• 한국과학교육학회지
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• 제32권8호
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• pp.1345-1355
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• 2012

In this study, 15 evaluation questions about light were developed and presented to 30 middle school students, and then the theory of knowledge space was used to analyze the status of the middle school students' knowledge about light. Not only was the state of the students' knowledge about light intended to be measured by schematizing the knowledge hierarchy obtained; the data obtained were also intended to be used as basic materials to improve the teaching methods used. To achieve the purpose of this study, the evaluation results, the individual knowledge state, and the hierarchy of questions were analyzed. As a result, different knowledge structures were found in the individuals and groups, and it was determined that individuals and groups should be diagnosed differently. In addition, the implication that there is a connection between each question and the individual knowledge state was drawn.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.29-32
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• 2007

This paper describes a multimodal dialog system that uses Hidden Information State (HIS) method to manage the human-machine dialog. HIS dialog manager is a variation of classic partially observable Markov decision process (POMDP), which provides one of the stochastic dialog modeling frameworks. Because dialog modeling using conventional POMDP requires very large size of state space, it has been hard to apply POMDP to the real domain of dialog system. In HIS dialog manager, system groups the belief states to reduce the size of state space, so that HIS dialog manager can be used in real world domain of dialog system. We adapted this HIS method to Smart-home domain multimodal dialog system.

• 한국환경과학회지
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• 제4권1호
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• pp.71-80
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• 1995

생화학적 산소요구량(BOD) 및 용존 산소(DO)을 이용하여 여러구간이 있는 강에 대한 이산 상태공간모델은 설정하였다. 상호작용 예측방법을 이용하여, 상태변수에 시간지연이 존재하는 대규모 시스템에 적용가능한 계층적 최적화 방법을 기술하였다. 정상상태 오차를 해석적으로 구하고, 상수 목표티 추적문제에 있어서 정상상태 오차가 발생하지 않을 필요충분조겆을 규명하였다. 수질오염 모델에 대한 컴퓨터 모사를 통하여 기술한 알고리듬의 타당성을 확인하였다.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2000년도 한국우주과학회보 제9권2호
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• pp.19-19
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• 2000

No Abstract, See Full Text

• 항공우주정책ㆍ법학회지
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• 제10권
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• pp.349-374
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• 1998

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2011년도 한국우주과학회보 제20권1호
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• pp.31.1-31.1
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• 2011

In this study, we investigated alignment state estimation performances of the three methods i.e. merit function regression (MFR), differential wavefront sampling (DWS) and Multiple Design Configuration Optimization (MDCO). The three target optical systems are 1) a two-mirror Cassegrain system for deep space Earth observation, 2) intermediate size three-mirror anastigmat for Earth ocean monitoring, and 3) extremely large segmented optical system for astronomical observation. We ran alignment state estimation simulation for several alignment perturbation cases including 1mm to 10mm in decenter and from 0.1 to 1 degree in tilt perturbation error for the two-mirror Cassegrain system. In general, we note that MDCO shows more competitive estimation performance than MFR and DWS. The computational concept, case definition and the simulation results are discussed with implications to future works.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.408-411
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• 1995

In this paper, we discuss the force control of flexible manipulators. Since the force control of flexible manipulators with planar one or two links using the distributed-parameter modeling has been the subject of a considerable number of publications until now, real time computations of the force control schemes are possible. But, application of those control schemes to multi-link spatial manipulators is fairly complicated. In this paper, we apply a concise hybrid position/force control scheme for a flexible manipulators. We use a lumped-parameter modeling for the flexible manipulators. The Hamilton's principle is applied to derive the equations of motion for the system and then, state-space model is obtained by the Lagrange's method. Finally, comparison of simulation results with experimental results is given to show the performance of our method.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1870-1873
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• 2004

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.