• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 추계학술대회 논문집
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• pp.118-122
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• 2000

In this study, Ringing is investigated by continuous wavelet transform. Ringing is considered to be one of the typical transient phenomena in the field of ocean engineering. The wavelet analysis is adopted to analyze ringing from the point that wavelet analysis is capable of frequency analysis as well as time domain analysis. The use mother wavelet is the Morlet wavelet. The relation between the frequency of the time series and that of wavelet can be clearly defined with Mor1et wavelet. Experimental data obtained by other researchers was used. The wave height time series and acceleration times series of the surface piercing cylinder were analyzed. The results show that the proposed scheme can detect typical frequency region by the time domain analysis which could hardly be detected if one relied on the frequency analysis.

• East Asian mathematical journal
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• 제26권1호
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• pp.105-113
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• 2010

In the spectral analysis, Fourier coeffcients are very important to give informations for the original signal f on a finite domain, because they recover f. Also Fourier analysis has extension to wavelet analysis for the whole space R. Various kinds of reconstruction theorems are main subject to analyze signal function f in the field of wavelet analysis. In this paper, we will present a new reconstruction theorem of functions in $L^1(R)$ using a nonharmonic Fourier series. When we construct this series, we have used dyadic subdivision schemes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.969-971
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• 1999

Wavelet analysis is applying to many fields such as the time-frequency localization of a time series and a time varying data. In this paper, a statistical testing based Wavelet power spectrum analysis for the stationary Nino3 Sea Surface Temperature(SST) data was executed. Specially, the 95% confidence level for SST was effective in searching the periods of El-Nino using various wavelet basis functions.

• 전기학회논문지
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• 제57권3호
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• pp.494-500
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• 2008

This paper presents the analyzed result of the series arc fault current by using the discrete wavelet transform. The series arcing is caused by a loose connection in series with the load circuit. The series arc current is limited to a moderate value by the resistance of the device connected to the circuit, such as an appliance or a lighting system. The amount of energy in the sparks from the series arcing is less than in the case of parallel arcing but only a few amps are enough to be a fire hazard. Therefore, it is hard to detect the distinctive difference between a normal current and a intermittent arc current. This paper, presents the variation of the ratio of peak values and RMS values of the series arc fault current, and proposes the novel series arc fault detecting method by using the discrete wavelet transform. Loads such as a CFL lamp, a vacuum cleaner, a personal computer, and a television, which has the very similar normal current with the arc current, were selected to confirm the novel method.

• 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.

• 한국전산구조공학회논문집
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• 제23권1호
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• pp.1-8
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• 2010

푸리에 급수는 사인 곡선처럼 일정한 진폭으로 진동하는 정규파(wave)를 사용한다. 그래서 푸리에 급수에서 사용하는 함수는 진동수의 크기가 시간에 따라 변하지 않기 때문에 국부적인 영역에서 급작스런 진동이나 불연속성을 갖는 신호를 표현하기에는 한계가 있다. 그러나 이러한 푸리에 해석의 단점을 여러개의 적절한 웨이블렛의 선형조합에 의해 보완할 수 있는 것이 웨이블렛 급수해석이다. 시간에 집중되어진 궤적의 작은 잔파(wavelet)를 사용함으로써 시간과 주기의 폭을 변화시킬 수 있기 때문에 유동적이고, 특이(singular)형상을 지닌 신호들을 보다 효율적으로 표현할 수 있다. 이 연구의 주요 목적은 웨이블렛 급수해석이라고 불리는 방법을 2계 편미분방정식으로 표현되는 1차원 축방향 부재에 웨이블렛 이론을 적용함과 동시에 유한요소법과 같은 수치해석법과의 비교를 통해 성능평가를 위해 제안되었다. 여러 형태의 웨이블렛 함수의 검토 후에 HAT 함수가 웨이블렛 및 스케일링 함수로 채택되었다. 등분포하중을 받는 경우의 축방향 부재해석에서 제안된 방법은 유한요소법과 같이 효율적임을 보이며, 특히 응력특이점에서는 더 정확한 값을 보였으며, 계산시간도 절약되는 장점을 얻을 수 있었다.

• 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.

• 한국정보통신학회논문지
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• 제13권8호
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• pp.1580-1586
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• 2009

웨이브릿 변환은 신호처리, 디지털 통신 등 여러 분야에 널리 사용된다. 본 논문에서는 웨이브릿 편이 변조(WSK : wavelet shift keying) 시스템에서 하러(Haar)와 도비치(Daubechies) 웨이브릿 계열(series)을 중심으로 웨이브릿 종류에 대한 성능을 분석한다. 사용된 웨이브릿은 하러, 도비치 4탭, 8탭, 12탭을 사용하였다. 분석방법은 눈 모양에 의한 방법과 에러확률에 의한 방법을 사용하였다. 모의실험 결과 필터계수의 개수가 적을수록 좋은 성능을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.