• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Microelectronics and Packaging Society
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• v.28 no.4
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• pp.41-45
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• 2021

In this study, in order to secure the high reliability of the memristor, we adopted a patterned lithium filament seed layer as the main agent for resistive switching (RS) characteristic on the 30 nm thick ZrO₂ thin film at the device manufacturing stage. Lithium filament seed layer with a thickness of 5 nm and an area of 5 ㎛ × 5 ㎛ were formed on the ZrO₂ thin film, and various electrode areas were applied to investigate the effect of capacitance on filament type memristive behavior in the parallel memristive circuit of memristor and capacitor. The RS characteristics were measured in the samples before and after 250℃ post-annealing for lithium metal diffusion. In the case of conductive filaments formed by thermal diffusion (post-annealed sample), it was not available to control the filament by applying voltage, and the other hand, the as-deposited sample showed the reversible RS characteristics by the formation and rupture of filaments. Finally, via the comparison of the RS characteristics according to the electrode area, it was confirmed that capacitance is an important factor for the formation and rupture of filaments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.815-824
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• 2022

The current radiation safety management system is also applied to radiation practices at universities. The application of the law raised concerns about poor radiation practice education and hindering the development of radiology. Accordingly, the Korean Radiology Professors Association needed to grasp the reality of the management system for radiation practice education at each university and the current radiation safety management system in the department of Radiological science. So, a survey was conducted on heads of radiological science departments across the country. Through the survey, it was found that the current application of the Nuclear Safety Act to radiation safety management in the department of Radiological science is excessively restrictive and not very effective. In addition, radiology practice education for the purpose of training health and medical professionals should be controlled by the Ministry of Health and Welfare and the Korea Centers for Disease Control, but there is a problem of being supervised by the Nuclear Safety and Security Commission. Therefore, in this study, as a legal improvement plan to solve this problem, first, a plan according to a partial amendment to the Higher Education Act, second, a plan to be supervised by the Ministry of Health and Welfare through the amendment of article 37 of the Medical Service Act, third, article 20-2 of the Enforcement Decree of the Medical Service Technologists Act was newly inserted to propose three measures to be supervised by the Ministry of Health and Welfare.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.975-984
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• 2022

Since a linear transducer has an area of interest equal to the length of the transducer, the area of interest can be expanded using the virtual convex function installed in the device.However, it was thought that the change in the direction of the ultrasonic sound velocity according to the change in the visual area of interest would affect the image quality, so this was objectively confirmed. For this study, image evaluation and SNR·CNR of the phantom for ultrasound quality control were measured. As a result, in the phantom image evaluation, both images were able to identify structures in functional resolution, grayscale, and dynamic range. However, it was confirmed that the standard image was excellent in the reproducibility of the size and shape of the structure. As a result of SNR·CNR evaluation, SNR·CNR of most trapezoidal images was low, except for structures at specific locations. In addition, through the statistical analysis graph, it was further confirmed that the SNR and CNR for each depth decreased as the size of the cystic structure decreased. Through this study, it was confirmed that the use of the function has the advantage of providing a wide visual area of interest, but it has an effect on the image quality. Therefore, when using the virtual convex function, it is judged that the examiner should use it in an appropriate situation and conduct various studies to acquire high-quality images and to improve the understanding and proficiency of the equipment.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.267-273
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• 2022

Purpose: The aim of this study was to assess the influence of kilovoltage- peak (kVp) and the metal artifact reduction (MAR) tool on the detection of buccal and lingual peri-implant dehiscence in the presence of titanium-zirconia (Ti-Zr) and zirconia (Zr) implants in cone-beam computed tomography (CBCT) images. Materials and Methods: Twenty implant sites were created in the posterior region of human mandibles, including control sites (without dehiscence) and experimental sites (with dehiscence). Individually, a Ti-Zr or Zr implant was placed in each implant site. CBCT scans were performed using a Picasso Trio device, with variation in the kVp setting (70 or 90 kVp) and whether the MAR tool was used. Three oral radiologists scored the detection of dehiscence using a 5-point scale. The area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity were calculated and compared by multi-way analysis of variance (α=0.05). Results: The kVp, cortical plate involved (buccal or lingual cortices), and MAR did not influence any diagnostic values (P>0.05). The material of the implant did not influence the ROC curve values(P>0.05). In contrast, the sensitivity and specificity were statistically significantly influenced by the implant material (P<0.05) with Zr implants showing higher sensitivity values and lower specificity values than Ti-Zr implants. Conclusion: The detection of peri-implant dehiscence was not influenced by kVp, use of the MAR tool, or the cortical plate. Greater sensitivity and lower specificity were shown for the detection of peri-implant dehiscence in the presence of a Zr implant.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Membrane Journal
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• v.32 no.5
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• pp.292-303
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• 2022

An eco-friendly energy conversion device without the emission of pollutants has gained much attention due to the rapid use of fossil fuels inducing carbon dioxide emissions ever since the first industrial revolution in the 18th century. Polymer electrolyte membrane fuel cells (PEMFCs) that can produce water during the reaction without the emission of carbon dioxide are promising devices for automotive and residential applications. As a key component of PEMFCs, polymer electrolyte membranes (PEMs) need to have high proton conductivity and physicochemical stability during the operation. Currently, perfluorinated sulfonic acid-based PEMs (PFSA-PEMs) have been commercialized and utilized in PEMFC systems. Although the PFSA-PEMs are found to meet these criteria, there is an ongoing need to improve these further, to be useful in practical PEMFC operation. In addition, the well-known drawbacks of PFSA-PEMs including low glass transition temperature and high gas crossover need to be improved. Therefore, this review focused on recent trends in the development of high-performance PFSA-PEMs in three different ways. First, control of the side chain of PFSA copolymers can effectively improve the proton conductivity and thermal stability by increasing the ion exchange capacity and polymer crystallinity. Second, the development of composite-type PFSA-PEMs is an effective way to improve proton conductivity and physical stability by incorporating organic/inorganic additives. Finally, the incorporation of porous substrates is also a promising way to develop a thin pore-filling membrane showing low membrane resistance and outstanding durability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.169-174
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• 2023

Currently, overall power usage is also increasing as power demand such as homes, offices, and factories increases. The increase in power use also raised interest in standby power as a change in awareness of energy saving appeared. Home and office devices are consuming power even in standby conditions. Accordingly, there is a growing need to reduce standby power, and it aims to have standby power of 1W or less. An intelligent outlet uses a near-field wireless network to connect to a home network and cut or reduce standby power of a lamp or appliance connected to an outlet. This research aims to develop a monitoring system and an intelligent outlet that can remotely monitor the amount of electricity used in a lighting lamp or a home appliance connected to an outlet using a short-range wireless network (Zigbee). Also, The intelligent outlet and monitoring system developed makes it possible for a user to easily cut off standby power by using a portable device. Intelligent outlets will not only reduce standby power but also be applicable to fire prevention systems. Devices that cut off standby power include intelligent outlets and standby power cutoff switches, so they will prevent short circuits and fires.