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이온빔으로 조사된 ITO 전극 표면이 유기 EL 소자성능에 미치는 영향
오재영,주진수,이천안,박병국,김동환,Oh, Jae-young,Joo, Jin-soo,Lee, Chun-An,Park, Byung-Gook,Kim, Dong-hwan 한국재료학회 2003 한국재료학회지 Vol.13 No.3
The influence of on ion beam irradiation to the indium tin oxide (ITO) substrate on the performance of the organic light-emitting diodes (OLEDs) was studied. ITO films were used as the transparent anode of OLEDs with poly(2-methoxy-5-(2'-ethylhexyloxy)-1,4-phenylenevinylene) (MEH-PPV) as a hole-injection/transport layer. Oxygen and argon plasma treatment of ITO resulted in a change in the work function and the chemical composition. For plasma treated ITO anodes, the device efficiency clearly correlated with the value of the work function. We also discussed the implications of our experimental study in relation to the modification of the ITO surface composition, transmittance, reflectance, and water contact angle (WCA).
국내 대학병원 공황장애 환자들의 임상적 특징에 대한 연구 : 다기관, 후향적 연구
오재영,이재헌,한상우,지익성,구본훈,우종민,양종철,김민숙,이상혁,허정윤,유범희,Oh, Jae-Young,Lee, Jae-Hon,Han, Sang-Woo,Chee, Ik-Seung,Koo, Bon Hoon,Woo, Jong Min,Yang, Jong-Chul,Gim, Min-Sook,Lee, Sang Hyuk,Heo, Jung-Yoon,Yu, Bum-Hee 대한불안의학회 2014 대한불안의학회지 Vol.10 No.1
Objective : Despite the high prevalence and clinical importance of panic disorder, studies on the clinical characteristics and course of panic disorder are relatively rare. This study is a multi-center, and retrospective study to examine the clinical characteristics and course of Korean panic disorder patients who visit university hospital. Methods : The study subjects were panic disorder patients who had visited the psychiatric outpatient clinics of 8 university hospitals in South Korea from January to December in 2008. Finally, 238 panic patients were included in this study. Their medical charts were retrospectively reviewed and reassessed by experienced psychiatrists to examine their clinical characteristics, demographic data and clinical course in repose to pharmacotherapy. Results : Among the 238 patients (121 males vs. 117 females), the mean age of disease onset was $41.3{\pm}12.7$ years and female patients showed 5 years older age of disease onset, compared with male patients. The mean score of PSR scale was $4.5{\pm}1.0$ at the first visit, reflecting a 'marked' level of severity of illness and impairment in functioning. Only 110 patients (46.4%) completed the whole follow up visits, whereas 128 patients (53.6%) dropped out during the treatment. After $17.7{\pm}0.5$ months of mean follow up period, the mean score of PSR scale at the last visit was reduced into $2.1{\pm}0.9$, reflecting a 'residual' severity of illness and impairment in functioning. The cumulative recovery rate was 62.1% in the completer group, whereas that of the drop-out group was 47.7%. Conclusions : The mean age of disease onset in Korean panic disorder patients who had visited university hospital was about 10 years older than that of Western panic disorder patients in previous studies, and the Korean panic disorder patients who had visited university hospital showed a relatively higher cumulative recovery rate. These differences might result from an ethnic difference in clinical characteristics and course in response to pharmacotherapy of panic disorder.
인공지능 해석 기법을 이용한 태양광 발전량 예측 성능 향상
오재영(Jae-Young Oh),이용건(Yong-Geon Lee),김기백(Gibak Kim) 대한전기학회 2020 전기학회논문지 Vol.69 No.7
Artificial intelligence (AI) has been effectively applied to various industries thanks to the increased availability of data and computing power. Advanced machine learning techniques also contribute to the widespread application of AI. However, it is becoming more difficult to interpret the AI implemented by advanced and highly complex machine learning algorithm. In this paper, for solar power forecasting system, we conduct SHAP value analysis which is one of the explainable AI techniques. We aim to improve the performance of the solar power forecasting by employing feature selection which is based on the feature importance computed by SHAP values. In the experimental results, three different machine learning algorithms (SVM, ANN, XGBoost) are applied for solar power forecasting and shown to improve the forecasting performance in all three methods.
XGBoost 기법을 이용한 단기 전력 수요 예측 및 하이퍼파라미터 변화에 따른 영향 분석
오재영(Jae-Young Oh),함도현(Do-Hyeon Ham),이용건(Yong-Geon Lee),김기백(Gibak Kim) 대한전기학회 2019 전기학회논문지 Vol.68 No.9
Accurate load forecasting is getting vital with social and economic development to secure electricity supply and minimize redundant electricity generation. The load forecasting is also essential for efficient power system operation. As machine learning techniques become popular due to the breakthroughs in the application of intelligent systems such as speech or image recognition, variety of machine learning algorithms have also been applied to predict electricity demand. For load forecasting, this paper employs XGBoost algorithm that has recently been receiving attention. To yield the maximum performance of the XGBoost model, we performed grid search method to find optimal hyperparameters of XGBoost. The effects of the XGBoost model’s hyperparameters on the model are assessed and visualized.