• 한국지능시스템학회논문지
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• 제11권6호
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• pp.449-453
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• 2001

본 논문에서는 F-투영법과 기하학적인 성장기준을 적용하여 웨이블렛 신경망의 적응적인 구조를 설계할수 있는 알고리즘을 제안한다. 기하학적인 성장기준은 지역오차를 고려한 예측 오차기준과 준직교성을 보장하는 웨이블렛 신경망의 노드를 추가하기 위한 각도기준으로 구성되어 있다. 이러한 성장기준을 웨이블렛 신경망을 설계자 의도에 부합하도록 구성할 수 있는 방법론을 제시할 수 있다. 제안한 웨이블렛 신경망의 적응구조 설계 알고리즘을 1차원과 2차원 함수 근사화 문제에 적용하여 rsm사한 능력의 우수성을 검증하였다.

• 한국산업정보학회논문지
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• 제9권2호
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• pp.81-88
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• 2004

잡음을 갖는 영상의 크기와 회전불변 패턴인식을 위한 광 웨이블릿 CHF-fSDF((wavelet circular harmonic function-filter modulation synthetic discriminant function, WCHF-fSDF) 필터를 제안하였다. 웨이블릿 CHF-fSDF 필터는 기준영상에 대하여 크기변화된 영상들을 웨이블릿 변환한 후, 이들로부터 추출한 단일 원형고조함수를 학습영상으로 사용하여 합성한다. 제안된 광 웨이블릿 CHF-fSDF 필터는 정합필터의 형태로서 전통적인 4f 광 상관기에 적용할 수 있도록 하였다. 컴퓨터 모의실험을 통하여 제안된 필터는 크기변화와 회전에 무관한 상관출력을 나타내며 특히 잡음환경하에서 유용함을 확인하였다.

• 전기학회논문지P
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• 제60권2호
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• pp.76-81
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• 2011

Power system frequency is the main index of power quality indicating an abnormal state and disturbances of systems. The nominal frequency is deviated by sudden change in generation and load or faults. Power system is used as frequency relay to detection for off-nominal frequency operation and connecting a generator to an electrical system, and V/F relay to detection for an over-excitation condition. Under these circumstances, power system should maintain the nominal frequency. And frequency and frequency deviation should accurately measure and quickly estimate by frequency measurement device. The well-known classical method, frequency estimation technique based on the DFT, could be produce the gain error in accuracy. To meet the requirements for high accuracy, recently Wavelet transforms and analysis are receiving new attention. The Wavelet analysis is possible to calculate the time-frequency analysis which is easy to obtain frequency information of signals. However, it is difficult to apply in real-time implementation because of heavy computation burdens. Nowadays, the computational methods using the Wavelet function and transformation techniques have been searched on these fields. In this paper, we apply the Recursive Discrete Wavelet Transform (RDWT) for the frequency estimation. In order to evaluate performance of the proposed technique, the user-defined arbitrary waveforms are used.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2432-2434
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• 한국공작기계학회논문집
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• 제16권1호
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.126-129
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• 2000

In order to approximate a nonlinear function, wavelet neural networks combining wavelet theory and neural networks have been proposed as an alterantive to coventional multi-layered neural networks. Wavelet neural networks provide better approximating performance than conventional neural networks. In this paper, an effective method to construct an optimal wavelet neural network is proposed using genetic algorithm. This is verified through experimental results.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.379-386
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• 2008

In this paper, the choice of a primary resolution and wavelet basis functions are introduced under random or irregular design points of which the sample size is free of a power of two. Most wavelet methods have used the number of the points as the primary resolution. However, it turns out that a proper primary resolution is much affected by the shape of an unknown function. The proposed methods are illustrated by some simulations.

• 한국정보전자통신기술학회논문지
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• 제7권3호
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• pp.126-131
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• 2014

본 논문에서는 웨이블릿 변환을 통하며 QRS complex를 검출 하며, 32비트 고정 소수점 연산이 가능한 프로세서에도 동작하도록 알고리즘 최적화 기법을 제시한다. 먼저 입력 ECG 신호를 밴드 패스 필터를 통과 시키고, 3개의 서로 다른 웨이블릿 함수를 하나로 병합한 웨이블릿 함수를 이용하여 웨이블릿 변환을 하며, 다음으로 시간 평균 함수를 뒤에 마지막으로 QRS complex를 검출 한다. 제안 알고리즘은 MIT-BIH arrhythmia database에 적용하여 검증한다. 모든 과정은 32비트 고정 소수점 연산으로 구현되며, 삼각함수 같은 복잡한 연산은 테이블화 하였다. 검출 알고리즘은 컴퓨터 시뮬레이션을 통해 평가 한다.

• 대한안전경영과학회지
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• 제3권4호
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• pp.91-101
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• 2001

This paper is to develop replacement models under minimal repair with exponential polynomial wavelet failure rate function. Wavelets have good time-frequency localization, fast algorithms and parsimonious representation. Also this study is presented along with numerical examples using sensitivity analysis for exponential polynomial trigonometric failure rate function.