• 대한전기학회논문지:전력기술부문A
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• 제48권11호
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• pp.1441-1447
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• 1999

A discrete time domain modeling is presented for the pulse-width modulated series resonant converter (PWM-SRC) with a discontinuous current mode. This nonlinear system is linearized about its equilibrium state to obtain a linear discrete time model for the investigation of small signal performances such as the stability and transient response. The usefulness of this small signal model is verified through the dynamic simulation.

• 전력전자학회논문지
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• 제10권3호
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• pp.219-225
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• 2005

본 논문에서는 평균전류모드제어를 이용하는 컨버터의 전류응답을 예측할 수 있는 새로운 이산시간 소신호 모델을 구한다. 평균전류모드제어는 최대전류모드제어와 달리 전류제어를 위해 복잡한 보상기 회로를 사용하므로 컨버터의 동작 특성 해석이 어렵다. 평균전류모드제어를 사용하는 컨버터의 소신호 전류응답을 예측하기 위해 샘플러모델을 제안하고, 이 모델로부터 새로운 이산시간 소신호 모델을 구한다. 제안된 방식은 기존 방식과 달리 복잡한 형태의 보상기를 사용하는 컨버터에도 적용 가능하다. 제안한 새로운 이산시간 소신호 모델을 이용한 예측 결과를 스위칭 모델 시뮬레이션 프로그램인 PSIM을 이용한 시뮬레이션 결과 및 실험결과와 비교하여 제안한 새로운 이산시간 소신호 모델의 우수성을 보인다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.609-614
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• 1998

A new small signal modeling of an average current mode control is proposed. In order to analyze the characteristics of the control scheme, the discrete and continuous time small signal models are derived. The derivation are mainly come from the analysis of the sampling effect presented in the current control loop. By the mathematical interpretation of practical sampler representing the sampling effect of a current control loop, the small signal models of an average current mode control can be easily derived. The instability of the current control loop, which gives rise to the subharmonic oscillation, can be identified by the proposed models. To show the usefulness of the proposed models, the simulation and experiment are carried out. The results show that the predicted results by the proposed model are much better agreed with the measured ones than that of the conventional model, even though the high gain of the compensation network of a current control loop is employed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.309-313
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• 1990

Pulse width modulation using discontinuous conduction modes are applied to a full-bridge series resonant converter to regulate the output from no load to full load with low switching loss and a narrow range of frequency variation. Finally, a simple nonlinear discrete-time dynamic model for this proposed converter is derived using approximation. This discrete time model is linearized and a general input - output transfer function for the propelled converter is derived.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.120-124
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• 1995

TCSC can not only increase power flow but also damp low frequency oscillation by controlling firing angles of thyristors. But, a model considering voltage, current firing angles is not derived. This paper used a small signal model considirng these variables which was derived in paper [1]. TCSC model is combined with swing equation. Being related to rotor angles and firing angles of thyristors, current and synchronizing torque coefficient is reformulated. Because firing angles of thyristors can be controlled only twice within one period, swing equation is transformed to discrete time model. It is shown that low frequency oscillation can be damped by controlling firing angles in one machine infinite bus power system.

• 대한기계학회논문집A
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• 제27권11호
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2001년도 종합학술발표회 논문집 Vol.11 No.1
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• pp.257-261
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• 2001

This paper presents the implementation of wide-band RFIC amplifier operating from near 50MHz to 3GHz using Tachyonics SiGe HBT foundry. Voltage shunt feedback is used for the flat gain and the broad band impedance matching. Initial design parameters are calculated using the low frequency small signal analysis. Since the HBT model was not available at the design time, discrete tuning board was made for fine tuning in the low frequency range. Fabricated amplifier shows 12dB gain with 1dB fluctuation and PldB reaches 15dBm at 850MHz.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2010년도 하계학술대회 논문집
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• pp.129-130
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• 2010

For the high power demand and N+1 redundancy, this paper presents the digital load share (LS) controller design and the implementation of paralleled phase-shifted full-bridge converters (PSFBC) used in distributed power systems. By adopting the digital control strategy, separately used ICs for PSFBC and LS control functions in analog systems can be merged into a cost-effective digital controller. To compensate and stabilize both PSFBC and LS loops with the direct digital design approaches, small-signal model of the system is derived in discrete-time domain. The steady-state and dynamic load sharing performances are also investigated. Experimental results from two 1.2 kW paralleled PSFBC modules are shown to verify the proposed work.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.