• 제목/요약/키워드: State-Space

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Hidden Information State 대화 관리자를 이용한 멀티모달 대화시스템 (Multimodal Dialog System Using Hidden Information State Dialog Manager)

  • 김경덕;이근배
    • 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.

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계층적 최적화 기법을 이용한 강의 수질오염 제어 (River Pollution Control Using Hierarchical Optimization Technique)

  • 김경연;감상규
    • 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.

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New Advances in Gravitational Microlensing

  • Gould, Andrew
    • 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

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Alignment estimation performance of Multiple Design Configuration Optimization for three optical systems

  • Oh, Eun-Song;Kim, Seong-Hui;Kim, Yun-Jong;Lee, Han-Shin;Kim, Sug-Whan
    • 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.

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Hybrid position/force control of flexible manipulators

  • Kim, Jin-Soo;Suzuki, Kuniaki;Konno, Atsushi;Uchiyama, Masaru
    • 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.

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상태공간 접근법에 의한 화학공정의 자동합성 (Automatic Synthesis of Chemical Processes by a State Space Approach)

  • 최수형
    • 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.

A State Space Modeling and Evolutionary Programming Approach to Automatic Synthesis of Chemical Processes

  • Choi, Soo-Hyoung;Lee, Bom-Sock;Chung, Chang-Bock
    • 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.

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Actor-Critic Algorithm with Transition Cost Estimation

  • Sergey, Denisov;Lee, Jee-Hyong
    • 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.

부분공간법을 이용한 연속 냉간압연기의 상태공간모델 규명 (State-Space Model Identification of Tandem Cold Mill Based on Subspace Method)

  • 김인수;황이철;이만형
    • 대한기계학회논문집A
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    • 제24권2호
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    • pp.290-302
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    • 2000
  • In this paper, we study on the identification of discrete-time state-space model for robust control of tandem cold mill, using a MOESP(MIMO output-error state-space model identification) algorithm based on subspace method. It is shown that the identified model is well adapted to input-output data sets, which are obtained from nonlinear mathematical equations of tandem cold mill. Furthermore, deterministic H$\infty$ norm bounds on uncertainties including modeling errors and disturbances are quantitatively identified in the frequency domain. Finally, the results give a basic idea to determine weighting functions included in formulating some robust control problems of tandem cold mill.