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경찰일탈 통제제도의 개선방안에 관한 연구-경찰관 인식조사를 중심으로-
홍태경(TaeKyung Hong) 한국부패학회 2011 한국부패학회보 Vol.16 No.1
The subjects of this study were police deviance preventing measures which were applied in the current police organization. In particular, the purpose of the study was to find the operating state of ethics education, vocational aptitude test, whistle-blowing, and inspect and their related problems, and to present improving measures for them. Based on the purpose, the survey of police officers found several problems from the current control system. First, police officers satisfaction on ethics education, vocational aptitude test, whistle-blowing, and inspection was generally low. Second, in particular, police officers showed a negative view of operating methods related with inspection. Third, there was a considerable gap between police officers' thinking measures and the current control system. The organization of police focused on deterrence measures such as inspect and disciplinary tightening, but police officers regarded the betterment of salary and welfare system, the improvement of solicitation culture, and the provision of correct standard of deviance behavior as important measures. Based on the above analysing results, the study proposed such improving measures as the strengthening and qualitative improvement of ethics education, the positive application of vocational aptitude test, the enhancement of guaranteeing anonymity of whistle-blowing, the movement of preventive inspect, the improvement of solicitation culture, the provision of correct standard of deviance behavior and the consistency of punishment, and so forth.
코로나19 확진자 수 예측을 위한 딥러닝 모델: 확산 패러다임 변화를 반영한 입력변수 조정
홍태경(Taekyung Hong),김은서(Eunseo Kim),이희상(Heesang Lee) 대한산업공학회 2023 대한산업공학회지 Vol.49 No.2
It is difficult to predict with traditional approaches since the number of confirmed cases of COVID-19 fluctuates significantly. Therefore, this study tried to predict it by adopting machine learning models, such as Support Vector Regression (SVR), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two experiments were performed with different prediction periods using two input data sets. The first input set consists of confirmed cases, severe cases, deaths, immunization cases, social distancing, and diffusion of COVID-19 mutations. The second input set is reconstructed to account for novel COVID-19 mutations without social distancing. As a result, the best deep learning model was selected for each experiment. This study showed that the models input variables should be adjusted according to changes in the virus spread pattern. It also contributed to implementing policies to contain the COVID-19 spread.
국내 제약·바이오기업들의 제품 R&D/판매 활동이 주가에 미치는 영향: 코로나바이러스감염증-19 치료제 제조기업과 진단키트 제조기업에 대한 사건연구
황인탁(Intak Hwang),홍태경(Taekyung Hong),김은서(Eunseo Kim),이희상(Heesang Lee) 대한산업공학회 2022 대한산업공학회지 Vol.48 No.1
The market values of many biopharmaceutical firms are known to have increased due to the outbreaks of Coronavirus Disease 2019 (COVID-19). This paper used the “Event Study” methodology to examine press releases’ impact on the stock prices of remedy and diagnosis kits after the pandemic of COVID-19 in Korea. By analyzing 97 events of R&D and marketing activities of the remedy manufacturers and diagnosis kit manufacturers, remedy manufacturers had more significant positive effects than diagnosis kit manufacturers. Furthermore, both manufacturers had strong positive effects on early R&D and sales stages and early periods of event dates. We interpreted these results that biopharmaceutical firms’ development and marketing activities responding to COVID-19 had different impacts on the stock price value according to the level of technological advancement, R&D and sales stages, and the dates of the events. Thus, this paper can give biopharmaceutical firms effective R&D and marketing strategies in response to rapid environmental changes.