• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.1-17
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• 2022

Accurate analysis of the causes of metro rail traffic congestion provides a means of addressing issues arising from metro rail traffic congestion in metropolitan areas. Currently, congestion analysis based on counting, weight detection, CCTVs, and mobile Wi-Fi is limited by poor accuracies or because studies have been restricted to single routes and trains. In this study, a train congestion analysis model was used that includes the transfer and multi-path behavior of metro passengers and train operation plans for metropolitan urban railroads. Analysis accuracy was improved by considering traffic patterns in which passengers must wait for next trains due to overcrowding. The model updates train crowding levels every 10 minutes, provides information to potential passengers, and thus, is expected to increase the social benefits provided by the Seoul metropolitan subway

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• Journal of Korean Dental Hygiene Science
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• v.5 no.1
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• pp.21-27
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• 2022

Background: This study was conducted to analyze the time for re-detection of bacteria after surface disinfection using wet wipes, isopropyl alcohol, and benzalkonium chloride antibacterial tissue and provide standards for re-execution of surface disinfection with wet and antibacterial tissues. Methods: Seven laptops were wiped with wet tissue and isopropyl alcohol and benzalkonium chloride antibacterial tissues. Test areas were rubbed with a sterile cotton swab at baseline and after 30, 60, and 120 min. After plating on a tryptic soy agar medium, the number of colonies was counted by culturing at 36.5℃ for 24 h. Results: The average number of bacterial colonies was 5.85 ± 4.33 before isopropyl alcohol wiping and nil after wiping. The average number of bacterial colonies was 12.28 ± 14.67 benzalkonium chloride wiping and nil after wiping. Before wiping with wet wipes, the average number of bacterial colonies on laptop surfaces was 3.42 ± 5.22. Bacteria decreased after wiping with wet wipes but increased again over time. Conclusions: Wet wipes can temporarily reduce bacteria but are unsuitable for removing bacteria.

• Journal of Technologic Dentistry
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• v.44 no.1
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• pp.15-23
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• 2022

Purpose: The purpose of this study was to identify the experiences of depression, suicidal thoughts, and habitual drug use in middle and high school students and examine their relationship with the oral symptoms experiences. Methods: The participants of this study were 54,948 middle and high school students who took the screening and health survey at the 16th "Youth Health Behavior Survey" (2020). The SPSS statistical software (IBM SPSS 23.0 for Windows; IBM) was used for data analysis. The significance level was set to 0.05. Results: Complex-sample logistic regression analysis was performed to confirm the relationship between the experiences of depression, suicidal thoughts, and habitual drug use and oral symptom experienced. The results indicated that the absence of depression, suicidal thoughts, or habitual drugs had a significant effect on oral symptom experience. Conclusion: A systematic counseling program for early detection of oral symptoms and oral health promotion as well as strategies for practicing correct oral hygiene are required. Additionally, it is necessary to develop a customized education program to promote health education in middle and high school students. It can be used as the basis for an integrated support system that students can use to grow healthy. A differentiated program on the topic of mental health promotion for each grade can be planned and its effects can be monitored.

• Annual Conference of KIPS
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• 2020.11a
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• pp.854-857
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• 2020

최근 사람형(Anthropomorphic)로봇손의 사물조작 지능을 개발하기 위하여 행동복제(Behavior Cloning) Deep Reinforcement Learning(DRL) 연구가 진행중이다. 자유도(Degree of Freedom, DOF)가 높은 사람형 로봇손의 학습 문제점을 개선하기 위하여, 행동 복제를 통한 Human Demonstration Augmented(DA)강화 학습을 통하여 사람처럼 사물을 조작하는 지능을 학습시킬 수 있다. 그러나 사물 조작에 있어, 의미 있는 파지를 위해서는 사물의 특정 부위를 인식하고 파지하는 방법이 필수적이다. 본 연구에서는 딥러닝 YOLO기술을 적용하여 사물의 특정 부위를 인식하고, DA-DRL을 적용하여, 사물의 특정 부분을 파지하는 딥러닝 학습 기술을 제안하고, 2 종 사물(망치 및 칼)의 손잡이 부분을 인식하고 파지하여 검증한다. 본 연구에서 제안하는 학습방법은 사람과 상호작용하거나 도구를 용도에 맞게 사용해야하는 분야에서 유용할 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.447-448
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• 2021

The boundary-based security architecture has the advantage of easy deployment of security solutions and high operational efficiency. The boundary-based security architecture is easy to detect and block externally occurring security threats, but is inappropriate to block internally occurring security threats. Unfortunately, internal security threats are increasing in frequency. In order to solve this problem, a zero trust model has been proposed. The zero trust model requires a real-time monitoring function to analyze the behavior of a subject accessing various information resources. However, there is a limit to real-time monitoring of file access of a subject confirmed to be trusted in the system. Accordingly, this study proposes a method to monitor user's file access in real time. To verify the effectiveness of the proposed monitoring method, the target function was verified after the demonstration implementation. As a result, it was confirmed that the method proposed in this study can monitor access to files in real time.

• The Journal of Bigdata
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• v.6 no.2
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• pp.99-108
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• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.161-170
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• 2006

A tracer test technique using a radioisotope was proposed to investigate pollutant mixing characteristics in rivers. The main advantages of radioisotope as a tracer in field tests are that it can be detected easily, and that its detection range is quite large. Also, using the radioisotope, the amount sorbed by the bed material and the biota may be a minimum. Field tracer tests were conducted at seven different sites in natural rivers with various meandering pattern. Based on the acquired data, the behavior of the tracer cloud in the intermediate-field was examined two-dimensionally, and dispersion coefficients were calculated using several evaluation methods. Results revealed that the tracer cloud was transported skewed to the outer bank and dispersion coefficients in bends were larger than those in straight reaches.