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Poloxamer-407로 유도한 고지혈증 동물모델에서 Quercetin-3-O-rhamnoside 및 어성초 분획물의 항고지혈증 효과
김도국,김세건,암릿파우델,정태숙,정현주 한국생약학회 2012 생약학회지 Vol.43 No.2
The anti-hyperlipidemic effect of Houttuynia cordata was assessed in poloxamer-407 induced hyperlipidemic mice model. The butanol fraction and its isolated compound, quercetin-3-O-rhamnoside, significantly reduced the blood triglyceride and total-cholesterol level and increased the blood HDL-cholesterol level. They also showed the significant reductive effect on the blood AST and ALT level, rising in proportion to the liver damage, in hyperlipidemic mice.
신광훈,김도국 한국정보과학회 2023 정보과학회논문지 Vol.50 No.8
Recently, time series data are being generated in various industries with advancement of the Internet of Things (IoT). Accordingly, demands for time series forecasting in various industries are increasing. With acquisition of a large amount of time-series data, studies on traditional statistical method based time-series forecasting and deep learning-based forecasting methods have become active and the need for data augmentation techniques has emerged. In this paper, we proposed a novel data augmentation method for time series forecasting based on adversarial training. Unlike conventional adversarial training, the proposed method could fix the hyperparameter about the number of adversarial training iterations and utilize blockwise clipping of perturbations. We carried out various experiments to verify the performance of the proposed method. As a result, we were able to confirm that the proposed method had consistent performance improvement effect on various datasets. In addition, unlike conventional adversarial training, the necessity of blockwise clipping and the hyperparameter value fixing proposed in this paper were also verified through comparative experiments.
김지은,김도국,구민석,김건희,권미영 대한의사협회 2015 대한의사협회지 Vol.58 No.12
The health and welfare of North Korean defectors is a rising interest as a large number of North Korean defectors are currently living in South Korea. Due to shortage of food provisions, intensive physical labor oriented lifestyle and inadequate medical service system, the medical environment and disease distribution is very different between North and South Korea. Furthermore the physical and mental hardships during the escape from North Korea and the difficulty of adjusting to a new society may all contribute to the health status of North Korean defectors. Recently many health concerns of North Korean defectors have been a social issue in the Korean society. There have been studies and statistics on the mental illnesses of the defectors due to the sufferings during the escape and the difficulty in adjusting into a new environment but there have been no information on the surgical aspects of the defectors. Analyzing the underlying diseases and the incidences of surgery may prepare for an improved understanding in patient care of North Korean defectors
에피소드 랜덤화 및 액션 노이즈를 통한 강화학습 기반의 포트폴리오 최적화 성능 향상
우세형,김도국 한국정보과학회 2024 정보과학회논문지 Vol.51 No.4
포트폴리오 최적화는 투자 관리 위험을 감소시키고 수익을 극대화하기 위해 필수적이다. 최근 인공 지능 기술이 급격히 발달하면서 다양한 분야에서 이를 활용하기 위해 연구 중이며, 특히 금융 분야에서는 강화학습을 적용하기 위한 연구가 활발히 진행되고 있다. 그러나 대부분의 연구들이 과거 금융 데이터의 반복 학습으로 인한 에이전트 과적합 문제를 해결하지 못하고 있다. 이에 본 연구에서는 강화학습 기반의 포트폴리오 최적화에서 에피소드 랜덤화 및 액션 노이즈를 통해 과적합을 완화하는 기법을 제안한다. 제안된 기법은 에피소드마다 학습 데이터 기간을 랜덤화하여 다양한 시장 상황을 경험하게 함으로써 데이터 증폭의 효과와 액션 노이즈 기법을 활용하여 에이전트가 특정 상황에 대응할 수 있게 탐색을 촉진한다. 실험 결과 제안 기법을 적용하였을 때 기존 강화학습 에이전트보다 성능이 향상되었음을 확인할 수 있었으며 비교 실험을 통해 다양한 조건에서 제안하는 기법 모두 성능 향상에 기여하였음을 확인하였다. Portfolio optimization is essential to reduce investment management risk and maximize returns. With the rapid development of artificial intelligence technology in recent years, research is being conducted to utilize it in various fields, and in particular, investigation on the application of reinforcement learning in the financial sector. However, most studies do not address the problem of agent overfitting due to iterative training on historical financial data. In this study, we propose a technique to mitigate overfitting through episode randomization and action noise in reinforcement learning-based portfolio optimization. The proposed technique randomizes the duration of the training data in each episode to experience different market conditions, thus promoting the effectiveness of data augmentation and exploration by leveraging action noise techniques to allow the agent to respond to specific situations. Experimental results show that the proposed technique improves the performance of the existing reinforcement learning agent, and comparative experiments confirm that both techniques contribute to performance improvement under various conditions.
김지은,김도국,구민석,김건희,권미영 대한마취통증의학회 2016 Anesthesia and pain medicine Vol.11 No.1
We report an extremely rare case of right ventricle perforation by a Swan-Ganz catheter during open heart surgery. Even when pulmonary artery catheters are inserted with the utmost care, serious complications such as hematoma formation, pneumothorax, hemothorax, perforation of the cardiac chambers, and rupture of the pulmonary artery may occur. We present a case of primary closure of a right ventricle perforation discovered during coronary artery bypass graft surgery. In this case, the Swan-Ganz catheter was found penetrating the anterior wall of the right ventricle during the surgery. The location of the Swan-Ganz catheter, the stiffness of the catheter caused by hypothermia, and excessive surgical manipulation were supposed to be the etiologies. Therefore, the location of the Swan-Ganz catheter and increased stiffness from hypothermia should be taken into consideration during heart surgery.