• 전기학회논문지
• /
• 제62권5호
• /
• pp.667-672
• /
• 2013

In this paper, we propose a multi-impedance changes localization method of on-voltage underground power cable using the wavelet transform based time-frequency domain reflectometry (WTFDR). To localize the impedance change in on-voltage power cable, the TFDR is the most suitable among reflectometries because the inductive coupler is used to inject the reference signal to the live cable. At this time, the actual on-voltage power cable has multi-impedance changes such as the automatic section switches and the auto load transfer switches. However, when the multi-impedance changes are generated in the close range, the conventional TFDR has the cross term interference problem because of the nonlinear characteristics of the Wigner-Ville distribution. To solve the problem, the wavelet transform (WT) is used because it has the linearity. That is, using WTFDR, the cross term interference is not generated in multi-impedance changes due to the linearity of the WT. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for the on-voltage underground power cable.

• 한국정밀공학회지
• /
• 제31권4호
• /
• pp.327-334
• /
• 2014

This paper presents a method of fault detection and fault localization from acoustic noise measurements. In order to detect the presence of noise sources wavelet transform is applied to acoustic signal. In addition, a cross correlation based method is proposed to calculate the exact location of the noise allowing the user to quickly diagnose and resolve the source of the noise. The fault detection system is implemented using two microphones and a computer system. Experimental results show that the system can detect faults due to artifacts accidentally inserted during the manufacturing process and estimate the location of the fault with approximately 1 cm precision.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 C
• /
• pp.1880-1884
• /
• 2000

The discrete wavelet transform is utilized as preprocessing of Neural Network(NN) to identify aging state of internal partial discharge in transformer. The discrete traveler transform is used to produce wavelet coefficients which are used for Classification. The statistical parameters (maximum of wavelet coefficients, average value, dispersion, skewness, kurtosis) using the wavelet coefficients are input into an back-propagation neural network. The neurons whose weights have obtained through Result of Cross-Validation. The Neural Network learning stops either when the error rate achieves an appropriate minimum or when the learning time overcomes a constant value. The networks, after training, can decide if the test signal is Early Aging State or Last Aging State or normal state.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.225-228
• /
• 2005

According to a popularization of the Internet and digital lifestyle, digital watermarks have been proposed for protection of copyrighted multimedia content. In blind watermark detection, which an original image is not provided, robustness against geometric distortion and compression remains challenging. In this paper, we propose a new perceptual blind discrete wavelet transform - discrete Fourier transform (DWT-DFT) composite watermarking scheme that is robust against both general linear transform and JPEG compression. This algorithm constructs an image-dependent watermark in the most significant DWT coefficients, which is determined by using a hierarchical tree structure. Strength of watermark is determined from a just-noticeable difference (JND) profile of a perceptual model. Furthermore, a desired template is inserted into DFT domain of the watermarked image. In new manner, a cross-ratio of four collinear points is used for detecting the template. Experimental results have showed that the proposed scheme is robust against general linear distortion, JPEG compression and various general kinds of attacks in the Stirmark 3.1 watermark evaluation tool.

• 한국전기전자재료학회논문지
• /
• 제14권1호
• /
• pp.84-92
• /
• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Structural Monitoring and Maintenance
• /
• 제9권2호
• /
• pp.157-177
• /
• 2022

In this paper, an innovative finite element updating method is presented based on the variation wavelet transform coefficients of Auto/cross-correlations function (WTCF). The Quasi-linear sensitivity of the wavelet coefficients of the WTCF concerning the structural parameters is evaluated based on incomplete measured structural responses. The proposed algorithm is used to estimate the structural parameters of truss and plate models. By the solution of the sensitivity equation through the least-squares method, the finite element model of the structure is updated for estimation of the location and severity of structural damages simultaneously. Several damage scenarios have been considered for the studied structure. The parameter estimation results prove the high accuracy of the method considering measurement and mass modeling errors.

• Journal of Information Processing Systems
• /
• 제15권5호
• /
• pp.1068-1081
• /
• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• 한국항해항만학회지
• /
• 제32권10호
• /
• pp.777-783
• /
• 2008

입자영상속도계(PIV)의 해석에 웨이블릿 변환을 적용하여 정성적인 유동정보뿐만 아니라 공간분해능을 갖는 정량적인 속도장 정보를 제공하고 있다 이 기법은 기포유동(bubbly flow)과 같은 다상(multi-phase)의 유동구조를 해석하는 데도 유용하게 살일 수 있다. 본 연구에서는 기체와 액체의 이상유동(two-pase flow)에 PIV기법을 적용하고 이산 웨이블릿 변환을 사용하여 유장해석을 수행함으로써, 기포를 포함한 속도장 특성과 유동특성을 조사한다.

• 한국군사과학기술학회지
• /
• 제8권2호
• /
• pp.38-48
• /
• 2005

Instantaneous frequency of doppler signals is used to get the information of the relative velocity and the miss distance between a missile and a corresponding target. In this paper, we have performed time-frequency analysis and instantaneous frequency estimation with Short Time Fourier Transform(STFT), Wigner Ville Distribution(WVD) and Continuous Wavelet Transform(CWT) about the doppler signals generated by moving targets. Performance evaluation was performed using simulated doppler signals generated by a single moving target and two moving targets. From the results of the time-frequency analysis, we found that WVD method was the most efficient instantaneous frequency estimator among the three methods. But in case of two moving targets, WVD method got cross talks and CWT method got oscillation when two doppler frequencies were close to each other.

• 펄프종이기술
• /
• 제32권5호
• /
• pp.14-25
• /
• 2000

In this investigation, a wavelet transform analysis was used to decompose beta-radiographic formation images into spectral and spatial components. Conventional formation analysis may use spectral analysis, based on Fourier transformation or variance vs. zone size, to describe the grammage distribution of features such as flocs, streaks and mean fiber orientation. However, these methods have limited utility for the analysis of statistically stationary data sets where variance is not uniform with position, e.g. paper machine CD profiles (especially those that contain streaks). A continuous wavelet transform was used to analyze formation data arrays obtained from radiographic imaging of handsheets and cross machine paper samples. The response of the analytical method to grammage, floc size distribution, mean fiber orientation an sensitivity to feature localization were assessed. From wavelet analysis, the change in scale of grammage variation as a function of position was used to demonstrate regular and isolated differences in the formed structure.