• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1028-1036
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• 2000

In this paper, an error-resilient image compression technique using wavelet transform is proposed. The zerotree technique that uses properties of statistics, energy and directions of wavelet coefficients in the space-frequency domain shows effective compression results. Since it is highly sensitive to the propagation of channel errors, evena single bit error degrades the whole image quality severely. In the proposed algorithm, the image is encoded by the SPIHT(Set Partitioning in Hierarchical Trees) algorithm using the zerotree coding technique. Encoded bitstreams are partitioned into some blocks using the subband correlations and then fixed-length blocks are made by using the effective bit reorganization algorithm. finally, an effective bit allocation technique is used to limit error propagation in each block. Therefore, in low BER the proposed algorithm shows similar compression performance to the zerotree compression technique and in high BER it shows better performance in terms of PSNR than the conventional methods.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.4
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• pp.217-223
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• 2013

This paper presents the wet damping estimation of the segmented hull model using the random decrement technique together with the continuous wavelet transform. The tested 16 sea states are grouped together based on the speed of the ship in order to figure out the possible influence of the ship speed on the damping ratio. The measured time histories of vertical bending moment for each tested sea state were processed with random decrement technique to derive the free decay signal, from which the damping ratios are estimated. Also, the autocorrelation functions of the filtered signal were calculated and comparison was made with the free decay signal obtained from the random decrement technique. Then the wet damping ratios for each sea state group, as well as precise wet natural frequencies, are estimated by using continuous wavelet transform. It turned out that the wet natural frequencies derived from the measured signal did not show any significant discrepancy compared with those obtained by wet hammering test, whereas the damping ratio did. It was considered that the discrepancy of the damping ratio between in calm and moving water may be attributed to the viscous effects caused by dramatically different flow pattern and relative velocity between the vibrating structure and surrounding fluid particles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2323-2329
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• 2008

Advances in wireless communication and hardware technology have made it possible to manufacture high-performance tiny sensor nodes. More recently, the availability of inexpensive cameras modules that are able to capture multimedia data from the environment has fostered the development of Wireless Multimedia Sensor Networks(WMSNs). WMSN supplements the a advanced technique that senses, transmits, and processes the multimedia contents upon the text based traditional wireless sensor network. Since the amount of data which the multimedia contents have, is significantly larger than that of text based data, multimedia contents require lots of computing power and high network bandwidth. To process the multimedia contents on the wireless sensor node which has very limited computing power and energy, a technique for WMSN should take account of computing resource and efficient transmission. In the paper, we propose a new image compression technique YWCE for efficient compression and transmission of image data in WMSN. YWCE introduces 4 type of technique for motion estimation and compensation based on the Resolution Scalability of Wavelet. Experimental result shows that YWCE has high compression performance with different set of 4 type.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.2
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• pp.152-162
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• 2001

Laser-generated ultrasonic technique which is one of the reliable nondestructive evaluation techniques has been applied to evaluate the integrity of structures by analyzing the characteristics of signal obtained from surface crack. Therefore, the signal analysis of the laser-generated ultrasonics is absolutely necessary for the accurate and quantitative estimation of the surface defects. In this study, one-sided measurement by laser-generated ultrasonic has been applied to evaluate the depth of the surface-breaking crack in the materials. However, since the ultrasonic waveform excited by pulse laser is very difficult to distinguish the defect signals, it is necessary to consider the signal analyses of the transient waveform. Wavelet Transform(WT) is a powerful tool for processing transient signals with temporally varying spectra that helps to resolve high and low frequency transient components effectively. In this paper, the analyses of the surface-breaking crack of the ultrasonic signal excited by pulse laser are presented by employing the WT analyses.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1899-1909
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• 2015

Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.110-124
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• 2006

The multi scale wavelet-Galerkin method implemented in an adaptive manner has an advantage of obtaining accurate solutions with a substantially reduced number of interpolation points. The method is becoming popular, but its numerical efficiency still needs improvement. The objectives of this investigation are to present a new numerical scheme to improve the performance of the multi scale adaptive wavelet-Galerkin method and to give detailed implementation procedure. Specifically, the subdomain technique suitable for multiscale methods is developed and implemented. When the standard wavelet-Galerkin method is implemented without domain subdivision, the interaction between very long scale wavelets and very short scale wavelets leads to a poorly-sparse system matrix, which considerably worsens numerical efficiency for large-sized problems. The performance of the developed strategy is checked in terms of numerical costs such as the CPU time and memory size. Since the detailed implementation procedure including preprocessing and stiffness matrix construction is given, researchers having experiences in standard finite element implementation may be able to extend the multi scale method further or utilize some features of the multiscale method in their own applications.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.662-673
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• 2012

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• The Journal of Engineering Geology
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• v.27 no.4
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• pp.513-518
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• 2017

Groundroll is a coherent noise showing dispersive behavior in land seismic-reflection records and its rejection has been a stubborn problem in data processing because they severely degrade the continuities and resolution of reflection signals. Conventional processing schemes of attenuating noises are the kind of frequency filtering (i.e., bandpass and f-k) that uses the Fourier transform (FT) along the entire trace in the time domain. To suppress them in this study, 1D wavelet filtering (WT) that can control time-varying frequency properties is tested and performed in the land-based synthetic and field seismic data. The results are compared to the ones from conventional filtering techniques in terms of continuities and resolution of reflection events. This filtering technique enhanced the reflection events by effectively eliminating the dispersive groundroll and random noises with control of time-scale function on wavelet domain.