• 전기학회논문지
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• 제57권9호
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• pp.1485-1490
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• 2008

This paper proposes a new Fourier transform-based phasor estimation method to eliminate the adverse influence of the exponentially decaying dc offsets when Discrete Fourier Transform (DFT) is used to calculate the phasor of the fundamental frequency component in a relaying signal. By subtracting the result of odd-sample-set DFT from the result of even-sample-set DFT, the information of dc offsets can be obtained. Two dc offsets in a relaying signal are treated as one dc offset which is piecewise approximated in one cycle data window. The effect of the dc offsets can be eliminated by the approximated dc offset. The performance of the proposed algorithm is evaluated by using computer-simulated signals and EMTP-generated signals. The algorithm is also tested on a hardware board with TMS320C32 microprocessor. The evaluation results indicate that the proposed algorithm has the stable and accurate eliminating performance even if the input signal contains two decaying dc components having different time constants.

• 한국전자통신학회논문지
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• 제18권6호
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• pp.1103-1108
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• 2023

본 논문에서는 음향신호의 배경잡음을 감쇠하기 위한 새로운 알고리즘을 제안한다. 이 알고리즘은 이산 웨이블릿 변환(DWT: Discrete Wavelet Transform) 후 기존의 적응필터를 대신 FNN(: Full-connected Neural Network) 심층학습 알고리즘을 이용하여 잡음감쇠 성능을 개선하였다. 입력신호를 단시간 구간별로 웨이블릿 변환한 다음 1024-1024-512-neuron FNN 딥러닝 모델을 이용하여 잡음이 포함된 단일입력 음성신호로부터 잡음을 제거한다. 이는 시간영역 음성신호를 잡음특성이 잘 표현되도록 시간-주파수영역으로 변환하고 변환 파라미터에 대해 순수 음성신호의 변환 파라미터를 이용한 지도학습을 통하여 잡음환경에서 효과적으로 음성을 예측한다. 본 연구에서 제안한 잡음감쇠시스템의 성능을 검증하기 위하여 Tensorflow와 Keras 라이브러리를 사용한 시뮬레이션 프로그램을 작성하고 모의실험을 수행하였다. 실험 결과, 제안한 심층학습 알고리즘을 사용하면 기존의 적응필터를 사용하는 경우보다 30%, STFT(: Short-Time Fourier Transform) 변환을 사용하는 경우보다는 20%의 평균자승오차(MSE: Mean Square Error) 개선효과를 얻을 수 있었다.

• 대한수학회보
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• 제35권2호
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• pp.345-362
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• 1998

We introduce and anlyze a naturally parallelizable frequency-domain method for parabolic problems with a Neumann boundary condition. After taking the Fourier transformation of given equations in the space-time domain into the space-frequency domain, we solve an indefinite, complex elliptic problem for each frequency. Fourier inversion will then recover the solution of the original problem in the space-time domain. Existence and uniqueness of a solution of the transformed problem corresponding to each frequency is established. Fourier invertibility of the solution in the frequency-domain is also examined. Error estimates for a finite element approximation to solutions fo transformed problems and full error estimates for solving the given problem using a discrete Fourier inverse transform are given.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 A
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• pp.64-66
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• 1998

This paper presents a new efficient method for transient analysis in magnetodynamic systems of linear eddy current problems. This mehtod employs the Fourier transform and the high-order frequency sensitivity of harmonic finite element method. By taking into account the time-constant of magnetodynamic system, the Fourier integral of continuous frequency is converted into the Fourier series of discrete frequency. And with the results of Fourier series expansion of converted input wave form, the responses of each sinusoids is superposed to give the total response of the magnetodynamic systems. But, if the frequency band of input wave form is broad, it takes long computational time since all responses for each sinusoids must be calculated. Therefore, the high-order frequency sensitivity method is employed to estimate the response variation to frequency. The proposed algorithm is applied to an induction heating system to validate its numerical efficiency.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 추계학술대회 논문집
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• pp.63-64
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• 2013

Induction motors' faults detection is almost a popular topic among researchers. Monitoring the output of motors is a key factor in detecting these faults. (Short-time) Fourier, (continuous, discrete) wavelet, and extended Park vector transformations are among the methods for fault detection. One major deficiency of these methods is not being able to detect the severity of faults that carry low energy information, e.g. in ball bearing system failure, there is absolutely no way to detect the severity of fault using Fourier or wavelet transformations. In this paper, the authors have applied the Discrete Wavelet Transform (DWT) frequency-domain analysis to detect bearing faults in an induction motor. In other words, in discrete transform which the output signal is decomposed in several steps and frequency resolution increases considerably, the frequency-band analysis is performed and it will be verified that first of all, fault sidebands become more recognizable for detection in higher levels of decomposition, and secondly, the inner race bearing faults turn out easier in these levels; and all these matter because of eliminating the not-required high energy components in lower levels of decomposing.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.166-168
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• 2002

This paper presents a new numerical algorithm suitable for defining recloser reclaims time and blocking automatic reclosing during permanent faults on overhead lines. It is based on terminal voltage input data processing. The decision if it is safe or not to reclose is determined from the voltage signal of faulted and tripped line phase using Total Harmonic Distortion factor calculated by Discrete Fourier Transform. The algorithm was successfully tested using signals recorded on the real power system. The tests demonstrate the ability of presented algorithm to determine the secondary arc extinction time and to block unsuccessful automatic reclosing of HV lines with permanent fault.

• 대한수학회보
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• 제53권3호
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• pp.733-749
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• 2016

We aim to compute discretely monitored geometric Asian option prices under the Heston model. This method involves explicit formula for multivariate generalized Fourier transform of volatility process and their integrals over different time intervals using a recursive method. As numerical results, we illustrate efficiency and accuracy of our method. In addition, we simulate scenarios which show evidently practical importance of our work.

• Smart Structures and Systems
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• 제1권2호
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• pp.185-215
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• 2005

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

• 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.