• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• 센서학회지
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• 제28권2호
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• pp.88-93
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• 2019

This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.

• 전기의세계
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• 제22권2호
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• pp.28-32
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• 1973

System modeling is applied in developing a probabilistic linear estimator for the load of an electric power system for the purpose of short term load forecasting. The model assumer that the load in given by the suns of a periodic discrete time serier with a period of 24 hour and a residual term such that the output of a discrete time dynamical linear system driven by a white random process and a deterministic input. And also we have established the main forecasting algorithms, which are essemtally the Kalman filter-predictor equations.

• 한국음향학회지
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• 제14권3호
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• pp.90-96
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• 1995

잡음섞인 관측데이타로부터 AR 모수를 추정하는 방법을 제안하였다. AP 방법이라고 이름붙인 이 방법은 단순하고도 신뢰성있는 적응예측기를 이용하려는 시도의 산물이다. 잡음섞인 입력수열로부터 계산된 AR 모수의 추정치보다 예측수열로부터 계산된 AR 모수의 추정치가 원래의 모수에 스펙트럼상의 거리가 더 가깝다는 것을 이론적으로 증명하였다. 실제 음성 신호와 칼만필터를 사용한 실험결과도 이론과 일치함을 보였다. 대략적으로, AP방법으로 계산된 추정치를 사용하였을때의 잡음감쇠성능은 잡음섞인 입력수열로부터 계산된 AP 모수의 추정치를 사용하였을때보다는 우수하였고, EM반복법에 의한 추정치를 사용하였을때보다는 약간 못한 것으로 나타났다. 그러나, 제안된 방법은 그 단순성으로 인하여 경우에 따라 더 복잡한 다른 방법의 대안으로 사용될 수 있을 것이다.

• 한국항행학회논문지
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• 제3권2호
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• pp.156-166
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• 1999

다중차량 추적시스템에서 칼만 필터는 차량을 추적하기 위하여 일반적으로 사용되는 예측 알고리듬이다. 칼만 필터는 제한된 조건에서 최적의 결과를 나타내는 좋은 특성이 있으나 계산량이 많아 다수의 차량을 실시간으로 추적해야 하는 다중차량 추적시스템에서의 구현은 다소 어려운 단점이 있다. 본 논문에서는 실시간 다중차량 추적시스템의 구현을 위해 비교적 계산이 간단한 순환최소자승 알고리듬을 횡구조의 필터에 적용한 적응 예측기를 도입한다. 칼만 필터를 이용한 추적시스템과 성능을 비교 분석하기 위하여 컴퓨터 그래픽 도구로 제작된 가상 연속영상과 실제 교차로에서 촬영한 동영상을 이용하였다. 모의실험 결과는 본 논문에서 제안한 다중차량 추적시스템이 전용하드웨어 없이 일반 개인용 컴퓨터 환경 하에서 초당 30프레임의 속도로 촬영한 영상의 차량을 실시간으로 추적하는데 사용될 수 있음을 보여준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.103-106
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• 2003

In this paper, we present a hybrid inertial and vision-based tracking system for VR applications. One of the most important aspects of VR (Virtual Reality) is providing a correspondence between the physical and virtual world. As a result, accurate and real-time tracking of an object's position and orientation is a prerequisite for many applications in the Virtual Environments. Pure vision-based tracking has low jitter and high accuracy but cannot guarantee real-time pose recovery under all circumstances. Pure inertial tracking has high update rates and full 6DOF recovery but lacks long-term stability due to sensor noise. In order to overcome the individual drawbacks and to build better tracking system, we introduce the fusion of vision-based and inertial tracking. Sensor fusion makes the proposal tracking system robust, fast, accurate, and low jitter and noise. Hybrid tracking is implemented with Kalman Filter that operates in a predictor-corrector manner. Combining bluetooth serial communication module gives the system a full mobility and makes the system affordable, lightweight energy-efficient. and practical. Full 6DOF recovery and the full mobility of proposal system enable the user to interact with mobile device like PDA and provide the user with natural interface.