• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 A
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• pp.376-378
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• 1996

This paper presents a new PFC control method which replaces a fast line current measurement with a filtered load current measurement. Using the power balance relation between the input and the output of the boost converter. the input current can be described as the function of load current. Thus the PWM signal which effects the switching control of the boost converter is generated using the PFC input voltage, the PFC output voltage and the load current as input variables. By using a filter between the bridge rectifier and a dc-to-dc converter, the input voltage of the dc-to-dc converter is forced to always maintain above zero volt. Then the input current traces a sinewave in phase. The proposed scheme accomplishes a very high power factor and a low harmonic distortion of the line current. The validity of this scheme is demonstrated through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1222-1227
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• 1990

The problem of identification of continuous systems is considered when both the discrete input and output measurements are contaminated by white noises. Using a predesigned digital low-pass filter, a discrete-time estimation model is constructed easily without direct approximations of system signal derivatives from sampled data. If the pass-band of the filter is designed so that it includes the main frequencies of both the system input and output signals in some range, the noise effects are sufficiently reduced, accurate estimates can be obtained by least squares(LS) algorithm in the presence of low measurement noises. Two classes of filters(infinite impulse response(IIR) filter and finite impulse response(FIR) filter) are employed. The former requires less computational burden and memory than the latter while the latter is suitable for the bias compensated least squares(BCLS) method, which compensates the bias of the LS estimate by the estimates of the input-output noise variances and thus yields unbiased estimates in the presence of high noises.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.955-959
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• 2011

In this paper, we assume that an output sensor of a ball and beam system is coupled with AC measurement noise. We propose an output feedback controller for a ball and beam system under measurement noise of feedback sensor. Measurement noise makes feedback signals distorted, and results in performance degradation or even system failure. Therefore, we need to design a robust controller to accommodate the possible measurement noise in the feedback information. Our controller is equipped with a gain-scaling factor to minimize the effect of measurement noise in output feedback information. We give an analysis of the controlled system and illustrate the improved control performance via simulation and experiment for a ball and beam system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.322-326
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• 1989

This paper describes design procedure of suboptimal control to minimize a performance index which is represented as sum of square output error and the heat input power in arc welding process. Heat input and temperature of a fixed point on the surface of the material are concerned as input and output of the process, repectively. The suboptimal control law considered here in is a proportional plus integral type and is implemented by using only the output variables available from sensor which is also optimally located in a fixed point w.r.t. a moving weld touch.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.892-895
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• 1996

An unknown input observer is proposed that can be used in wheelbase preview control of commercial vehicles. The preview and state information, required to calculate actuator force, are reconstructed from the measurement variables such as heave and pitch acceleration. Gain matrix of observer is optimally selected so that influence of system and measurement noises on the estimation error can be minimized. Estimated preview information requires low pass filtering to eliminate high frequency components resulting from differentiation of noisy output signals. Effectiveness of the proposed method is demonstrated by numerical simulation of half car model.

• 한국경영과학회지
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• 제23권3호
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• pp.75-90
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• 1998

The improper use of input and output factors in DEA has a critical and negative impact on the efficiency measurement and the discernment of decision making units(DMUs) : hence the proper selection Process of the factors should precede the actual applications of DEA. In this paper, we propose a new approach to selecting proper factors based on Tofallis' partial efficiency evaluation method(1996). With the approach, the factors aye clustered by measuring their respective partial efficiencies and analyzing the rank correlations of them. The method and procedure we propose in this paper are then applied to measure the efficiencies of the public libraries in Seoul District area, and the results show that the proposed approach can provide meaningful information to improve discernment of the DMUs while using less number of input factors (and less information). The proposed method can be effectively used in the situation where the number of the DMUs to be considered is relatively small compared to the number of available input and output factors, which usually lessens the power to identify the inefficient units in DEA.

• 대한전자공학회논문지
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• 제22권5호
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• pp.17-23
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• 1985

This paper describes a method for the design of a self-tuning controller of single-input/single-output systems with system noises and obsrrvation noises. The method uses state-space techniques to assign the closed-loop system poles to desired locations, but the control law is made up of process input and output measurement values, so that state estimation is unnecessary. Also the difficulties of tracking of reference inputs in state.space pole-placing control are tackled by including the reference input in the cost function proposed by Beger.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 A
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• pp.282-284
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• 1997

The harmonics are injurious on the operation of the motor, electric instruments etc. In this paper, it is certified the capability of the electric measuring devices at the input/output of inverter. And it is measured the harmonics of the voltage and current waveforms. Also, this paper presents the efficiency of the inverter's power conversion by measuring the fundamental and total power at the input, DC bus and output.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• 한국수자원학회논문집
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• 제55권spc1호
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• pp.1295-1303
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• 2022

안정적인 수도 공급을 위한 상수도관망의 역할이 더욱 주목받음에 따라 비정상 상황에 대한 신속한 탐지와 적절한 대처 역시 중요시되고 있다. 장치에 의존한 탐지기법 등 기존의 방법론에는 한계가 존재하므로 데이터를 이용한 모델 기반의 방법이 개발되었다. 하지만 상수도관망 내 측정 데이터는 불확실성을 가져 실제 사용량과 다르다. 따라서 본 연구에서는 기계학습 방법의 하나인 인공신경망 모델을 이용하여 상수도관망 압력값을 예측함에 있어 데이터 불확실성의 영향을 조사한다. 정규분포를 따르는 임의의 값을 고려하여 데이터에 측정치 오류를 형성하고 측정치 오류 여부 및 종류에 따라 총 9가지 데이터를 인공신경망 모델을 통해 예측해 경향성을 비교한다. 분석을 통해 데이터 불확실성이 증가할수록 모델 성능이 감소하며, 출력데이터의 측정치 오류가 모델 성능에 미치는 정도가 더 큼을 확인하였다. 특히 입력데이터와 출력데이터의 측정 오차 크기가 동일한 경우 예측 정확도는 각각 72.25%, 38.61%로 큰 차이를 보였다. 따라서 ANN 모델 예측 성능 향상을 위해서는 입력 데이터보다 출력데이터인 주절점의 측정 오류 크기를 줄이는 것이 중요하다.