• Asian-Australasian Journal of Animal Sciences
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• v.13 no.2
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• pp.137-148
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• 2000

This paper is based on the 9 goat colonies along the middle and lower Yellow River valley and 7 local goat colonies in the Northeast, Tibet and the Yangtze valley. After collecting the same data about the 22 goat colonies in China and other countries, it establishes and composes the matrix of fuzzy similarity relation describing the genetic similarities of different colonies. It also clusters 38 colonies according to their phylogenetic relationship. The establishment of the matrix and the cluster are effected in terms of the frequency of 18 loci and 43 allelomorphs in blood enzyme and other protein variations. The study proves that the middle Yellow River valley is one of the taming and disseminating centers of domestic goats in the South and East of Central Asia. Compared with other goat populations in this vast area, the native goat populations in the west of Mongolian Plateau, the Qinghai-Tibet Plateau and the middle Yellow River valley share the same origin. The colonies in the lower Yellow River valley and those in the middle valley, however, are relatively remote in their phylogenetic relationship. The native goat colonies in the southeast of Central Asia can be classified into two genetic groups: "East Asia" and "South Asia" and the colonies in Southeast Asia belong to either group.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1302-1309
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• 2008

Traditional clustering methods, like k-means or fuzzy clustering, are prototype-based methods which are applicable only to convex clusters. On the other hand, spectral clustering tries to find clusters only using local similarity information. Its ability to handle concave clusters has gained the popularity recent years together with support vector machine (SVM) which is a kernel-based classification method. However, as is in SVM, the kernel width plays an important role and has a great impact on the result. Several methods are proposed to decide it automatically, it is still determined based on heuristics. In this paper, we proposed an adaptive method deciding the kernel width based on distance histogram. The proposed method is motivated by the fact that the affinity matrix should be formed into a block diagonal matrix to generate the best result. We use the tradition Euclidean distance together with the random walk distance, which make it possible to form a more apparent block diagonal affinity matrix. Experimental results show that the proposed method generates more clear block structured affinity matrix than the existing one does.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2757-2763
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• 2015

p-Snake is an energy minimizing algorithm that applies an additional prototype energy to the existing Active Contour Model and is used to extract the contour line in the area where the edge information is unclear. In this paper suggested the creation of a prototype energy field that applies a variable prototype expressed as a combination of circle and straight line primitives, and a fudge function, to improve p-Snake's contour extraction performance. The prototype was defined based on the parts codes entered and the appropriate initial contour was extracted in each primitive zones acquired from the pre-processing process. Then, the primitives variably adjusted to create the prototype and the contour probability based on the distance to the prototype was calculated through the fuzzy function to create the prototype energy field. This was applied to p-Snake to extract the contour from 100 images acquired from various small parts and compared its similarity with the prototype to find that p-Snake made with the adaptive prototype was about 4.6% more precise than the existing Snake method.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.162-173
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• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.

• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.71-80
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• 2006

In web search engines, there are two main methods: directory searching and keyword searching. Keyword searching shows high recall rate but tends to come up with too many search results to find which users want to see the pages. Directory searching has also a difficulty to find the pages that users want in case of selecting improper category without knowing the exact category, that is, it shows high precision rates but low recall rates. We designed and implemented a new web search engine to resolve the problems of directory search method. It regards a category as a fuzzy set which contains keywords and calculate the degree of inclusion between categories. The merit of this method is to enhance the recall rate of directory searching by expanding subcategories on the basis of similarity.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.