http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
비백금 연료전지 촉매로서의 Co/PANi/CNT 합성 및 특성
이효준 ( Hyo June Lee ),안지은 ( Ji Eun Ahn ),김현종 ( Hun Jong Kim ),한명근 ( M. K. Han ),김한성 ( Hang Sung Kim ),이헌우 ( H. W. Lee ) 한국공업화학회 2011 응용화학 Vol.15 No.1
Platinum catalyst activity and stability is excellent in terms of fuel cells as a catalyst here. Although it is widely used, to compensate for the high price issue non-precious fuel cell catalysts are being developed. In this study, Co/PANi/CNT composite and non-precious as a catalyst for oxygen reduction was applied. Polyaniline on the interaction between cobalt and the oxygen reduction reaction and the structural characteristics observed in the impact and heat treatment was carried out according to the improved catalytic performance. Potential range is oxygen reduction reaction 0.55 V to 0.78 V(vs. NHE) after pyrolysis. Through this study, Co /PANi/CNT composites as a potential catalyst for fuel cells were non-precious.
항만 물류 환경에서 다중 에이전트 강화학습 기반 최적 배차 모델링 방법
이효준(Hyo-june Lee),장우석(Woo-seok Jang),이성진(Seong-Jin Lee),김동규(Dong-gyu Kim) 한국정보기술학회 2022 Proceedings of KIIT Conference Vol.2022 No.12
항만 물류 환경에서 배차 업무원은 화주가 등록한 컨테이너 화물을 화물 운송 기사에게 매칭하는 배차 업무를 진행한다. 이 때, 최대 효율을 위해서는 컨테이너 운송기사의 위치 및 컨테이너의 상차 또는 하차 위치를 고려하여 배차 업무를 수행함으로서 운송거리를 최소화 하는 것이 요구된다. 본 논문에서는 운송 효율을 최대화 하는 강화학습 기반 최적 배차 모델링 방법을 제안하고 그 분석 결과를 제시한다. In a port logistics environment, dispatchers carry out dispatching tasks that match container registered by shippers with truck drivers. At this time, for maximum efficiency, it is required to minimize the transportation distance by performing the dispatching task in consideration of the location of the container transport driver and the loading or unloading location of the container. In this paper, we propose an optimal vehicle dispatch modeling method based on reinforcement learning that maximizes transportation efficiency and present the analysis results.
전도성 고분자를 이용한 연료전지용 비백금 촉매의 특성화 정량
김현종 ( Hun Jong Kim ),이효준 ( Hyo June Lee ),안지은 ( Ji Eun Ahn ),김한성 ( Han Sung Kim ),이호년 ( Ho Nyun Lee ) 한국공업화학회 2011 응용화학 Vol.15 No.2
Excellent active and stable platinum catalyst fuel cells currently being used as a catalyst. However, because of the high price of platinum catalyst, such as non-precious catalyst has been studied by a variety of fuel cell catalysts. In this study, Co/ PANi//CNT composite catalyst after synthesis through various heating process was to increase the activity of the catalyst. At 700℃ showed the best catalytic activity, using a composite catalyst was to be used as cathode electrodes in fuel cell.
항만 물류 환경에서 강화학습 기반 컨테이너 다단 적재 모델링 방법
장우석(Woo-seok Jang),이효준(Hyo-june Lee),서민성(Min-Seong Seo),이성진(Seong-Jin Lee),김동규(Dong-gyu Kim) 한국정보기술학회 2022 Proceedings of KIIT Conference Vol.2022 No.12
항만 물류 환경에서 한정된 공간에 최대한 많은 컨테이너를 적재하기 위하여 컨테이너 다단 적재를 하고있다. 다단 적재 상황에서 저단 화물을 취급하려면 상단에 있는 화물을 다른 장소로 이동시켜야 한다. 이러한 과정을 컨테이너 리 핸들링이라 하며, 리 핸들링 과정에서 비용이 소모되므로 컨테이너 장치장 운영사는 리 핸들링을 최소화하고자 한다. 본 논문에서는 이러한 리 핸들링을 최소화하여 컨테이너를 다단 적재하도록 적재 위치를 추천, 선택하는 강화학습 모델을 제안하고 그 분석 결과를 제시한다. In port logistics, container multi-stage loading is used to load as many containers as possible in a limited space. In order to handle lower cargo in a multi-level loading situation, the upper cargo must be moved to another location. This process is called container re-handling, and since cost is consumed in the re-handling process, the container yard operator wants to minimize re-handling. In this paper, we propose a reinforcement learning model that recommends and selects a loading location to stack containers in multiple stages by minimizing such re-handling, and presents the analysis results.
항만 물류 환경에서 강화학습 기반 컨테이너 다단 적재 모델링 방법
장우석(Woo-Seok Jang),이효준(Hyo-June Lee),서민성(Min-Seoung Seo),이성진(Seong-Jin Lee),김동규(Dong-Gyu Kim) 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.4
In a port logistics environment, containers are stacked in multiple layers to maximize the use of limited space. To handle the lower-layer cargo in a multi-layer stacking situation, the cargo stacked on top must be moved to another location. This process is called container re-handling, and as it incurs costs, container terminal operators aim to minimize re-handling. This paper proposes a reinforcement learning model that recommends and selects loading positions to minimize re-handling while stacking containers in multiple layers. The proposed reinforcement learning model optimizes the loading order by considering the yard departure date of the containers, ensuring that the containers in the lower layer do not depart before those in the upper layer. This can reduce logistics costs and time during the container storage process.