• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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• pp.309-314
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• 2003

Impact Echo method has been successful in detecting a variety of defects in concrete structure. This study has the objectives to show important aspects of applying the Discrete Wavelet Transform(DWT) to signal processing of Modified Impact Echo(ModIE) Measurement systems and to the understanding of the seismic wave propagation. The data of ModIE were processed by DWT and compared with the results of conventional ModIE Analysis. Although it is inconsistent in the evaluated thickness of concrete lining, the DWT provides the features of separation, synthesis and de-noising in the original signal. The application of technique by wavelet was explained numerically with ABAQUS and performed experimentally with a real scale model in this work. Further works on the possible ways for creating new mother wavelet are specially needed for the enhancement of seismic signal analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3050-3052
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• 2000

Recently. the subject of "wavelet analysis" has drawn much attention from both mathematical and engineering application fields such as Signal Processing, Compression/ Decomposition, Statistics and etc. Analogous to Fourier analysis, wavelets is a versatile tool with very rich mathematical content and great potential for applications. Specially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. In this paper, discrimination analyses of acquired electrical current signals for each and mixed loads were tried by using Morlet wavelet transform. Their representative loads were classified as TV, DRY, REF, and FL.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.391-394
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• 1997

In this paper, we considered newly the use of wavelet transform in order to improve the troubles of the established methods for the analysis of ultrasound echo signals. We made the phantoms of 13.2g, 19.8g, 26.4g, 33.0g, 39.8g by ourselves, and extracted the only pulse-echo signals that reflected through the mediums using windowing technique. For determining the characterized value, the signals were wavelet transformed, absoluted, and integral calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them except of some datas. But this will be improved by advanced work as sellecting a proper mother wavelet, a method of making phantoms, correcting the various errors, etc. We expect that wavelet transform is powerful for analysis of ultrasound signals.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• 전기학회논문지P
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• 제57권4호
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• pp.377-382
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• 2008

For stator winding protection of AC generator, KCL(Kirchhoff's Current Law) is widely applied. Actually a CRDR(Current Ratio Differential Relay) based on DFT(Discrete Fourier Transform) has been used for protecting generator. It has been pointed out that defects can occur during the process of transforming a time domain signal into a frequency domain one which can lead to loss of time domain information. Wavelets techniques are proposed for the analysis of power system transients. This paper introduces an algorithm to choose a suitable Mother Wave1et for generator stator fault detection. For optimal selection, we analyzed db(Daubechies), sym(Symlets), and coif(Coiflects) of Mother Wavelet. And we compared with performance of the choice algorithm using detail coefficients energy and RMS(root mean square) error. It can be improved the reliability of the conventional DFT based CRDR. The feasibility and effectiveness of the proposed scheme is proved with simulation using collected data obtained from ATP (Alternative Transient Program) package.

• 한국정보통신학회논문지
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• 제3권1호
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• pp.83-95
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• 1999

웨이브릿 변환은 시간 및 주파수에 대하여 국부성을 가지며, 비정상상태의 신호를 해석하는데 유용하다. 웨이브릿 변환에서의 기저함수들은 원형 웨이브릿을 천이(translation) 및 확장/수축(dilation)을 시킴으로서 만들어진다. 본 논문은 두 개의 웨이브릿을 선형적으로 조합한 선형조합 웨이브릿 변환을 사용하여 시간-주파수 분석방법을 제안하였다. 그리고 제안된 선형조합 웨이브릿 변환을 사용하여 진단모니터링 시스템에 적용하였다. 제안한 선형조합 웨이브릿 변환 분석 방법의 유효성을 검증하기 위하여 FFT(Fast Fourier Transform), Daubechies, Haar 기법과 비교한다. 분석 대상 신호로는 linear chirp 신호, 팬 소음신호, 회전체 회전신호, 전기신호를 사용하였다. 그 결과는 정상상태 신호처럼 비정상상태 시간 신호를 나타내는데 적당하다. 또한 선형조합 웨이브릿을 사용한 진단 모니터링 시스템은 효과적인 신호분석을 수행한다.

• 정보처리학회논문지B
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• 제10B권4호
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• pp.459-464
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• 2003

본 논문에서 제안한 위치 이동에 무관한 웨이블렛 변환을 이용한 홍채 인식 방법은 영상 획득 장비에 의해 획득한 사용자의 눈 영상에 대하여 홍채 영역만을 추출하기 위한 전처리를 수행하고 전처리를 거친 홍채 영상에 의하여 사용자의 신원을 식별하는데 있어서 홍채 영상의 기울어짐 및 이동 문제를 해결하였다. 이를 위해서 일반적인 웨이블렛을 사용하는 대신, 위치 이동에 무관한 웨이블렛 변환을 통하여 최적의 특징값을 추출한후, 이를 코드화하여 저장한 후, 비교하여 본인 여부를 식별하였다. 실험결과 제안된 방법으로 생성된 특징 벡터와 기존에 등록된 특징 벡터의 일치도 측정에 있어서 종래의 웨이블렛 변환 홍채 인식 방법보다 오인식률(FAR) 및 오거부율(FRR)이 현저하게 감소하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.62_63
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• 2009

This paper is distribution utility to generation of analysis fault several cases on the ground of substation in a energy meter three phase current, voltage data measurement to fault type and characteristics. Mother wavelet transformation of suitable to method algorism from the distribution utility to generation of fault in image impedance etc several parameter for utility characteristics effective to probatory.

• Smart Structures and Systems
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• 제18권5호
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Transactions on Electrical and Electronic Materials
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• 제18권4호
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• pp.221-224
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• 2017

This paper dealt with the extraction of series arc signals based on wavelet transform in order to improve the accuracy of arc detection in indoor wiring systems. Three types of arc sources including a cord-cord, a terminal-cord, and an outlet-plug were fabricated to simulate typical arc defects. An arc generator fabricated according to UL 1699 was used to generate arcs. The optimal mother wavelet was selected as bior1.5 by calculating the correlation coefficients between the detected single current pulse and the wavelet. The detected arc current signals were then decomposed into eight levels using the discrete wavelet transform that implements the multi-resolution analysis method. By analyzing the decomposed components, the detail components D6, D7, and D8 were associated with arc signals, which were used for signal reconstruction. From the result, it was verified that the proposed method can be used for the extraction of the series arc signal from the AC mains, which is expected to be applied to further analysis of arc signals in indoor wiring systems.