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조선 초기 관료의 관청이동을 통해 본 주요 통치기구의 위상 - HAVNet 자료를 중심으로 -
최상일 ( Choi Sangil ),백승민 ( Paek Seungmin ),최지우 ( Choi Jiwoo ),예홍진 ( Yeh Hongjin ),이상국 ( Lee Sangkuk ) 수선사학회 2021 史林 Vol.- No.75
This paper is an introductory study to comprehensively grasp the betweenness centrality of government offices in the early Joseon dynasty using HAVNet data. The process of extracting data around the “Annals of the Joseon Dynasty” and designing and building HAVNet as the basis for research was conducted by history and computer science researchers, which were not the usual method in Korean historical research until now. The approach and analysis results carried out in this study are fundamentally different from previous studies related to the government organizations in the early Joseon dynasty. It is unique study in that it was analyzed based on the contents recorded in the Annals of the Joseon dynasty, not on previous research framework of analysis of state administration and government organizations in the early Joseon dynasty. Based on this study, we will conduct an interdisciplinary research that comprehensively analyzes blood ties and relationships among all historical figures of the Joseon dynasty.
신 장애물제한표면에 관한 이론적 고찰과 실증분석 - 인천국제공항을 중심으로 -
최상일,유수정,곽기열,김현미,김휘양,Choi, Sangil,Yu, Soojeong,Kwak, Kiyeol,Kim, Hyeonmi,Kim, Huiyang 한국항공운항학회 2022 한국항공운항학회지 Vol.30 No.3
Obstacle Limitation Surface (OLS) is conceptual surface establishing the airspace around aerodromes to be maintained from obstacles to ensure safe aircraft operations. Despite advances in the technologies for aircraft, navigation systems and the development of new flight procedures, the criteria defining OLS have not been amended since its initial establishment, resulting in the overestimation of areas for height restriction. As there were requests to examine OLS at the 12th Air Navigation Conference and the 38th ICAO Assembly, the research on the OLS revision began in earnest and ICAO has proposed Obstacle Free Surface (OFS) and Obstacle Evaluation Surface (OES) as an alternative of the existing OLS. OFS is surfaces where obstacles shall not be permitted, and OES is ones where obstacles be evaluated with an aeronautical study and could be permitted under some conditions. The purpose of this study is to preemptively assess the efficiency and safety of OFS and OES by applying them to the second runway (15L/33R) of Incheon International Airport. The results show that OFS and OES are capable of serving the instrument flight procedure safely with a smaller obstacle clearance area compared to the existing OLS.
최지우(Jiwoo Choi),최상일(Sangil Choi),강태원(Taewon Kang) 한국정보기술학회 2022 한국정보기술학회논문지 Vol.20 No.10
In a society centered on hyper-connectivity, as important as information sharing is that each piece of information must be viewed only by legitimate users. In this study, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method. After learning human gait with CNN, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 7 seconds while carrying the smartphone. Accuracy, precision, recall, F1-score, and EER were applied as evaluation indicators of the model proposed in this study. As a result, accuracy, precision, recall, and F1-score all achieved an average of 95% or more, and the average EER was 0.048. What the system analysis results show is that the system proposed in this study has high reliability and low error rate. As a result, this study showed the possibility that human gait could be used as a new user authentication method.
최지우(Jiwoo Choi),최상일(Sangil Choi),강태원(Taewon Kang) 한국정보기술학회 2022 한국정보기술학회논문지 Vol.20 No.11
Various studies exist to identify individuals. Personal identification research based on inertial data, that is, acceleration and angular velocity acquired with an inertial sensor, is also one of these efforts. In fact, when learning inertial data with CNN, individuals can be identified with high accuracy. However, the individual identification model using inertial data significantly lowers the identification performance when the shoes worn by the individual change. This paper deals with improving this problem by using gait cycle data extracted from inertia data. First, the CNN model using the gait cycle was implemented, and then the model was evaluated using the representative performance evaluation indicators, such as accuracy, precision, recall, and F1-score. As a result, it was confirmed that the proposed model can identify individuals with more than 90% accuracy even when the shoes worn are different.
최지우(Jiwoo Choi),유형진(Hyungjin Yoo),최상일(Sangil Choi),강태원(Taewon Kang) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.8
In the gait cycle, heel strike (HS) and toe off (TO) of both feet are repeatedly included, and identification of the gait cycle is to find these activities within the cycle. In this paper, we study a method to identify the gait cycle using LSTM, a representative recurrent neural network model. Learning data were collected by performing a gait experiment 20 times each on 50 study participants, 25 males and females each. When performing the experiment, they wear an inertial sensor(acceleration and angular velocity) on their left wrist for collecting gait data. As a result of obtaining the average precision and recall after learning through the LSTM model, the precision was 95.98% and the recall rate was 93.18%. Through this, it can be confirmed that it is an effective method to identify the gait cycle by learning the data obtained from the inertial sensor with the proposed LSTM model.