• Journal of Digital Convergence
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• v.17 no.2
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• pp.155-161
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• 2019

Electroencephalography (EEG), a representative method of identifying temporal and spatial changes in brain activity, is a voluntary electrical activity measurable in the human scalp. Various interface technologies have been provided to control EEG activity, and it is possible to operate a machine such as a wheelchair or a robot through brainwaves. The characteristics of EEG data are collected in various types of channels in real time, and a server system for analyzing them is required to have an independent and lightweight system for the platform. In these days, the Spring platform is used as a large business server as an independent, lightweight server system. In this paper, we propose an EEG analysis system using the Spring server system. Using the proposed system, the reliability of EEG control can be enhanced, and analysis and control interface expansion can be provided in various aspects such as game and medical areas.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.47-53
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• 2019

In general, dizziness is caused by various situations, but among them, symptoms due to dysfunction of the motor system belonging to the nervous system are the most severe, accompanied by nausea and vomiting. Treatment of these dizziness includes drug therapy, surgical therapy, and rehabilitation. Drug therapy and surgery are generally performed in vest rehabilitation training, which is a rehabilitation therapy because of the risk of aftereffects. The vestibular rehabilitation training includes eye training, posture stabilization training, and walking training. Among them, walking training is performed in a certain space under the supervision of a doctor or a professional therapist, so that the time and space burden is increased. In order to solve this problem, we implemented gait training contents which can be used for rehabilitation training by using the augmented reality technology. It is expected that it can be utilized as dizziness rehabilitative contents which can be used in medical environment through clinical tests for patients with dizziness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.77-83
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• 2019

With the recent development of medical technology, the diagnosis rate of type 1 diabetes is increasing and patients are increasing. However, diabetes education content is not aligned with the interest level of children. As a result of the interviews with experts, It was found that the measures of coping with the change of blood sugar and the behavior therapy require steady and repetitive learning. Therefore, this study proposes Edutainment content which can be repeatedly Learned by 10~11year old children. For effective learning, the contents of the laboratory practice were constructed and the hybrid method was used for the repetitive learning. Usability test showed that this configuration is effective. This study is expected to contribute to the study of diabetic education content that is suitable for the children's level of understanding which will be developed easily in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.687-689
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• 2014

Due to the development of health promotion and health care technology of smartphones paradigm from treatment to manage change, and this is increasing the fitness in time for the user. However, fitness club, users find the right exercise law-personal trainer must be costly to pay companies describes the management system is required in order to operate the high costs strain. In this paper, such as scalable servers to OpenWrt Fonera is mainly based on the client to configure the server iOS mobile sensors collect a user's movement through the history of the user's input. This data is sent to the Web and check whether the receiving system via a trainer is to record the users through weight training and to receive feedback.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.573-575
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• 2019

Recently, augmented reality (AR) technology has been widely used in a variety of fields such as defense, medical, education, and entertainment. In the augmented reality environment, a user can interact a virtual object by the user 's specific behaviors. Also, it is necessary to use a technique to automatically interacts among the generated virtual objects. In this paper, we propose an authoring model to apply augmented reality environments based on quantum computing inspired entanglement and overlapping phenomenon. For example, it is possible to allow the user to automatically increase and decrease the number of the object with the characteristics of the virtual object without user intervention. As a tested platform for this purpose, this paper provide an augmented reality eco-system environment with automatic interaction among virtual animals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.699-705
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• 2022

Purpose: Sequence control is widely used by being applied to manufacturing, distribution, construction, and automation in the medical industry. With the development of the fourth industry, artificial intelligence convergence technology in the control field is becoming an important factor in the industry. In particular, it is required to evaluate the safety and innovation of facilities where microprocessors and artificial intelligence are fused to existing systems and develop reliable equipment, so it is intended to develop equipment for educational purposes and drive the development of the field. Method: The self-developed all-in-one artificial intelligence controller module is a device that combines artificial intelligence capabilities with existing sequence and PLC control circuits. As the performance evaluation items of this equipment, the recognition ability of motion, voice, text, color, etc. and the stability and reliability of the circuit were evaluated. Conclusion: After designing the sequence and PLC circuit, the performance evaluation items of the integrated integrated artificial intelligence controller module were all satisfied, and there was no problem in the safety and reliability of the circuit.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.28-30
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• 2022

Recently, the number of confirmed cases has increased again with the new variant of COVID-19. Quarantine is recommended, especially to prevent the rapidly increasing spread, as environmental controls, such as minimizing contact with others, can increase safety. In addition, there are often cases in which the patient's condition cannot be confirmed from the standpoint of a guardian, such as visitation being prohibited under certain conditions. At this time, the sensor data values of oxygen, carbon dioxide concentrations, temperature and humidity, and alcohol, which are medical gases used in hospitals, are collected remotely using ZigBee wireless communication technology. Design a system that can be stored and monitored in a database. We propose an environmental monitoring system, which is a visualization system designed to allow hospitals to check and feedback data on the managed environment, and to give reliability to parents.

• Convergence Security Journal
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• v.24 no.2
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• pp.55-61
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• 2024

With the development of IoT technology, wearable services have also developed rapidly. Wearable devices required for this service are used as sensors and controllers in the form of smart bands. Wearable devices implement very concise SWlogic for possible long-term use and use wireless communication protocols to improve convenience. However, because this wearable device aims to be lightweight, it is more vulnerable to security than terminals used for other information services. Many smart healthcare or smart medical services are passive or do not apply security technology. By exploiting this security environment, attackers can obtain or modify important information through access to wearable devices. In this study, we analyzed the technical operating environment of wearable services and identified authentication information reuse attacks, BIAS attacks, battery drain attacks and firmware attacks on wearable devices. And we analyzed the mechanism of each security threat and confirmed the attack effect. In this study, we presented a response plan to respond to the identified security threats. When developing wearable services, it is expected that safer services can be built if the response plan proposed in this study is considered.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.