• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1677-1685
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• 2014

Major problem in the high voltage power transmission line is the flashover due to polluted ceramic insulators which leads to failure of equipments, catastrophic fires and power outages. This paper deals with the development of a better diagnostic tool to predict the flashover and pollution severity of power transmission line insulators based on the wavelet transform and fuzzy c-means clustering approach. In this work, laboratory experiments were carried out on power transmission line porcelain insulators under AC voltages at different pollution conditions and corresponding leakage current patterns were measured. Discrete wavelet transform technique is employed to extract important features of leakage current signals. Variation of leakage current magnitude and distortion ratio at different pollution levels were analyzed. Fuzzy c-means algorithm is used to cluster the extracted features of the leakage current data. Test results clearly show that the flashover and pollution severity of power transmission line insulators can be effectively realized through fuzzy clustering technique and it will be useful to carry out preventive maintenance work.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.179-186
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• 2009

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.376-382
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• 2003

Recently, there have been many types of wavelet transformations proposed to remove the noise from an signal and image data By using feature of seperating the noise from the original image the Wavelet transformations can retain the edges of the images The wavelet analysis is complete when the basis function is coded into the wavelet This Thesis describes a method of using wavelet transformation to remove the noise from an image signal. Although the wavelet transformation proposed by Donoho and Johnstone works, it does not reliably remove all the noise from the images. So this thesis propose an algorithm that selected Wavelet Shrinkgae and threshold according to the features of bands and amplitude of noise.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.585-591
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• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.57-65
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• 2010

In this paper, we present a universal steganalysis scheme. The proposed method extract features of two types. First feature set is extracted from histogram characteristic of the wavelet subbands. Second feature set is determined by statistical moments of wavelet characteristic functions. 3-level wavelet decomposition is performed for stego image and cover image using the Haar wavelet basis. We extract one features from 9 high frequency subbands of 12 subbands. The number of second features is 39. We use total 48 features for steganalysis. Multi layer perceptron(MLP) is applied as classifier to distinguish between cover images and stego images. To evaluate the proposed steganalysis method, we use the CorelDraw image database. We test the performance of our proposed steganalysis method over LSB method, spread spectrum data hiding method, blind spread spectrum data hiding method and F5 data hiding method. The proposed method outperforms the previous methods in sensitivity, specificity, error rate and area under ROC curve, etc.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.302-305
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and denpends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.9
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• pp.663-669
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• 2004

The mobile tole-cardiology system is the new research area that support an ubiquitous health care based on mobile telecommunication networks. Although there are many researches presenting the modeling concepts of a GSM-based mobile telemedical system, practical application needs to be considered both compression performance and error corruption in the mobile environment. This paper evaluates three wavelet ECG compression algorithms over CDMA networks. The three selected methods are Rajoub using EPE thresholding, Embedded Zerotree Wavelet(EZW) and Wavelet transform Higher Order Statistics Coding(WHOSC) with linear prediction. All methodologies protected more significant information using Forward Error Correction coding and measured not only compression performance in noise-free but also error robustness and delay profile in CDMA environment. In addition, from the field test we analyzed the PRD for movement speed and the features of CDMA 1X. The test results show that Rajoub has low robustness over high error attack and EZW contributes to more efficient exploitation in variable bandwidth and high error. WHOSC has high robustness in overall BER but loses performance about particular abnormal ECG.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.372-377
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• 1998

This paper deals with the detection, feature extraction and classification of surface defects in cold rolled strips. Inspection systems are one of the most important fields in factory automation. Defects such as slipmark and dullmark can be effectively detected with a Gaussian matched filter because their shapes are similar to Gaussian. It is justified that the proposed WF(Wavelet Frame) method could be regarded as multiscale Gaussian matched filter which can be applied to the inspection of cold rolled strip. After a wavelet frame transform, the entropies and moments are computed for each subband which pass through both local low pass filter and nonlinear operator. With these features as input, a MLP(Multi Layer Perceptron) is used as a classifier. The proposed inspection method was applied to the real images with defects, and hence showed good performance. The role of each extracted feature is analyzed by KLT(Karhunen-Loeve Transform).