• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.9-15
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• 2017

In this study we performed the experiment to detect the ERP using SVM with wavelet features. The EEG signal that is generated visual stimulated ERP database in SCCN applied for the experiment. The feature vectors for experiment are categorized frequency and continuous wavelet- based vectors. In experimental results, the detection rate of SVM with wavelet feature vectors improved above 10% comparing with frequency- based feature vector. Based on the experimental results we analyzed the relation between the activity degree of the ERP and the band split characteristics of the ERP by wavelet transform.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.87-92
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• 1996

Transient signals in underwater show several characterisrics, that is, short duration, strong nonstationarity, various types of transient sources, which make it difficult to analyze and classify transient signals. In this paper, the feature vector extraction method for transient SOMAR signals is discussed by applying digital signal processing methods to the analysis of transient signals. A feature vector extraction methods using wavelet transform, which enable us to obtain better recognition rate than automatic classification using the classical method, are proposed. It is confirmed by simulation that the proposed method using wavelet transform performs better than the classical method even with smaller number of feature vectors. Especially, the feature vector extraction method using PR-QMF wavelet transform with the Daubechies coefficients is shown to perform well in noisy environment with easy implementation.

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.76-85
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• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.671-674
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• 1997

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FIT, DWT(Discrete Wavelet Transform), and Fisher's criterion are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 10-class power quality disturbances are also provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.281-286
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• 2003

Wavelet Transform can effectively represent the local characteristics of a signal in the space-frequency domain. However, the feature vector extracted using wavelet transform is not translation invariant. This paper describes a new feature extraction method using wavelet transform, which is translation-invariant. Based on this translation-invariant feature extraction, the iris recognition method, based on this feature extraction method, is robust to noises. Experimentally, we show that the proposed method produces super performance in iris recognition.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.327-344
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• 2013

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.