• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.209-211
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• 2000

SPIHT(Set Partitioning In Hierarchical Trees) 부호화 알고리즘은 EXW(Embedded Zerotree Wavelet) 부호와 알고리즘의 부호화 방법을 개선하여 압축 효율을 개선한 알고리즘이다. SPIHT 부호화 알고리즘은 웨이브렛 변환된 영상의 계수 값이 동일한 방향은 갖는 대역 사이에서 상관 관계를 갖는다는 점을 이용한다는 점에서 EZW 부호화 알고리즘과 동일하다. 그러나 zerotree의 부호화 부분에서 계수의 중요도에 따라 부분 집합으로 분할해 가는 과정과 분할된 계수들을 부호화하는 과정을 개선하였다. 이 부호화 과정에서의 significant map은 모든 threshold에 대해서 LSP(List of Insignificant Pixels), LIP(List of Insignificant), LIS(List of Insignificant Sets)의 세가지 리스트를 통하여 구하여 진다. 그리고, 전체 알고리즘은 초기화, Sorting pass, Refinement pass, 양자화 값 갱신의 네 가지 단계로 구성된다. 본 논문에서는 SPIHT 구현에 필요한 자료구조를 제안하고 SPIHT 부호화 모듈을 구현에 대하여 설명한다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.561-566
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• 2005

This paper introduces a compression technique for power qualify disturbance signal via discrete wavelet transform(DWT). The proposed approach is based on a previous estimation of the stationary component of power quality disturbance signal, so that it could be subtracted from the original signal in order to reduce a dynamic range of signal and generate transient events signal, which is subsequently applied to the compression technique. The compression techniques is performed through the difference signal decomposition, thresholding of wavelet coefficients, and signal reconstruction. It presents the relation between compression efficiency and threshold. It shouts that the wavelet transform leads to a power quality data compression approach with high compression efficiency, small compression error and good de-nosing effect.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.105-111
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of discrete wavelet transform to the various HIF data. These data were measured in actual 22-9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.1
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• pp.25-34
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• 2003

Gas insulated switchgear(GIS) is an important equipment in a substation. It is highly desirable to measure a partial discharge(PD) in GIS which is a symptom before insulation breakdown occurs. The issue is that the PD signal is weak and sensitive to external noise. In this paper, the algorithm for detection of PD in GIS using wavelet transform is proposed. The wavelet transform provides a direct quantitative measure of spectral content, "dynamic spectrum", in the time-frequency domain. The recommended mother wavelet is 'Daubechies' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. Through the procedure of wavelet transform, noise extraction and reconstruction, the signal is Analyzed to determine the magnitude of PD in GIS. In experimental results, we can know that partial discharge is exactly detected in combination of Dl and D2 using wavelet transform.transform.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1296-1302
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• 1999

In this paper, we propose an algorithms for the mounted PCB classification system using wavelet transform and ART2 neural network. The feature informations of a mounted PCB can be extracted from the coefficient matrix of wavelet transform adapted subband concept. As the preprocessing process, only the PCB area in the input image is extracted by histogram method and the feature vectors are composed of using wavelet transform method. These feature vectors are used as the input vector of ART2 neural network. In the experiment using 55 mounted PCB images, the proposed algorithm shows 100% classification rate at the vigilance parameter $\rho$=0.99. The proposed algorithm has some advantages of the feature extraction in the compressed domain and the simplification of processing steps.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.284-287
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• 2003

A wavelet transform which is presented as a new technique of signal processing field decompose input signals into subsignals for expressing them in different resolutions and into detail signals for expressing the remaining signals. And the signals obtained from the progress include the information about input signals at the same time and scale. And when two wavelet bases are designed to form Hilbert transform pair, wavelet Pair show superior performance than the existing DWT in data detection of pulse type. Therefore in this paper, we detected position of impulse noise by using two dyadic wavelet bases which are designed by truncated coefficient vector.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.102-105
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• 2003

Up to the present studies of the car number recognition system, it is generally known to have serious problems such as relatively long processing time due to the excessive length of data extracted from the number plate based on the current image characteristics, and the image blurring with the physical damage of the brightness and darkness signals of the number plate caused by external impulses with many difficulties in the extraction of the highlighted numbers. In this Paper we used the characteristics firstly having a constant brightness of number plate, and a high density to the horizontal axis, and the influences of highlighted signal could be reduced by making reflections less through the polarized filter on the camera for any highlighted signal. For the more, the data processing time and the noise reduction are effectively implemented by using the wavelet transform of time-space scale with the considerations on the physical loss and processing time.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2040-2042
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• 2003

In this paper, we propose a new method about wavelet-based fuzzy modeling using a DNA coding method. DNA coding techniques is known that expression of knowledge is various than Genetic Algorithm(GA) usually by made optimization technique because done base in structure of biologic DNA and optimization performance is superior. The reposed method make fuzzy system model in wavelet transform and equivalence relation after identification with coefficient of wavelet transform using a DNA coding techniques. Also, can get fuzzy model effectively of nonlinear system using advantage of strong wavelet transform about function that have sudden change. In this paper, in order to demonstrate the superiority of the proposed method compared with GA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2706-2713
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• 1997

The fast implementation algorithm of M-band discrete wavelet transform is propsed using the factorization of lossless matrix when the length of discrete orthogonal wavelet bases equals to 2M. In computational complexity when direct filtering method is employed, the number of multiplicationand addition is (2M$^{2}$) and (2M$^{2}$ -M), respectively. But by proposed algorithm, it can be reduced to (M$^{2}$+M) and (M$^{2}$+2M-1), respectively. and it is possible to reduce the compuatational complexity further when unitary matrix employed to design the discrete or thogonal wavelet basis has the fast algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.225-228
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• 1996

ECG data are used for the diagnostic purposes with many clinical situations, especially heart disease. In this paper, an efficient ECG data compression technique by wavelet transform and high-speed vector quantization on PMS-B algorithm is proposed. In general, ECG data compression techniques are divided into two categories: direct and transform methods. The direct data compression techniques are AZTEC, TP, CORTES, FAN and SAPA algorithms, besides the transform methods include K-L, Fourier, Walsh, and wavelet transforms. In this paper, we applied wavelet analysis to the ECG data. In particular, vector quantization on PMS-B algorithm to the wavelet coefficients in the higher frequency regions, but scalar quantized in the lower frequency regions by PCM. Finally, the quantized indices were compressed by LZW lossless entropy encoder. As the result of simulation, it turns out to get sufficient compression ratio while keeping clinically acceptable PRD.