• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.57-60
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• 2001

In this paper, we propose growing algorithm of wavelet neural network. It is growing algorithm that adds hidden nodes using wavelet frame which approximately supports orthogonality in wavelet neural network based on wavelet theory. The result of this processing can be reduced global error and progresses performance efficiency of wavelet neural network. We apply the proposed algorithm to approximation problem and evaluate effectiveness of proposed algorithm.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.15-168
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• 2001

In this paper, we propose growing algorithm of wavelet neural network. It is growing algorithm that adds hidden nodes using wavelet frame which approximately supports orthogonality in wavelet neural network based on wavelet theory. The result of this processing can be reduced global error and progresses performance efficiency of wavelet neural network. We apply the proposed algorithm to approximation problem and evaluate effectiveness of proposed algorithm.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.224-227
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• 2004

From 1980's, the DWT(Discrete Wavelet Transform) is applied to the data/image processing. Many people use the DWT in remote sensing for diversity purposes and they are satisfied with the wavelet theory. Though the algorithm for wavelet is very diverse, many people use the standard wavelet such as Daubechies D4 wavelet and biorthogonal 9/7 wavelet. We will overview the wavelet theory for discrete form which can be applied to the image processing. First, we will introduce the basic DWT algorithm and review the wavelet algorithm: EZW (Embedded Zerotree Wavelet), SPIHT(Set Partitioning in Hierarchical Trees), Lifting scheme, Curvelet, etc. Finally, we will suggest the properties of wavelet algorithm; and wavelet filter for each image processing in remote sensing.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.124-130
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• 2006

A wavelet-based image compression system allowing both lossless and lossy image compression is proposed in this paper. The proposed algorithm consists of the two stages. The first stage uses the wavelet packet transform and the quad-tree coding scheme for the lossy compression. In the second stage, the residue image taken between the original image and the lossy reconstruction image is coded for the lossless image compression by using the integer wavelet transform and the context based predictive technique with feedback error. The proposed wavelet-based algorithm, allowing an optional lossless reconstruction of a given image, transmits progressively image materials and chooses an appropriate wavelet filter in each stage. The lossy compression result of the proposed algorithm improves up to the maximum 1 dB PSNR performance of the high frequency image, compared to that of JPEG-2000 algorithm and that of S+P algorithm. In addition, the lossless compression result of the proposed algorithm improves up to the maximum 0.39 compression rates of the high frequency image, compared to that of the existing algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.247-250
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• 2001

In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can constructs wavelet neural network according to one's intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• 한국멀티미디어학회논문지
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• 제8권8호
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• pp.1051-1056
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• 2005

적응신호처리 분야에서 LMS 알고리즘은 수식이 간단하고, 적은 계산량으로 인해 널리 사용되고 있지만, 시간영역의 적응 알고리즘은 입력신호의 고유치 분포폭이 넓게 분포할 때는 수렴속도가 느려지는 단점이 있다. 또한 알고리즘의 성능을 좌우하는 고정된 적응상수를 적절하게 선택해야만 알고리즘이 수렴할 수 있다. 이런 문제점을 개선하기 위하여 본 논문에서는 시간영역의 적응알고리즘을 변환영역인 웨이블렛 변환에서 적응알고리즘을 적용한다. 그리고 안정되고 빠른 수렴을 위해 고정된 적응상수를 오차신호의 순시치 절대값에 비례하여 각 반복구간마다 변화시키는 가변스텝사이즈를 갖는 웨이블렛 기반 적응알고리즘을 제안, 적응 잡음제거기에 적용하여 기존의 알고리즘과 비교하여 그 성능이 우수함을 입증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.89-92
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• 2000

In this paper, an effective image restoration using Genetic Algorithm(GA) in wavelet transform region is proposed. First, a wavelet transform is used for decomposition of a blurred image with white Gaussian noise as a preprocessing of the proposed method. The wavelet transform decomposes a degraded image into a wavelet subband coefficient planes. In this wavelet transformed subband coefficient planes, three highest subbands is composed entirely of noise elements on a degraded image. So, these subbands are removed. And remained subbands except for the lowest subband are individually applied to GA. For the performance evaluation, the proposed method is compared with a conventional single GA algorithm and a conventional hybrid method of wavelet transform and GA for a Lenna image and a boat image. As an experimental result, the proposed algorithm is prior to a conventional methods as each PSNR 3.4dB, 1.3dB.

• 대한전기학회논문지:전력기술부문A
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• 제49권8호
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• pp.369-373
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• 2000

High impedance fault(HIF) is defined as a fault that the general overcurrent relay can not detect or interrupt. Especially when HIF occurs in residential areas, energized high voltage conductor results in fire hazard, equipment damage or personal threat. This paper proposes a fault location estimation algorithm for high impedance fault using wavelet transform. The algorithm is based on the wavelet analysis of the fault voltage and current signals. The performance of the proposed algorithm is tested on a typical 154kV korean transmission line system under various fault conditions. From the tests presented in this paper it can be concluded that a fault location estimation algorithm using wavelet transform can precisely calculate the fault point for HIF.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.115-118
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• 2000

In order to approximate a nonlinear function, modular wavelet networks combining wavelet theory and modular concept based on single layer neural network have been proposed as an alternative to conventional wavelet neural networks and kind of modular network. Modular wavelet networks provide better approximating performance than conventional one. In this paper, we propose an effective method to construct an optimal modualr wavelet network using genetic algorithm. This is verified through experimental results.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.109-115
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• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.