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鮮于 洋一,嚴慶一 東亞大學校 大學院 1981 大學院論文集 Vol.5 No.-
Isozymes and chromosomes of Kaloula borealis, hyla alborea japonica, and Bombina arientalis were studied by starch gel electrophoresis and bone marrow air-drying method. Twenty-three enzymatic proteins and enzymes of the three anuran species collected in Seoul province provided a basis for estimating the genetic distance among these species. On the genetic similariry, the relationship between Bombina orientalis and Kaloula alborea was nearer than the others. The chromosome number of Hyla alborea japonica and Bombina orientalis was 24, and Kaloula borealis was 28. The differences of these were a little.
조영복,선우창신,유의연 全南大學校 觸媒硏究所 2003 觸媒硏究 論文集 Vol.24 No.-
A study on the carbon deposition of nickel catalysts for carbon dioxide reforming by methane was performed. The experiments were carried out at atmospheric pressure using a fixed bed flow reactor. The nickel catalysts for the carbon dioxide reforming were compared concerning the influence of promoter on catalytic activity and carbon deposition. The coke deposition on the promoted nickel catalysts was remarkably diminished with the addition of promoters such as K, Mn and Sm. The activitie of the promoted nickel catalysts decreased in the order Ni-Li> Ni-Mg > Ni-Ca > Ni > Ni-K > Ni-Na> Ni-Cs. The carbon deposition of the promoted nickel nickel catalysts decreased in the order Ni-Mg> Ni-Li> Ni> Ni-Ne> Ni-K> Ni-Cs. In the Ni-K/α-Al_2O_3, its activity was maintained over 70 hours at 700℃ without deactivation.
이산화탄소에 의한 메탄 개질반응에서 니켈계 담지촉매의 탄소 침적에 관한 연구
조영복,선우창신,김상채,유의연 全南大學校 觸媒硏究所 1998 觸媒硏究 論文集 Vol.20 No.-
The reforming of methane by carbon dioxide is studied over nickel based catalysts, which compared the influence of the support in catalyst activity and carbon deposition. The experiments were carried out at atmospheric pressure using a fixed bad flow reactor. The catalytic activity and carbon deposition is influenced by support. The Ni/α-Al₂O₃showed high methane conversion. The Ni/La₂O₃ showed high methane conversion, but the reactor was plugged in 15 h. The carbon deposition was observed in all nickel based catalysts. Compared to Ni/α-Al₂O₃, Ni/La₂O₃, catalyst showed higher whisker carbon formation. Whisker carbon was not observed in the Ni/MnO₂. The carbon deposition of the nickel based catalysts increased in the order MnO₂〈SiO₂〈α-Al₂O₃〈TiO₂〈La₂O₃. The carbon deposition on the catalysts was affected by reaction conditions such as temperature, molar ratio(CO₂/CH₄).
Ⅷ족 전이금속 촉매를 이용한 메탄에 의한 이산화탄소의 개질반응
조영복,유의연,선우창신 全南大學校 觸媒硏究所 1997 觸媒硏究 論文集 Vol.19 No.-
Carbone dioxide reforming by methane was studied using a fixed bed reactor under an atmosphere. Transition metal catalysts and noble metals supported on α - AI₂O₃were compared. The order of activity was Ni> Pd> Co. The effects of metal additives on Ni/α - AI₂O₃were investigated for activated catalysts. The order of activity was Co > Cu > Fe. The catalyst performance depend on the CO₂/CH₄ratio and the reaction temperature.
Self-Imitation Learning을 이용한 개선된 Deep Q-Network 알고리즘
선우영민(Yung-Min Sunwoo),이원창(Won-Chang Lee) 한국전기전자학회 2021 전기전자학회논문지 Vol.25 No.4
Self-Imitation Learning은 간단한 비활성 정책 actor-critic 알고리즘으로써 에이전트가 과거의 좋은 경험을 활용하여 최적의 정책을 찾을 수 있도록 해준다. 그리고 actor-critic 구조를 갖는 강화학습 알고리즘에 결합되어 다양한 환경들에서 알고리즘의 상당한 개선을 보여주었다. 하지만 Self-Imitation Learning이 강화학습에 큰 도움을 준다고 하더라도 그 적용 분야는 actor-critic architecture를 가지는 강화학습 알고리즘으로 제한되어 있다. 본 논문에서 Self-Imitation Learning의 알고리즘을 가치 기반 강화학습 알고리즘인 DQN에 적용하는 방법을 제안하고, Self-Imitation Learning이 적용된 DQN 알고리즘의 학습을 다양한 환경에서 진행한다. 아울러 그 결과를 기존의 결과와 비교함으로써 Self-Imitation Leaning이 DQN에도 적용될 수 있으며 DQN의 성능을 개선할 수 있음을 보인다. Self-Imitation Learning is a simple off-policy actor-critic algorithm that makes an agent find an optimal policy by using past good experiences. In case that Self-Imitation Learning is combined with reinforcement learning algorithms that have actor-critic architecture, it shows performance improvement in various game environments. However, its applications are limited to reinforcement learning algorithms that have actor-critic architecture. In this paper, we propose a method of applying Self-Imitation Learning to Deep Q-Network which is a value-based deep reinforcement learning algorithm and train it in various game environments. We also show that Self-Imitation Learning can be applied to Deep Q-Network to improve the performance of Deep Q-Network by comparing the proposed algorithm and ordinary Deep Q-Network training results.
심층 강화학습을 이용한 모바일 로봇의 맵 기반 장애물 회피 알고리즘
선우영민(Yung-Min Sunwoo),이원창(Won-Chang Lee) 한국전기전자학회 2021 전기전자학회논문지 Vol.25 No.2
심층 강화학습은 학습자가 가공되지 않은 고차원의 입력 데이터를 기반으로 최적의 행동을 선택할 수 있게 하는 인공지능 알고리즘이며, 이를 이용하여 장애물들이 존재하는 환경에서 모바일 로봇의 최적 이동 경로를 생성하는 연구가 많이 진행되었다. 본 논문에서는 복잡한 주변 환경의 이미지로부터 모바일 로봇의 이동 경로를 생성하기 위하여 우선 순위 경험 재사용(Prioritized Experience Replay)을 사용하는 Dueling Double DQN(D3QN) 알고리즘을 선택하였다. 가상의 환경은 로봇 시뮬레이터인 Webots를 사용하여 구현하였고, 시뮬레이션을 통해 모바일 로봇이 실시간으로 장애물의 위치를 파악하고 회피하여 목표 지점에 도달하는 것을 확인하였다. Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.