• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of Korea Technology Innovation Society
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• v.22 no.1
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• pp.119-157
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• 2019

It is important for the people in the 3D printing industry to determine which factors influence the decision-making that determine the adoption of 3D printers and the role of the factors. Through this, we intend to find ways to contribute to the development of 3D printing industry in Korea by increasing utilization of 3D printer used in domestic companies and increasing investment in related industries. 3D printers are making rapid progress according to the development of technology, the public interest, and the activation of investment. Foreign countries have made remarkable progress in equipment, materials, software, and industrial applications, but they are lower than expected in Korea. It is necessary to introduce a smooth 3D printer in order to revitalize the 3D printer industry and enlarge the base, but it is insufficient for actual introduction and field application. The independent variables that represent economic, technological, and environmental characteristics were selected through a literature survey, and a model for accepting integrated technology for convergence of societies in the 3D printing industry was proposed. This study confirms that economic factors such as output unit price, government support, and environmental factors such as 3D contents should be developed organically for the introduction of 3D printing technology and equipment. This require systematic and effective support from the government, and it is necessary to improve the economic support, related laws, and systems that can be directly experienced by the user as a user. As the domestic 3D printing industry develops with economic, technological and time investment, 3D printing industry should be the key engine of the 4th industrial revolution.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.185-194
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• 2019

Objective: This study was conducted to investigate the intensity of the extremely low frequency magnetic fields(ELF-MF) generated inside of the cabins during subway operation. Methods: The ELF-MF intensity were investigated on 30 subway lines in Korea, including in the Greater Seoul Metropolitan Area(Seoul and Gyeonggi-do Province), Incheon, Busan, Daegu, Daejeon, and Gwangju. ELF-MF intensity was measured at 0.9 m from the floor using EMDEX II meters with a resolution of $0.01{\mu}T$. All data were collected every three seconds and analyzed with EMCALC 2013 version 3.0B software. Basic characteristics of subway operation, including alternative current(AC) or direct current(DC), voltage level, and opening year of the line were investigated. Real-time information during measurement, such as the time of departure, moving and arrival of trains, were also recorded. Results: The arithmetic mean(AM) and maximum(Max) intensity of ELF-MF were $0.62{\mu}T$ and $11.51{\mu}T$, respectively. Compared by region, the ELF-MF intensity measured inside cabin were the highest in the Seoul Metropolitan Area($AM=0.80{\mu}T$), followed by Busan($AM=0.30{\mu}T$), Daegu($AM=0.29{\mu}T$), Incheon($AM=0.14{\mu}T$), Gwangju($AM=0.04{\mu}T$) and Daejeon($AM=0.03{\mu}T$). The average ELF-MF level measured in AC trains($AM=1.36{\mu}T$) was also significantly higher than in DC trains($AM=0.28{\mu}T$). In terms of the opening year of the subway, trains opened before 1990($AM=0.85{\mu}T$) was the highest and the lowest was 2000-2009($AM=0.24{\mu}T$). Conclusions: The AC supply has the greatest influence on the generation of the ELF-MF intensity in subway cabins.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3117-3126
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• 2018

The operating system update is a basic step to maintain a safe internet use environment. This study analyzed whether the implementation of the operating system update was related to gender and age group during the violation accident prevention act in relation to information protection on the internet, and tried to verify the validity of these factors by analyzing the influence of gender and age group. In this study, logistic regression analysis was conducted based on the information security survey data surveyed by the Korea Internet & Security Agency in 2016. As a result, gender and age were surveyed as factors related to the implementation of operating system updates. As a result of analyzing the impact on the implementation of operating system updates by gender, it is estimated that the odds are 0.419 times higher for women than for men. According to the analysis of the operating system update by age group based on the 50s, which is a vulnerable group of information, the result is that the odds are 13.266 times higher in the 20s than the 50s.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.21-26
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• 2022

This study aims to enhance the cultivation of SW·AI basic competencies of middle school students by forming and distributing SW·AI education programs for middle school students who form the basis of their lives. In addition, by planning SW·AI education programs in connection with the regular curriculum, it is intended to serve as a cornerstone for the public education of SW·AI education that will be implemented from 2025. To this end, the concept of SW and AI in middle school was first defined and a plan to link software/artificial intelligence learning factors to the regular curriculum was proposed, and based on this, SW·AI education programs for middle school students were prepared. Based on literature research, the understanding of artificial intelligence technology, the value of data, and the use of artificial intelligence technology in real life were set as SW·AI education contents, and educational programs were organized by linking them with the current middle school curriculum. All SW·AI education was organized in the form of practice rather than theory so that classes could be conducted centered on participants, and the purpose of the course was to cultivate the ability to use artificial intelligence technology in real life based on understanding artificial intelligence technology.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.473-481
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• 2020

Data science starts with small data analysis and includes machine learning and deep learning for big data analysis. Data science is a core area of artificial intelligence technology and should be systematically reflected in the school curriculum. For data science education, The Entry also provides a data analysis tool for elementary education. In a big data analysis, data samples are extracted and analysis results are interpreted through statistical guesses and judgments. In this paper, the big data analysis area that requires statistical knowledge is excluded from the elementary area, and data science education examples focusing on the elementary area are proposed. To this end, the general data science education stage was explained first, and the elementary data science education stage was newly proposed. After that, an example of comparing values of data variables and an example of analyzing correlations between data variables were proposed with public small data provided by Entry, according to the elementary data science education stage. By using these Entry data-analysis examples proposed in this paper, it is possible to provide data science convergence education in elementary school, with given data generated from various subjects. In addition, data science educational materials combined with text, audio and video recognition AI tools can be developed by using the Entry.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.409-415
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• 2021

Medical waste management is becoming increasingly important, specifically in light of the current COVID-19 pandemic, as hospitals, clinics, quarantine centers, and medical research institutes are generating tons of medical waste every day. Previously, a traditional incineration process was utilized for managing medical waste, but the lack of landfill sites, and accompanying environmental concerns endanger public health. Consequently, an innovative sterilization shredding system was developed to resolve this problem. In this research, we focused on the design and numerical analysis of a shredding system for hazardous and infectious medical waste, to establish its operational performance. The shredding machine's components were modeled in a CAD application, and finite element analysis (FEA) was conducted using ABAQUS software. Static, fatigue, and dynamic loading conditions were used to analyze the structural stability of the cutting blade. The blade geometry proved to be effective based on the cutting force applied to shred medical waste. The dynamic stability of the structure was verified using modal analysis. Furthermore, an S-N curve was generated using a high cycle fatigue study, to predict the expected life of the cutting blade. Resultantly, an appropriate shredder system was devised to link with a sterilization unit, which could be beneficial in reducing the volume of medical waste and disposal time, thereof, thus eliminating environmental issues, and potential health hazards.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.351-357
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• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.