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高昌林 慶北工業專門大學 1979 論文集 Vol.16 No.-
The magnitude approximation of low pass Butterworth is excellent at and neat ω=0, but poor at ω=??. And it possess excellent phase linearity. In this paper, by cascading second order low pass and band stop filter, the improvement of attenuation in the stop band is obtained. Experimental results are presented and discussed.
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高昌林 慶一大學校 1998 論文集 Vol.15 No.1
In this paper, Two aspects of pattern clustering process using ART neural network are studied and discussed. First, the relations between vigilance parameter values and the number of clustering centers are examined. It can be concluded that the number of cluster centers are not linearly increased for the linear increase of vigilance parameter(from 0.1 to 0.9) in clustering procedure. Second, for given vigilance parameter(0.95) with the ordered sequence of input patterns, the related informations of pattern clustering process are examined. In this procedure, if any of the stored rpototype patterns is similar to input pattern, then the input pattern is included to that cluster center with the prototype pattern is modified to the input pattern for the matching. Otherwise new cluster center is formed and the input pattern is included to that cluster. Moreover the result of pattern clustering process are varied on the order of input pattern sequence. Experimental results are obtained and discussed.
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高昌林 慶一大學校 1995 論文集 Vol.12 No.2
Two approaches to the initial probability for the image segmentation using the relaxation process are studied. The first initial probabilities are defined based on the image histogram. In this case, the convergence rate of label probability is fast and an image is segmented to the roughly segmented regions. Therefore this method is suitable for the large size general image. Another initial probability is derived from the local feature of an image. Although the convergence rate of pixel labeling is relatively slow, an image is segmented to feature regions with low segmentation error. Experimental results show that it is very useful for the medical image segamentation with the low contrast and small size area of interest.
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패턴벡터들간의 직교성에 따른 홉필드 신경망의 복원특성 : -잡음이 인가된 부분패턴의 경우- -For Noisy and Partial Test Pattern-
高昌林 慶一大學校 1999 論文集 Vol.16 No.5
Recall properties of the Hopfield neural network based on orthogonality among the exemplar pattern vectors with the partial and noisy test patterns are studied. The bitmap characters "KOREA" and four 10*10 bitmap patterns which are composed of 10 bits bar pattern combination are used as training patterns for the nonorthonormal and orthonormal case respectively. From the simulation results it is proved that nonorthonormal pattern case the recall of test pattern is not good regardless of shape and amount of noise to be added, and orthonormal pattern case exact recall of test pattern can be obtained regardless of shape and amount of nosie to be added.
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高昌林 慶一大學校 1997 論文集 Vol.14 No.2
The convergence properties of back propagation algorithm for learning constant and momentum rate, and its application to character recognition are studied. Combination of 0.1, 0.5, 0.9 of learning rate and momentum constant, convergence characteristics are obtained and analyzed. And using selected values of these parameters with adding small random noise to input patterns, character recognition using back propagation neural network is tested and verified. At present the choice of these parameters are non-trival problem, the results in this paper will provide the useful guidelines for the choice of these parameters in the applications of back propagation neural networks.
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高昌林 慶一大學校 1987 論文集 Vol.3 No.1
In this paper thresholding method via relaxation process is studied. In relaxation process initial probability for pixel classification is defined and the pixels are classified probabilistically. And then adjust the probabilities for each pixel based on its neighbors probabilities with light, dark, medium classes. When this adjustment process is iterated, the label probabilities are refined and the pixels are clustered in one of 3 classes so that thresholding becomes trivial with regardless of histogram distribution. Experimental results are presented and discussed on the ocular fundus fluorescein angiogram with low contrast and unimodal histogram distribution.
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高昌林 慶一大學校 1992 論文集 Vol.8 No.1
Digital image analysis for the ocular fluorescein angiogram with the low contrast and unimodal hisgram distribution and studied. The procedure are image segmentation, Correction, and the discrimination of the feature regions. Based on the relaxation process, thresholding by pixel clustering and the pixel classification through refined label probabilities are discussed. Moreover for the extraction of useful diagnostic information, correction via the registration of nonlinearly distorted images and discrimination of feature regions by its shape are studied.
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差分演算子를 利用한 頂點과 溪谷點 檢出 : 畵像의 階層的 分析技法
高昌林 慶北工業專門大學 1984 論文集 Vol.21 No.-
A new technique on peak and valley detection by difference operator using restamping is developed. The gray level difference matrices are constructed from the two dimensional data, and are used for detection of local peaks and valleys. This hierarchical image search technique based on restamping is computationally efficient method. There are no parameters to adjustment of the process. Effects of noise are minimized without altering the peaks and valleys points. The results of the process can be used as a description of region properties.
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高昌林 慶北工業專門大學 1983 論文集 Vol.20 No.-
The recursive least square lattice and the fast kalman algorithm are investigated in the view of geometrical structure and nature. Also, If the lattice filter order is fixed and only time updates are used, the predictor coefficients of fast kalman algorithm are determined via metric space ismorphism of input data and predictor error residual.
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高昌林 慶一大學校 1996 論文集 Vol.13 No.2
In this paper, the properties of SOFM process and the effects of neighborhood function with the width are studied. For the set of input vectors, the SOFM learns overall input vector distribution and clustering each set of patterns simultaneously, therefore it can create self-organizing feature map of input space topology. Within the number of output layer neurons, using the lager size width of neighborhood function can obtain more reasonable clustering results. By the use of the computer simulation, it is verified that these results are useful for the input vectors which consist of 4 sets and each set it composed of 5 patterns, with radius 2 and 3 of neighborhood function via the 10,000 training epochs.
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