• Title/Summary/Keyword: wavelet multi-resolution analysis

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A Study of Shorted-Turn Detection in the Cylindrical Synchronous Generator Rotor Windings via Discrete Wavelet Transform (이산 웨이브렛 변환을 이용한 동기발전기 회전자 층간단락 진단에 관한 연구)

  • Kim, Y.J.;Kim, J.M.
    • Proceedings of the KIPE Conference
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    • 2005.07a
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    • pp.476-478
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    • 2005
  • This paper describes a method for the detection of shorted-turn in the cylindrical synchronous generator rotor windings based on the discrete wavelet transform. Multi-resolution analysis(MRA) based on discrete wavelet transform provides a set of decomposed signals in independent frequency bands. In the proposed method, shorted-turn detection in rotor windings is based on the decomposition of the rotor currents, where wavelet coefficients of these signals have been extracted. Comparing these extracted coefficients is used for diagnosing the healthy machine from faulty machine. Experimental results show the effectiveness of the proposed method for shorted-turn detection in the cylindrical synchronous generator rotor windings.

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A Comparative Study of Discrete Wavelet Transform(DWT) and Wavelet Packet Transform(WPT) for a Li-Ion Cell (이차전지의 이산 웨이블릿 변환(DWT) 및 웨이블릿 패킷 변환(WPT) 비교 분석)

  • Kim, J.H.
    • Proceedings of the KIPE Conference
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    • 2014.07a
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    • pp.152-153
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    • 2014
  • 본 논문에서는 이차전지의 특성비교/분석을 위해 이산 웨이블릿 변환(DWT;discrete wavelet transform)과 웨이블릿 패킷 변환(WPT;wavelet packet transform)을 적용한 연구를 소개한다. 다해상도 분석(MRA; multi resolution analysis)의 시간-주파수 분석을 통해 저주파 성분(approximation;$A_n$)과 고주파 성분(detail;$D_n$)로 분해되는 것은 두 방법 동일하다. 하지만, 이산 웨이블릿 변환이 단순히 저대역 부분만 계속 분해하는 것과 달리 웨이블릿 패킷 변환은 저대역과 고대역을 모두 분해하여 높은 분해성능을 가지는 웨이블릿의 일반화이다. 웨이블릿 패킷 변환을 자세히 소개하고 이를 이차전지에 적용하여 이산 웨이블릿 변환과의 상관성을 정리하였다.

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Video Segmentation and Key frame Extraction using Multi-resolution Analysis and Statistical Characteristic

  • Cho, Wan-Hyun;Park, Soon-Young;Park, Jong-Hyun
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.457-469
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    • 2003
  • In this paper, we have proposed the efficient algorithm that can segment the video scene change using a various statistical characteristics obtained from by applying the wavelet transformation for each frames. Our method firstly extracts the histogram features from low frequency subband of wavelet-transformed image and then uses these features to detect the abrupt scene change. Second, it extracts the edge information from applying the mesh method to the high frequency subband of transformed image. We quantify the extracted edge information as the values of variance characteristic of each pixel and use these values to detect the gradual scene change. And we have also proposed an algorithm how extract the proper key frame from segmented video scene. Experiment results show that the proposed method is both very efficient algorithm in segmenting video frames and also is to become the appropriate key frame extraction method.

A Study on the Road Extraction Using Wavelet Transformation

  • Lee, Byoung-Kil;Kwon, Keum-Sun;Kim, Yong-Il
    • Proceedings of the KSRS Conference
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    • 1999.11a
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    • pp.405-410
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    • 1999
  • Topographic maps can be made and updated with satellite images, but it requires many human interactions that are inefficient and costly. Therefore, the automatizing of the road extraction procedures could increase efficiency in terms of time and cost. Although methods of extracting roads, railroads and rivers from satellite images have been developed in many studies, studies on the road extraction from satellite images of urbanized area are still not relevant, because many artificial components In the city makes the delineation of the roads difficult. So, to extract roads from high resolution satellite images of urbanized area, this study has proposed the combined use of wavelet transform and multi-resolution analysis. In consequence, this study verifies that it is possible to automatize the road extraction from satellite images of urbanized area. And to realize the automatization more completely, various algorithms need to be developed.

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An Emotion Recognition Method using Facial Expression and Speech Signal (얼굴표정과 음성을 이용한 감정인식)

  • 고현주;이대종;전명근
    • Journal of KIISE:Software and Applications
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    • v.31 no.6
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    • pp.799-807
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    • 2004
  • In this paper, we deal with an emotion recognition method using facial images and speech signal. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Emotion recognition using the facial expression is performed by using a multi-resolution analysis based on the discrete wavelet transform. And then, the feature vectors are extracted from the linear discriminant analysis method. On the other hand, the emotion recognition from speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and then the final recognition is obtained from a multi-decision making scheme.

Estimation of Probability of Image Fusion to Improve Accuracy of NDVI Analysis (식생지수 분석의 정확도 향상을 위한 영상융합의 가능성 평가)

  • Song Yeong-Sun;Sohn Hong-Gyoo;Park Chung-Hwan
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2006.04a
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    • pp.297-304
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    • 2006
  • This paper estimates the probability of image fusion to improve accuracy of NDVl analysis. NDVI has been utilized in monitoring extensive forest or forest fire, and image fusion is a method to improve the resolution of multi-spectra image same resolution as high resolution panchromatic image. In this paper wavelet, PCA, IHS, Brovey and multiplicative method was applied to improve spatial resolution of SPOT-4 satellite image. NDVI images were generated from original and fused images and the correlation coefficient of fused and original image was calculated. The results of their comparison, PCA method showed best performance.

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Iris Recognition System using Multi-Resolution Frequency Analysis and Back-Propagation (다해상도 주파수 분할과 Back-Propagation을 이용한 홍채인식)

  • Park, Kyoung-Woo
    • Journal of Integrative Natural Science
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    • v.1 no.3
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    • pp.221-229
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    • 2008
  • 본 논문에서는 기존의 개인 식별 방법의 한계를 해결하는 대안으로 떠오르고 있는 생체인식 기술 중 인식률이 뛰어나고 신뢰성 있는 홍채인식 시스템을 구현하고자 한다. 구현을 위하여 신호처리 분야에서 주로 사용되는 wavelet변환으로 계수 특징 값 추출을 하였으며, 인식률을 알아보기 위하여 신경망 기법을 이용하고자 한다. 그러나 신경망 기법에서 주로 사용되는 비선형 최적화기법인 Scale Conjugate Gradient는 최적화 문제점을 해결하기에는 수렴속도가 느리기 때문에 적합하지 않다. 따라서 본 논문에서는 기존 Scale Conjugate Gradient를 보완한 Levenberg-Marquardt Back-Propagation을 홍채인식에 적용하여 구현함으로써 인식율을 높이고자 한다. 적용한 알고리즘 구현으로 해의 수렴정도, 변수 벡터의 변화정도에 따라 크기를 적절히 변화시킴으로써 수렴속도를 개선하고, 효율성과 안정성을 동시에 얻을 수 있었다.

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Iris Recognition using Multi-Resolution Frequency Analysis and Levenberg-Marquardt Back-Propagation

  • Jeong Yu-Jeong;Choi Gwang-Mi
    • Journal of information and communication convergence engineering
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    • v.2 no.3
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    • pp.177-181
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    • 2004
  • In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

A Merging Algorithm with the Discrete Wavelet Transform to Extract Valid Speech-Sounds (이산 웨이브렛 변환을 이용한 유효 음성 추출을 위한 머징 알고리즘)

  • Kim, Jin-Ok;Hwang, Dae-Jun;Paek, Han-Wook;Chung, Chin-Hyun
    • Journal of KIISE:Computing Practices and Letters
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    • v.8 no.3
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    • pp.289-294
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    • 2002
  • A valid speech-sound block can be classified to provide important information for speech recognition. The classification of the speech-sound block comes from the MRA(multi-resolution analysis) property of the DWT(discrete wavelet transform), which is used to reduce the computational time for the pre-processing of speech recognition. The merging algorithm is proposed to extract valid speech-sounds in terms of position and frequency range. It needs some numerical methods for an adaptive DWT implementation and performs unvoiced/voiced classification and denoising. Since the merging algorithm can decide the processing parameters relating to voices only and is independent of system noises, it is useful for extracting valid speech-sounds. The merging algorithm has an adaptive feature for arbitrary system noises and an excellent denoising SNR(signal-to-nolle ratio).

Face Recognition Using Wavelet Coefficients and Hidden Markov Model (웨이블렛 계수와 Hidden Markov Model을 이용한 얼굴인식 기법)

  • Lee, Kyung-Ah;Lee, Dae-Jong;Park, Jang-Hwan;Chun, Myung-Geun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.6
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    • pp.673-678
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    • 2003
  • In this paper, we proposes a method for face recognition using HMM(hidden Markov model) and wavelet coefficients First, input images are compressed by using the multi-resolution analysis based on the discrete wavelet transform. And then, the wavelet coefficients obtained from each subband are used as feature vectors to construct the HMMs. In the recognition stage, we obtained higher recognition rate by summing of each recognition rate of wavelet subband. The usefulness of the proposed method was shown by comparing with conventional VQ and DCT-HMM ones. The experimental results show that the proposed method is more satisfactory than previous ones.