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이용원,Lee, Yong won 한국산업보건학회 2017 한국산업보건학회지 Vol.27 No.3
Objectives: This study was conducted to prepare fundamental data and prevention measures on health promotion and occupational disease, and to assess the effects of the working environment on subjective health status and absenteeism among workers using data from the third working environment survey in Korea. Methods: This study's subjects were composed of 29,711 wage workers from the 3rd working environment survey data. The dependent variables were several diseases, subjective health status and absences, and the independent variable was the working environment. The collected data were analyzed by One-Way ANOVA, Pearson's correlation and stepwise multiple analysis using the IBM SPSS(ver. 20.0) statistical package program. Results: The effecting factors for cardiovascular disease were age, working shift and emotional state. The effecting factors for anxiety and depression were years of education, working condition, duties, and emotional state. The effecting factors of insomnia were duty and emotional state. The positive effecting factors for absent days were work standing, working shift, number of night shifts, autonomy, and duties. The positive effecting factors of subjective health status were age, work standing, working years, working shift, appropriateness of working hours, leadership of superiors, duties and emotional state. Conclusions: Based on the above results, the author considers that it is necessary to improve the working environment to reduce absent days, such as by reducing of number of night shifts and giving autonomy regarding duties, and to improve the working environment for subjective health status such as by controlling the appropriateness of working hours and stability of the emotional state. In addition, this study provides fundamental data on health promotion and occupational disease among workers.
CFIT 자율 회피를 위한 심층강화학습 기반 에이전트 연구
이용원,유재림 한국항공운항학회 2022 한국항공운항학회지 Vol.30 No.2
In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.