• Smart Structures and Systems
• /
• v.18 no.5
• /
• pp.1063-1085
• /
• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2002.05c
• /
• pp.170-176
• /
• 2002

In this papers, we proposed the feature extraction method due to partial discharge type of transformers. For wavelet transform, Daubechie's 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 acoustic emission signal generated from each partial discharge type. The defects which could occur in a transformer were simulated by using needle-plane electrode, IEC electrode and Void electrode. Also, these coefficients are used to identify signal of partial discharge type electrode fault in transformer. As a result, from compare of acoustic emission amplitude and acoustic average value, we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise, In case of skewness and kurtosis, we are obtained results of Needle-Plane electrode electrode> Void electrode> IEC electrode.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.731-735
• /
• 2001

In this paper, we propose the new audio watermarking method and can be used on line processing. Instead of the wavelet transform, we use the integer wavelet transform for the reduction of the computational load. The watermark associated with the chi-square distribution is inserted into the signal on the integer wavelet domain. When extracting the watermark, the spread spectrum methods are used with the coefficients associated with the covariance sequence. We show that the chi-square distribution is a good tool for the spread spectrum method on the wavelet domain. This watermarking technique may be sued for the control of the electrical product which can be controlled with the hidden signals and can be moved according to the audible signals simultaneously.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.3
• /
• pp.178-184
• /
• 2009

An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.10
• /
• pp.2629-2639
• /
• 1997

The video coding for very low bit-rate has recently received considerable attention, but the conventional coding schemes with block based transform suffer from the blocky effect for the constraints of limited bit-rate. In this paper, we present a video coding system based on wavelet transform and multiresolution motion estimation/compensation for very low bit-rate video. The proposed scheme uses the wavelet transform which is flexible to represent non-stationary image signals and adaptable to the human visual characteristics. The wavelet transformed coefficients are coded by various coding modes in accordance with the sum of absolute error after motion estimation/compensation in wavelet decomposed domain. And simple buffer control technique is applied to handle constant image quality. It is shown that the presented scheme has more acceptable image quality without blocky effects than conventional block based transform video coding.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.754-759
• /
• 2007

This study exploits the data discriminating capability of silhouette statistics, which combines wavelet-based vertical energy threshold technique for the purpose of extracting damage-sensitive features and clustering signals of the same class. This threshold technique allows to first obtain a suitable subset of the extracted or modified features of our data, i.e., good predictor sets should contain features that are strongly correlated to the characteristics of the data without considering the classification method used, although each of these features should be as uncorrelated with each other as possible. The silhouette statistics have been used to assess the quality of clustering by measuring how well an object is assigned to its corresponding cluster. We use this concept for the discriminant power function used in this paper. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating both open- and breathing-type damage even in the presence of a considerable amount of process and measurement noise.

• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.3
• /
• pp.147-153
• /
• 2018

Jamming countermeasures are used to decrease or prevent the impact of intentional jamming applied to degrade the quality of information provided by a global navigation satellite system (GNSS) receiver. The maximum performance of jamming countermeasure can be obtained only when a proper technique is applied according to the type of jammer. This paper suggests a jamming identification technique for providing information regarding the type of jamming. The center frequency and bandwidth of jammer signal are inconsistent and may change according to time, and thus a spectral correlation function and wavelet coherence were considered in order to analyze the signal in the time and frequency space. Because the two characteristics derive different analysis results, two different identification techniques were suggested and the performances thereof were analyzed. Numerical results show that the two identification techniques have relative advantages and disadvantages as to time consumed and performance. The suggested methods can sufficiently identify the jammer before the GNSS receiver becomes inoperable because of jamming.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.6
• /
• pp.937-942
• /
• 2002

The use of digital imaging technique and digital contents based on internet has grown rapidly for last several years, and the needs of digital image protection become more important. For the purpose of copyright protection on digital image, the verification of authentication techniques like content authentication, ownership authentication, illegal copy and etc are needed. Digital watermarking, the invisible encryption technique to insert digital watermark into image the sophisticated perceptual information should be used for providing transparency and robustness of images on watermarking process. In this paper, we implement the algorithm for preventing forged attack, ownership protection and authentication by transforming the wavelet algorithms in frequency domain in terms of human visual system.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.5
• /
• pp.94-102
• /
• 2007

In de-noising, it is wellknown that wavelet-thresholding algorithm shows near-optimal performances in the minimax sense. However, the wavelet-thresholding algorithm is difficult in realization it on hardware, such as FPGA, because of wavelet function complexity. In this paper, we present a new do-noising technique with the binary tree structured filter bank, which is based on the signal power ratio of each subbands to the total signal power. And we realize it on FPGA. For simple realization, the filter banks are designed by Hadamard transform coefficients. The simulation and hardware experimental results show that the performance of the proposed method is similar with that of soft thresholding de-noising algorithm based on wavelets, nevertheless it is simple.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.703-715
• /
• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.