• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.261-266
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• 2002

In this Paper, we Propose model-based tuning rules of the PID controller incorporating with genetic algorithms. Three sets of optimal PID parameters for step set-point tracking are obtained based on the first-order time delay model of plants and a genetic algorithm which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are obtained using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• 대한교통학회지
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• 제23권7호
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• pp.137-148
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• 2005

일정구간의 도로를 주행하는 차량에 대한 단위시간대별 위치정보를 취득하게 되면, 도로의 교통상황에 대한 정보와 개별차량의 미시적인 주행행태를 파악할 수 있게 된다. 기존 사용되는 영상기술은 짧은 지점에 대한 교통량, 속도 등의 제한적인 자료만의 취득이 가능하여 도로구간의 교통상황을 대표하는데 한계가 있다. 또한 기존 영상기술은 주행차량의 미시적행태분석을 위해서 비디오로 촬영된 영상을 한 프레임씩 수동으로 작동하여 데이터를 수집함으로써 많은 인력과 시간이 소요되었다. 본 연구에서는 차량의 단위시간대별 위치자료를 자동으로 얻어낼 수 있는 규칙기반 차량추적기술을 개발하였다. 또한 기술의 검증을 위해 130m의 도로구간에서 차량의 주행위치를 0.05초 단위로 추적한 기초 자료를 추출하였으며, 이 데이터의 가공을 통해 산출된 속도와 실측된 속도와의 비교를 통해 차량추적의 정확도를 검증하였다. 향후 이러한 차량추적기술은 도로의 교통상황에 대한 주요 정보의 제공 등의 실용적 측면과 차량의 주행행태 분석 등의 학문적 분야에 널리 활용될 수 있을 것이다.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.539-544
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• 2004

본 논문에서는 CONDENSATION 알고리즘을 이용하여 입자 필터(particle filter)에 기반 한 물체 추적 알고리즘을 제안한다. 입자 필터는 조건 확률 전파 모델(Conditional Density Propagation)인 베이지안(Bayesian) 추론 규칙을 적용하는 추적구조를 갖고 있기 때문에 다른 어떤 종류의 추적 알고리즘보다 뛰어난 성능을 보인다. 논문에서는 실험 결과를 통해, 외곽(contour) 추적 입자 필터가 복잡한 환경 속에서 강인한 추적 성능을 나타냄을 증명한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.296-296
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• 2000

This paper describes the features of a transmission line and a wiring, and a design rule based on a demanded condition for a wiring. Like as the simulation of a circuit, by tracking the wiring path among parts that are disposed on PCB, we analyze the feature of the corresponding wiring using the design formula and rule. We implement a signal integrity simulator, which is capable of electrical and electronic simulation for the feature of a wiring signal and the corresponding signal, and the results are demonstrated.

• 한국정밀공학회지
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• 제20권6호
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.239-239
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• 2000

This paper present experimental results on temperature trajectory tracking. The benefits of precalculated feedforward input together with PID feedback control are demonstrated by experimental results. To find the feedforward input, the plant (autoregresiive) model is first identified and convex optimization procedure is applied. PID controller is then implemented based on Ziegler-Nickels tuning rule to reduce effects of disturbances and modeling errors. Experimental results show an improvement in slope tracking performance over the fully PID controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.192-194
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• 1995

An improved filter for tracking a maneuvering target is presented. The proposed filter consists of two kalman filters based on different dynamic models and double decision logic. The use of double decision logic for the maneuver onset and ending detection leads to reduction in estimation error. This decision rule is based on evidence theory, Dempster-Shafer theory, which is extended in order to be applicable in the tracking problem. Simulation results show that the proposed filter performs better than IMM at a lower computational load.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.