages to introducing image analysis.

     

    ● It leads to improvement of working environment

    By combining image discrimination equipment with robots or systems, automation of the production process (FA) can be promoted. Speaking of inspection work, it is possible to inspect products without human hands by incorporating a program that removes or moves products using a robot arm in conjunction with an error in the image discrimination device. The system is free of fatigue, injury, and illness, so you can operate for long periods or work in dangerous locations without any problems. It will be easier for people to create a safe working environment, such as preventing long working hours and work-related accidents in dangerous areas.

     

    ● It will be easier to improve work efficiency and reduce labor costs.

    Image analysis using a computer can detect small scratches and shape differences that are overlooked by the human eye. The information that can be processed by the human eye is limited, and there is a limit to preventing quality fluctuations due to individual skill differences and fatigue, but AI’s image analysis system can solve these problems. It is also possible to prevent oversights and false positives due to human error and to support high-speed lines, improving the accuracy of inspection work. The labor and personnel required for the work can be reduced, resulting in cost reduction.

     

    Disadvantages of introducing image analysis

    On the other hand, the introduction of image analysis also has its disadvantages.

     

    ● Costs will be incurred for the introduction of equipment such as cameras and monitors

    With a high-performance image analysis system and high-resolution images, it is possible to find small things that cannot be found visually. However, it is costly to introduce such a high-precision system, so there is a disadvantage that it is not easy for companies with limited budgets.

     

    ● There are few image analysis apps and software that can be used for business.

    When introducing an image analysis system in a company, choosing a system with the performance required for the business can balance the cost and the result. However, image analysis systems are not diverse, and it is difficult to find apps and software that fit your business and needs. There is a possibility that various systems will become widespread in the future, but image analysis is still a developing field.

     

    Who is good at developing image analysis systems and software?

    Image analysis has a high affinity with the field of artificial intelligence, and there are many cases where companies that specialize in AI development are involved in image analysis system development. How much effective data can be extracted from images is important for improving work efficiency, so if you request system development, choose a company that can effectively incorporate artificial intelligence. The following pages introduce recommended companies for system development using such artificial intelligence (AI). We have compiled the latest information on companies that specialize in image analysis systems and software development, so please check it out if you are in charge of introducing image

     

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  • What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

    What is “image analysis”? Easy-to-understand explanation of the mechanism and specific uses

     

    Recent technological improvements have made it possible to handle image analysis to a high level.
    For example, human face authentication is at a level that is also used for immigration at airports.
    In the first place, what kind of technology can image analysis be? And we will introduce examples of how it is used in multiple industries.
    We will explain all the advantages and disadvantages that you should know when considering the introduction.

     

    What is image analysis?

    Image analysis is a system in which a computer makes various decisions after an image is acquired using a sensor such as a camera. It also uses the recognition technology installed in the computer to understand the contents of the image, enabling information extraction and data conversion. The image is to recognize the characteristics of an object in image data such as photographs and videos, and to “mechanically determine what kind of object it is.”

    For example, when a human sees an apple, he or she comprehensively looks at the shape, color, size, etc., and determines that this is an apple. The reason why we can make that distinction is that there are many judgment factors such as knowledge, perception, and memory that human beings have accumulated so far. Taking apples as an example, the following points will help you make a decision.

    • “It has a shape close to a sphere”
    • “The color is red or blue (green)”
    • “It’s about the size of a baseball ball or a shot put the ball.”
    • “There are dents at the top and bottom”
    • “It has a calyx”

    Image analysis performs this kind of information processing that humans naturally do on a computer. The computer that captured the image as data extracts the characteristics of the object and determines “what is reflected in the image?”. Technologies that are used in our daily lives include fingerprint recognition systems for smartphones and face recognition systems installed in digital cameras. Some digital cameras even have a smile detection function. With similar technology, “video analysis” that analyzes video is also advancing. Both refer to processing and analyzing images and videos with a computer to retrieve useful information.

     

    Why image analysis is needed

    There is a limit to the amount that can be visually diagnosed by the human eye, and it is impossible to analyze a large amount of image data instantly. On the other hand, continuing a lot of work is a specialty of computers. For image analysis, all you have to do is apply the captured image or video to the AI ​​analysis engine.

    In the modern age of Rewa, where the promotion of telework and paperless operations is remarkable, there are increasing opportunities to handle images and videos in many businesses. Being able to perform a large amount of image analysis faster than humans and draw conclusions is a great business advantage. Below, we will introduce the image analysis mechanism in an easy-to-understand manner.

    image analysis

    How is image analysis performed?

    Image discrimination on a computer is a very sophisticated and complex process. This is because mathematical methods are required from object extraction to data arithmetic processing and final discrimination, without relying on the feeling of appearance and smell like human beings. In short,

    • Extracting objects
    • Arithmetic processing of extracted pixel data
    • Discrimination based on calculation results

    It is a mechanism that the image is analyzed through the procedure such as.

    To explain these procedures in order, after acquiring the image data of the object, “image processing/extraction” is performed to make it easier to recognize mechanically. The procedure for image processing/extraction is as follows.

    • Remove noise and distortion from the image
    • Adjust brightness and color
    • Emphasize the outline of the object
    • Extract the area of ​​the object and distinguish it from the background
    • Extract image data of an object in pixel units

    Once you know what is in the image, the next step is to identify “what is the data of the extracted object”. This is the process of “judging an object from accumulated memories and experiences” when compared to humans. A computer is trained in advance with a large amount of “image data” and “labels (information about what the image data represents)”, and the object is identified from the information.

    Nowadays, by combining it with AI technology (deep learning), it has become possible to discriminate images with higher accuracy. Technologies that combine image discrimination and deep learning include:

    • Object recognition: Determines whether or not there is a specified object in space
    • Face recognition: Identify an individual from the faces in the image
    • Character recognition: Identifies characters are written on blog articles and YouTube thumbnails

    With the development of AI, the accumulation and analysis of vast amounts of image data have progressed, and these are widely used not only at manufacturing sites but also in the fields of medicine and agriculture.

     

    When is image analysis used?

    From here, we will introduce examples of image analyses that are already active in various industries and businesses.

     

    ● “Inspection of products flowing on the production line” in the manufacturing industry

    An image discrimination system can be used to label products flowing on conveyors. Labeling work performed manually has uneven accuracy and speed, and it tends to be difficult to allocate personnel according to the production volume. Therefore, by entrusting the process of checking the product with the robot and the image discrimination system and labeling the product in the correct position, it becomes easier to achieve quality averaging while greatly improving the work speed.

    In addition, the beer bottles for recycling collected at the beer factory are checked by human eyes for cracks, and this visual system can also reduce labor costs. The use of image analysis is expanding in the manufacturing industry, such as the adoption of a system that automatically repels bottles in poor condition by photographing and analyzing beer bottles.

     

    ● “Pathological examination” to analyze X-rays and CT scans

    At medical institutions and clinical laboratory centers, incorporating AI analysis into pathological diagnosis and examination will improve work efficiency and reduce the labor burden. The act of counting cells in a pathological test tends to be laborious. However, image analysis systems have evolved to complete within seconds. Complex analysis is possible, such as detecting objects that meet the conditions and not including unnecessary parts in the count results.

     

    ● “Detection of suspicious persons” at commercial facilities and event venues

    Image analysis and video analysis systems are also expanding into the field of security. It is now possible to identify and track people in a space where many people gather.

    For example, you can quickly narrow down similar people from camera images of a large number of people simply by specifying the characteristics (clothes, baggage) of the person to be monitored. Video analysis is better than human power to quickly check the accumulated camera images. In addition, you can track the behavior by identifying the position and time of the camera in which the target person was shown. Depending on the combination with an external system, it is possible to seamlessly receive and track alerts indicating that suspicious persons/objects have been detected.

    The “face recognition system” that utilizes the human face itself is also being used more and more. Face authentication is one of biometric authentication and is a system that identifies and authenticates the person by extracting the face part from the image and collating it with the database. Faces are especially difficult to duplicate and forge, so they have come to be used in the field of security. The use of face recognition systems is expanding to various fields, from the use of smartphone apps to the security of financial systems and anti-terrorism measures.

    The mechanism of the face recognition system and its advantages over conventional security will be explained in detail in the following articles.

    ■ Enhanced security with a face recognition system! Utilization of face path that spreads by improving accuracy

     

    In addition to this, it can also be used for technical purposes such as extracting photos of our products from SNS posts and using them for marketing. If the analysis technology is further advanced, the range of applications is likely to expand, and image analysis is a field with future potential.

    In addition, image analysis systems are increasingly being adopted from the perspective of crime prevention. A system is in operation that registers the data of a person with a criminal record in a computer in advance and constantly checks whether the person appears in the installed security camera. It is expected that this system will become more widespread to deter crime, such as strengthening anti-terrorism measures.

     

    Benefits of introducing image analysis

    Advantages and disadvantages of introducing image analysis into your business
    meritDemerit
    Leading to an improvement in the working environmentCosts are incurred for the introduction of equipment such as cameras and monitors
    It will be easier to improve work efficiency and reduce labor costs.There are few image analysis apps and software that can be used for business

    There are two advantages to introducing image analysis.

     

    ● It leads to improvement of working environment

    By combining image discrimination equipment with robots or systems, automation of the production process (FA) can be promoted. Speaking of inspection work, it is possible to inspect products without human hands by incorporating a program that removes or moves products using a robot arm in conjunction with an error in the image discrimination device. The system is free of fatigue, injury, and illness, so you can operate for long periods or work in dangerous locations without any problems. It will be easier for people to create a safe working environment, such as preventing long working hours and work-related accidents in dangerous areas.

     

    ● It will be easier to improve work efficiency and reduce labor costs.

    Image analysis using a computer can detect small scratches and shape differences that are overlooked by the human eye. The information that can be processed by the human eye is limited, and there is a limit to preventing quality fluctuations due to individual skill differences and fatigue, but AI’s image analysis system can solve these problems. It is also possible to prevent oversights and false positives due to human error and to support high-speed lines, improving the accuracy of inspection work. The labor and personnel required for the work can be reduced, resulting in cost reduction.

     

    Disadvantages of introducing image analysis

    On the other hand, the introduction of image analysis also has its disadvantages.

     

    ● Costs will be incurred for the introduction of equipment such as cameras and monitors

    With a high-performance image analysis system and high-resolution images, it is possible to find small things that cannot be found visually. However, it is costly to introduce such a high-precision system, so there is a disadvantage that it is not easy for companies with limited budgets.

     

    ● There are few image analysis apps and software that can be used for business.

    When introducing an image analysis system in a company, choosing a system with the performance required for the business can balance the cost and the result. However, image analysis systems are not diverse, and it is difficult to find apps and software that fit your business and needs. There is a possibility that various systems will become widespread in the future, but image analysis is still a developing field.

     

    Who is good at developing image analysis systems and software?

    Image analysis has a high affinity with the field of artificial intelligence, and there are many cases where companies that specialize in AI development are involved in image analysis system development. How much effective data can be extracted from images is important for improving work efficiency, so if you request system development, choose a company that can effectively incorporate artificial intelligence. The following pages introduce recommended companies for system development using such artificial intelligence (AI). We have compiled the latest information on companies that specialize in image analysis systems and software development, so please check it out if you are in charge of introducing image

     

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  • What is a legacy system? Benefits of migration and openness

    What is a legacy system? Benefits of migration and openness

    Legacy systems are “older systems”.
    Legacy systems are often related to the core of a company, such as core systems, and cannot be easily changed.
    However, because it is an old model, it is difficult to support new technology, and maintenance costs and time are required.
    This time, we will introduce the basic knowledge of legacy systems, migration to new systems, and opening of legacy systems.

     

    What is a legacy system?

    The legacy system is a term that refers to a system that has been built with old technologies and mechanisms and has become complicated and black-boxed.

    “Legacy” originally means “assets” or “things that have been passed down from generation to generation.” It’s not a bad thing in the first place, but when we use the term “legacy system” in information system terms, it means “old and outdated system”.

    Legacy systems have been introduced and put into practical use for a long time, so they are difficult to expand and maintain and are not compatible with new technologies and business models. In some cases, more advanced technologies have already become commonplace. Such legacy systems are also known as “Technical Debt.”

    An old system is called a legacy system, but there is no clear definition of how old it is called “legacy”.

    Also, what is pointed to depends on the situation, for example, when introducing a new system, the existing system is called “legacy system”, and when new technology such as USB comes out, the old technology such as serial connection is called “legacy”. increase.

    In system development, a legacy system uses a mainframe (general-purpose computer) installed in a company and is the basis of the company’s core system. Also called the “proprietary (proprietary specification)” system, it was developed by a vendor based on old-fashioned technology with its technology, and it is a system built to order for each company.

    In Japan, vendors collectively provide legacy systems such as mission-critical systems, applications (software) for each business, and necessary hardware. It is a stable system that has been used for many years while being maintained by the vendor.

    However, it is a very complicated system because it does not suit the current corporate situation and it is revised and changed by Tsugihagi every time the management activity changes.

     

    Impact of system legacy

    People tend to think that it would be good if the system works stably even with a legacy system, but what kind of impact will it have if the system becomes a legacy system?

     

    ● Mechanism of legacy

    IT technology is advancing so fast that it is called dog-ear (growing 7 times faster than humans) or mouse-ear (growing 18 times faster than humans), and current technology will soon become a legacy. However, user progress is not as fast as technological progress, so many people continue to use older devices and systems. Therefore, the system will not easily disappear as it gets older. This is the mechanism by which “legacy” is born.

     

    ● What happens when the system becomes a legacy?

    <The usability of the system deteriorates>

    Legacy systems utilize old technologies and cannot introduce new technologies or devices. A bespoke system may look like a black box that is different from the original specifications and documents due to various modifications and changes over time. If this happens, it will be difficult to link with other systems, and the usability of the system will deteriorate.

     

    <Maintenance cost will be high>

    Legacy systems have higher maintenance and operational costs. There are several factors in this, one is that manufacturer support has ended. Since the core system introduces the hardware and the system together, the hardware is also becoming a legacy. If the hardware is old, the firmware will be old and the security response will be slow. Due to the end of manufacturer support, parts may not be available for repair.

    Also, over the years, the improved system has become bloated and complex. What’s more, if the engineer who first built the system is retired, no staff can see the whole picture, so it becomes a black box, a rigid system that is difficult to modify, and extra time for management. Will be applied.

     

    <Delayed utilization of the latest technology>

    Legacy systems use old technology that is premised on closed systems. We do not anticipate the introduction of new technologies such as the cloud and OSS (Open-source software). As a result, major modifications are required to accommodate new technologies.

    ● Legacy-free method

    There is a method called “legacy-free” to solve the weaknesses of such legacy systems. Legacy-free means “free from legacy”, eliminating systems and devices that are considered legacies, such as ISA buses and earphone jacks, like Apple’s Macs and iPhones, and making only new technologies. Refers to a system or computer.

    You can use new technology to build high-performance systems, but it’s also a controversial approach because it takes the time and effort of users to adapt to the new system.

     

    What is the “cliff of 2025”?

    The “cliff of 2025”, which is closely related to the legacy system, was announced by the Ministry of Economy, Trade, and Industry in 2018. It is the content pointed out in the DX report. By the way, “DX” is an abbreviation for “Digital Transformation”. DX means that digital technology can be used by companies to create businesses and improve consumers’ lives.

    Specifically, the “cliff of 2025” is the delay in international competition, economic stagnation, etc. that would be expected if the existing system (complexity, aging, black-boxed legacy system) remains in the future. It is a word that refers to. He also points out that the retirement of IT human resources and the end of support expected by 2025 may cause more stagnation.

    The occurrence of these economic losses is referred to as the “wall of 2025.”

     

    ● The legacy system closely related to “Cliff in 2025”

    There is a deep connection between the “Cliff of 2025” and the legacy system. In the DX report, companies need DX to leverage new digital technologies to transform their businesses, create new business models, and flexibly modify them for their future growth and competitiveness. You understand. However, in reality, he points out that it has not led to major business transformation.

    The existence of “legacy systems” is cited as one of the major factors that have not led to business transformation.

    As mentioned above, we are concerned that a large amount of cost and human resources spent on legacy systems will make it impossible to invest resources such as IT budgets in new digital technologies, which will reduce the global competitiveness of companies. increase.

    In the DX report released, it is predicted that by 2025, legacy systems that have been in operation for more than 21 years will account for 60% of the total system.

    In the future, these systems need to be renewed, and we are cautioning that companies that miss this wave of renewal will lose many business opportunities. To overcome the wall of 2025, it is necessary to clear the problem of the legacy system, but it is pointed out that it will take time to overcome it. For this reason, companies are required to take immediate action against legacy systems.

     

    Legacy system measures (“migration” and “modernization”)

    Legacy migration is the migration of an existing legacy system to a new system. “Migration” means “relocation” and “migration” and is one of the important issues for many companies using legacy mission-critical systems.

     

    ● Advantages of legacy migration

    <Review of the system according to the times>

    Migration involves switching vendor-specific closed systems to open applications. While inheriting the existing software assets, we will reduce the size of the hardware for easy maintenance.

    Since an open environment is built by migration for a closed legacy system, it is also possible to support cloud computing. You can build a system with a high degree of freedom that does not depend on a specific vendor.

     

    <Utilization of assets such as know-how>

    Since migration can utilize many years of know-how as it is, the cost of user education related to system introduction can also be reduced. In addition, gradual migration is possible, so you can work without stopping the system or use the new system immediately after performing the migration.

     

    <Reduction of TCO>

    Legacy migration can reduce TCO (Total Cost of Ownership). Since it is based on the existing system, the initial cost can be reduced compared to the new installation, and the running cost of the hardware can be reduced by downsizing.

     

    ● Further evolved “modernization”

    As an advanced version of legacy migration, there is also a method called “modernization”. Modernization is a noun for the word “modernize”.

    In modernization, we will introduce new IT technology without changing the parts that correspond to the system specifications and requirements definition. By using the current specifications as they are, the system can be turned into new hardware while keeping the existing software assets. Modernization makes it possible to maintain the assets and stability of legacy systems and adapt to new technologies at the same time.

     

    Benefits of opening a legacy system

    Opening means changing a closed system to an open system. It is not an original system, but a system built by combining popular OS and hardware whose technical specifications are generally open to the public.

    However, in Japan, many companies are reluctant to open legacy systems for security reasons.

    Systems with general-purpose computers are inflexible and unsuitable for real-time data analysis. On the other hand, open systems are highly expandable and allow flexible system operation. With the advancement of IT technology, we are entering an era in which a sense of speed is emphasized in business, and open systems have become indispensable.

    In addition, open systems can be easily modified, changed, or added to the system. Since all legacy systems are made-to-order and proprietary, it was difficult for non-developers to maintain. However, open systems are easy to use because they can be easily added, modified, and improved, and since they are not proprietary specifications, the options that can be used increase, and the expandability of functions increases.

     

    Utilizing the cloud to break away from legacy systems

    We talked about migration and modernization, but “utilizing the cloud” is also indispensable for breaking away from legacy systems.

    The on-premises type legacy system owned by the company needs to be operated on the assumption of hardware failure. As a result, hardware replacement costs are incurred regularly, and this accumulation puts pressure on the company’s IT budget. Many IT professionals will be worried about these hardware lifecycle spells.

    Also, in legacy systems, there is a big disadvantage that the system structure becomes a black box.

    If it cannot be modified or expanded by the company, it will not be usable as a system. And, to continue operation, new maintenance costs will continue to increase, and at the same time, know-how will become easier to personalize and it will be difficult to take over. What’s more, it falls into a negative spiral, where system changes tend to be difficult.

    “Utilization of the cloud” solves the above problems. By utilizing the cloud, the cost related to hardware that was incurred regularly is only the service usage fee paid to the external service. In addition, the man-hours required for hardware maintenance can be reduced, so the operation of human resources can be secured not for maintenance but other tasks. Doing so allows you to focus your resources on more productive tasks and get closer to solving problems.

    If companies can take advantage of the cloud, they will be freed from the curse of legacy systems.

    To get rid of the old and inefficient system operation, let’s consider the use of the cloud and take immediate action.

     

    Continuous metabolism of legacy systems

    Since many of the core systems and business systems that have been used for a long time have become legacy, many companies have started to take measures or have already completed the measures.
    However, as technology advances, it will be necessary to migrate again.
    When migrating, use open standard specifications and applications, and try to constantly metabolize the system in daily operations even after migration.
    That is the point to prevent the legacy system from becoming more legacy.

     

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