• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.78-79
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• 2014

This study is concerned with the VS shear reinforcement that it can be installed easily in filed as product at the factory and seismic performance can be achieved. The method of study is as follows. first, we researched constructability and economy of existing construction method. Secondly, we made specimen and were examined structural performance tests in order to verify the performance of the shear reinforcement for lateral force like seismic load. As a result, developed VS shear reinforcement increased in shear strength and stiffness of reinforcement, structural safety is judged to be increased.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 춘계 학술논문 발표대회
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• pp.173-174
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• 2012

This study is concerned with the VSTUD shear reinforcement that it can be installed easily in filed as product at the factory and seismic performance can be achieved. The method of study is as follows. first, we researched constructability and economy of existing method. Secondly, we made specimen and were examined structural performance tests in order to verify the performance of the shear reinforcement. As a result, developed SL shear reinforcement increased in shear strength and stiffness of reinforcement, structural safety is judged to be increased.

• 대한조선학회지
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• 제19권4호
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• pp.11-18
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• 1982

When the perforated plate is under in-plane load of compression, buckling analysis becomes to be necessary because of the presence of stress concentration around holes. To constraint it, we need reinforcement. The methods of reinforcement are attaching doubler around hole and attaching stiffener in the direction of initial stress. In this paper, two methods are investigated mentioned above, which of the two better effective reinforcement. In the consequence of the above investigation, following conclusion was obtained. The method of doubler reinforcement was less buckling stress than that of stiffener because the former had large compressive stress. So, effective method of reinforcement is stiffener reinforcement.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.13-16
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• 2001

In reinforcement teaming, Q-learning converges quite slowly to a good policy. Its because searching for the goal state takes very long time in a large stochastic domain. So I propose the speedup method using the Q-value initialization for model-free reinforcement learning. In the speedup method, it learns a naive model of a domain and makes boundaries around the goal state. By using these boundaries, it assigns the initial Q-values to the state-action pairs and does Q-learning with the initial Q-values. The initial Q-values guide the agent to the goal state in the early states of learning, so that Q-teaming updates Q-values efficiently. Therefore it saves exploration time to search for the goal state and has better performance than Q-learning. 1 present Speedup Q-learning algorithm to implement the speedup method. This algorithm is evaluated. in a grid-world domain and compared to Q-teaming.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1072-1075
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• 2005

This paper presents that TD method is applied to the human adaptive devices for smart home with context awareness (or recognition) technique. For smart home, the very important problem is how the appliances (or devices) can adapt to user. Since there are many humans to manage home appliances (or devices), managing the appliances automatically is difficult. Moreover, making the users be satisfied by the automatically managed devices is much more difficult. In order to do so, we can use several methods, fuzzy controller, neural network, reinforcement learning, etc. Though the some methods could be used, in this case (in dynamic environment), reinforcement learning is appropriate. Among some reinforcement learning methods, we select the Temporal Difference learning method as a core algorithm for adapting the devices to user. Since this paper assumes the environment is a smart home, we simply explained about the context awareness. Also, we treated with the TD method briefly and implement an example by VC++. Thereafter, we dealt with how the devices can be applied to this problem.

• 예술인문사회 융합 멀티미디어 논문지
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• 제8권2호
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• pp.351-358
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• 2018

강화 학습은 변화하는 환경에서의 최적의 보상을 얻을 수 있는 행동을 결정하기 위한 정책을 얻는 기계 학습 기법이다. 하지만 기존에 연구되어 온 강화 학습은 불확실하고 연속적인 실제 환경에서 최적의 행동을 얻기 위해 발생되는 높은 계산 복잡도 문제와 학습된 결과를 얻기 위해서는 많은 시간이 소요 된다는 문제점을 가지고 있다. 앞에서 언급한 문제를 해결하기 위해, 높은 계산 복잡도 문제를 해결을 위해서는 강화 학습을 구성하는 가치 함수와 정책을 독립적으로 구성하는 AC(actor-critic) 기법이 제안되었다. 그리고 빠른 학습 결과를 얻기 위해 기 학습된 지식을 새로운 환경에서 이용하여 기존 학습보다 빠르게 학습 결과를 얻을 수 있는 전이 학습(transfer learning) 기법이 제안되었다. 본 논문에서는 기존에 연구되어 왔던 기계 학습 기법의 향상 기법인 AC 기법과 전이 학습 기법에 대해 소개하고, 이를 무선 액세스 네트워크 환경에서 기지국 상태 조정을 위해 적용되고 있는 사례를 소개한다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2011년도 춘계 학술논문 발표대회 2부
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• pp.53-56
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• 2011

This study is concerned with the SL shear reinforcement that it can be installed easily in filed as product at the factory and seismic performance can be achieved. The method of study is as follows. first, we researched constructability and economy of existing method. Secondly, we made specimen and were examined structural performance tests in order to verify the performance of the shear reinforcement. Shear strength of HILL01-HILL03 specimen applied to SL shear reinforcement increased about 5-14% when compared with the applied shear stirrup reinforcing existing specimens. Also, the amount of the maximum deflection of the central sub-section of HILL01-HILL03 specimen applied to SL shear reinforcement decreased about 41-42% when compared with the applied shear stirrup reinforcing existing specimens. As a result, developed SL shear reinforcement increased in shear strength and stiffness of reinforcement, structural safety is judged to be increased.

• 터널과지하공간
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• 제14권4호
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• pp.275-286
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• 2004

본 논문에서는 신규 터널노선과 교차하는 폐터널의 보강방식을 결정하기 위하여 의사결정기법을 검토하였고, 여러 가지 의사결정기법들 중에서 설문조사의 과정을 최소화 하고, 조사항목 별 정성적ㆍ정량적 특성을 모두 반영할 수 있도록 쌍대비교와 퍼지근사추론을 이용하여 폐터널의 보강방식에 대한 적정성을 평가하여 보았다. 페터널 보강방식을 선정하기 위하여 4개의 주 요인들 즉, 시공성, 경제성, 안전성, 유지관리성을 평가의 수단으로 사용하였고, 간단한 설문조사와 쌍대비교행렬을 이용하여 4가지 주 요인들의 가중치를 결정하였다. 퍼지근사추론은 4개의 주 요인별 평가점수를 산정 하는데 사용되어졌고, 이 결과들에 가중치를 반영하여 최종적인 폐터널의 보강방식을 선정할 수 있었다.

• 한국터널지하공간학회 논문집
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• 제24권6호
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• pp.495-512
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• 2022

본 연구에서는 프리스트레스와 그라우팅을 이용한 터널 필라부 보강공법에 대해 검토하였다. 병설터널의 문제점을 보완할 수 있는 보강법들은 다양하지만 프리스트레스와 그라우팅을 이용한 터널 필라부 공법이 현장적용성, 안정성, 경제성에서 우수하다고 판단됨에 따라 실질적인 거동 메커니즘의 이론적 검토 및 수치해석적 검토가 필요하기 때문에 축소모형 실험을 진행하였다. 축소모형 실험은 PC강연선 + 강관보강 그라우팅 + 프리스트레스(Case 1), PC강연선 + 강관보강 그라우팅(Case 2), 무보강(Case 3)으로 나누어 필라부의 변위와 벽체에 가해지는 토압을 측정하였다. 실험을 통하여 여러 공법 중 PC강연선 + 강관보강 그라우팅 + 프리스트레스 공법은 가장 우수한 보강공법임을 확인하였고 추후 현장실험을 통해 이를 검증하고 보완해 나간다면 현재 적용되는 보강공법보다 변위 제어 및 부재력 측면에서 우수한 결과를 도출해 낼 수 있을 것이라 판단되었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.138-141
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• 2003

Recently reinforcement learning has attracted attention of many researchers because of its simple and flexible learning ability for any environments. And so far many reinforcement learning methods have been proposed such as Q-learning, actor-critic, stochastic gradient ascent method and so on. The reinforcement learning system is able to adapt to changes of the environment because of the mutual action with it. However when the environment changes periodically, it is not able to adapt to its change well. In this paper we propose the reinforcement learning system that is able to adapt to periodical changes of the environment by introducing the time-varying parameters to be adjusted. It is shown that the proposed method works well through the simulation study of the maze problem with aisle that opens and closes periodically, although the conventional method with constant parameters to be adjusted does not works well in such environment.