• 제목/요약/키워드: Stationary wavelet transform

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웨이브렛 변환평면에서의 근전도신호 인식에 관한 연구 (A Study on the Identification of the EMG Signal in the Wavelet Transform Domain)

  • 김종원;김성환
    • 대한의용생체공학회:의공학회지
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    • 제15권3호
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    • pp.305-316
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    • 1994
  • All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

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웨이브렛 변환을 이용한 비정상 신호의 순간 주파수 결정 (Non-stationary signal analysis by Continuous Wavelets Transform)

  • 조익현;이인수;윤동한
    • 한국정보전자통신기술학회논문지
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    • 제2권2호
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    • pp.29-36
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    • 2009
  • 비선형적인 위상 변화를 지닌 비정상(non-stationary)신호는 레이더(Radar), 통신(telecommunication), 생체공학, 지질탐사, 음향 등 여러 분야에서 쉽게 접하는 신호이다. 비정상신호는 일반적으로 시간에 따라 신호의 물리적 특성이 변화하는 신호를 의미하며, 순간 주파수는 신호의 특정시간에 해당하는 신호의 주파수를 의미한다. 이 논문에서는 순간 주파수를 결정하기 위한 연속 웨이브렛 변환의 적용에 대하여 논하였다.

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웨이브렛 패킷 변환을 이용한 적응알고리듬의 수렴속도 향상 (Enhancement of Convergence Speed of Adaptive Algorithm using Wavelet Packet Transform)

  • 박서용;김대성
    • 정보학연구
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    • 제2권2호
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    • pp.127-138
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    • 1999
  • 최근 들어 신호처리 분야에서 웨이브렛 변환을 이용한 연구가 활발히 진행되고 있다. 본 논문에서는 웨이브렛 변환 영역에서의 적응 알고리듬을 구현하고 비 정제적 신호에 대한 성능을 평가하였다. 입력 신호를 웨이브렛 패킷 변환하여 다해상도 분해하고 NLMS알고리듬을 이용하여 부 밴드에서의 적응 알고리듬을 구현하였다. 제안한 방법을 화이트 가우시안 잡음이 섞인 도플러 신호의 잡음 제거에 적용하여 그 성능을 평가하였다.

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웨이브렛 기반 주파수 응답함수를 이용한 고속철도차량의 진동 및 궤도불규칙 특성 분석 (Analysis of the Vibration of High Speed Trains and the Irregularity of Railway Using a Wavelet-based Frequency Response Function)

  • 이준석;최성훈;김상수;박춘수
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2008년도 춘계학술대회논문집
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    • pp.766-771
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    • 2008
  • In this paper, the vibration of high speed trains and the irregularity of railway are examined using a wavelet-based frequency response function. To investigate their characteristics, non-stationary acceleration data are acquired and processed using the wavelet transform. Also, the railway irregularity is examined by acquiring the data from the on-board laser-based measurement system. The correlation between the train vibration and the railway irregularity has been investigated. From the analysis, the wavelet-based frequency response function is a promised method for the dynamic characteristics of high speed trains.

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인공신경망과 정상 웨이블렛 변환을 활용한 감조하천 수위 예측 (Prediction of the Water Level of the Tidal River using Artificial Neural Networks and Stationary Wavelets Transform)

  • 이정하;황석환
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2021년도 학술발표회
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    • pp.357-357
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    • 2021
  • 홍수로 인한 침수피해 발생을 최소화하기 위해 정확한 하천의 수위 예측과 리드타임 확보가 매우 중요하다. 특히 조석현상의 영향을 받는 감조하천의 경우 기존의 물리적 수문모형의 적용이 제한되어 하천수위 예측의 정확도가 떨어지기도 한다. 따라서 본 연구에서는 이러한 감조하천 수위 예측의 정확도를 높이기 위해 조석현상을 분리하고 인공신경망을 활용하는 하이브리드 모델을 제안 하였으며 다중 선형회귀분석과 비교 분석하였다. 감조하천에 위치한 교량의 수위데이터에서 Stationary Wavelet Transform으로 조석현상을 분리하였으며, 이외의 수위에 영향을 주는 time series data와 인공신경망(ANN)을 활용하여 1시간, 2시간, 3시간 후의 수위를 예측하였다. 하이브리드 모델은 96% 이상의 정확도를 보였으며 다중 선형회귀 분석과 비교하여도 높은 정확성을 보여주었다.

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웨이블릿에 기반한 시그널 형태를 지닌 대형 자료의 feature 추출 방법 (A Wavelet based Feature Selection Method to Improve Classification of Large Signal-type Data)

  • 장우성;장우진
    • 대한산업공학회지
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    • 제32권2호
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    • pp.133-140
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    • 2006
  • Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

노이지 제거기법을 이용한 지중송전계통 고장점 추정 (Fault Location Using Noise Cancellation Technique on Underground Power Cable Systems)

  • 정채균;이종범
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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    • pp.440-441
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    • 2006
  • The fault location algorithm based on wavelet transform was developed to locate the fault more accuracy after the operation of relay. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. The algorithm was based on the correlation of wavelet coefficients at multi-scales. Fault location algorithm was tested by simulation on real power cable system. From these results, the fault can be located even in very difficult situations, such as at different inception angle and fault resistance.

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Applications of the wavelet transform in the generation and analysis of spectrum-compatible records

  • Suarez, Luis E.;Montejo, Luis A.
    • Structural Engineering and Mechanics
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    • 제27권2호
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    • pp.173-197
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    • 2007
  • A wavelet-based procedure to generate artificial accelerograms compatible with a prescribed seismic design spectrum is described. A procedure to perform a baseline correction of the compatible accelerograms is also described. To examine how the frequency content of the modified records evolves with time, they are analyzed in the time and frequency using the wavelet transform. The changes in the strong motion duration and input energy spectrum are also investigated. An alternative way to match the design spectrum, termed the "two-band matching procedure", is proposed with the objective of preserving the non-stationary characteristics of the original record in the modified accelerogram.

Multiscale Correlation을 이용한 지중송전계통 고장점 추정에 관한 연구 (A Study on the Fault Location Using Multiscale Correlation in Underground Power Cable Systems)

  • 정채균;이종범;강지원
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2004년도 하계학술대회 논문집 A
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    • pp.557-559
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    • 2004
  • The study of applying wavelet transform in power cable system fault location has been recognized by many researchers and investigated. For performance of fault location, the fault generated transients can be captured at one end of the cable or both ends. Between two approaches, single-ended approach is less expensive and more reliable as it doesn't need communication link between the ends of the cable. So, we performs the approach based on the one. In this paper, we are going to introduce a new algorithm to discriminate the transient and the reflected signal using wavelet coefficient. For wavelet transform, the stationary wavelet transform(SWT) is applied instead of conventional DWT because SWT has redundancy properties which is more useful in noisy signal processing.

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Riesz and Tight Wavelet Frame Sets in Locally Compact Abelian Groups

  • Sinha, Arvind Kumar;Sahoo, Radhakrushna
    • Kyungpook Mathematical Journal
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    • 제61권2호
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    • pp.371-381
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    • 2021
  • In this paper, we attempt to obtain sufficient conditions for the existence of tight wavelet frame sets in locally compact abelian groups. The condition is generated by modulating a collection of characteristic functions that correspond to a generalized shift-invariant system via the Fourier transform. We present two approaches (for stationary and non-stationary wavelets) to construct the scaling function for L2(G) and, using the scaling function, we construct an orthonormal wavelet basis for L2(G). We propose an open problem related to the extension principle for Riesz wavelets in locally compact abelian groups.