• 한국통신학회논문지
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• 제25권7A호
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• pp.1028-1036
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• 2000

본 논문에서는 웨이블릿 변환을 이용한 오류에 강인한 영상 압축 기법을 제안하였다. 공간·주파수 영역에서 웨이블릿 계수들의 통계적 특성, 에너지 특성, 방향 특성을 이용한 제로트리 기법은 높은 압축 성능을 나타낸다. 하지만, 제로트리 부호와 기법은 부호에 따른 부호화 되는 계수의 수가 다르기 때문에 오류에 민감하게 반응하여 한 개의 오류가 전체 영상에 확산되어 영향을 미치게 된다. 제안 알고리듬에서는 SPIHT(Set Partitioning in Hierachical Trees) 알고리듬을 이용한 제로트리 기법으로 영상을 부호화한다. 그리고 부호화 계수들을 부밴드간 상관도를 이용하여 비트열을 다수의 블록으로 분리하고 비트 재구성 알고리듬을 이용하여 같은 크기의 블록으로 만든다. 이 과정으로 효율적인 비트 할당과 오류의 전파를 해당 블록으로 제한하여 오류가 없거나 적은 환경에서는 제로트리 압축 기법과 유사한 성능을 보이며 오류가 많은 환경에서는 제로트리 압축 기법 및 기존의 오류에 강인한 압축보다 더 효율적으로 부호화 할 수 있다.

• 대한조선학회논문집
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• 제50권4호
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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.

• 한국정보통신학회논문지
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• 제12권12호
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• pp.2323-2329
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• 2008

저가형 이미지 센서 기술의 발전과 무선 센서 네트워크 기술의 발전으로 인해 WMSN(Wireless Multimedia Sensor Networks) 기술이 활발히 연구되고 있다. WMSN은 기존의 무선 센서 네트워크 기술에 멀티미디어 컨텐츠를 센싱하고 전송 및 처리하는 기반기술을 포함한다. 멀티미디어 컨텐츠는 많은 데이터량을 가지므로 이를 처리하기 위해서는 많은 컴퓨팅 자원과 높은 네트워크 대역폭을 필요로 한다. 저사양의 센서 노드에서 멀티미디어 컨텐츠를 수용하기 위해서는 컴퓨팅 자원을 고려한 압축 기법 및 효율적인 전송에 대한 연구가 필요하다. 본 논문에서는 무선 센서 네트워크에서 이미지를 효율적으로 압축하고 전송하기 위한 압축기법인 YWCE기법을 제안한다. YWCE는 웨이블릿의 Resolution Scalability 특성을 이용한 4가지 움직임 보상/예측 기법을 도입한다. 실험을 통하여 4가지 각 압축 기법들의 조합에 따라 매우 높은 성능을 나타냄을 보였다.

• 비파괴검사학회지
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• 제21권2호
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• pp.152-162
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• 2001

비파괴 평가 기술들 중의 하나인 레이저 초음파 응용 기술은 각종 구조물에 존재하는 표면결함에 의한 신호를 통해 건전성을 평가하는 기법이다. 따라서 결함의 신뢰성 높은 정량적 평가를 위해서는 결함으로부터의 레이저 초음파 신호특성에 대한 기본적 이해가 필수적이며 따라서 이를 위한 신호해석 연구가 요구된다. 본 연구에서는 레이저유도 초음파에 의한 one-sided 기법을 이용하여 표면균열을 평가하고자 하였다. 하지만 레이저를 이용한 평가방법들은 수신된 신호의 해석이 까다로우며 또한 상당한 전문적인 지식이 요구된다. 웨이블렛 변환(wavelet transform, WT) 기법은 신호처리 분야에서 하나의 새로운 방법으로서 다양한 분야에 적용되고 있으며 특히 한 시점에 대한 주파수 분해가 가능한 신호처리 방법으로서 시간-주파수 분석에 아주 유용하게 이용되고 있다. 본 연구에서는 이러한 레이저 유도 초음파를 이용하여 재료의 표면 결함신호들에 대한 웨이블렛 변환기법을 적용하여 보다 정량적인 결함 크기를 예측하고 그 타당성을 평가하고자 하였다.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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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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• 제20권1호
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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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• 제6권5호
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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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• 제36권5호
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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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• 제8권5호
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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.

• 지질공학
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• 제27권4호
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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.