• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• The Journal of the Korean Society for Microbiology
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• v.35 no.1
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• pp.31-40
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• 2000

Bacillus anthracis, the etiological agent of anthrax has been classified into the Bacillus subgroup I with B. cereus, B. mycoides and B. thuringiensis based on morphological and DNA similarity. DNA studies have further indicated that these species have very AT-rich genomes and high homology, indeed it has been proposed that these four sub-species be recognized as members of the one species. Several methods have been developed to obtain good differentiation between these species. However, none of these methods provides the means for an absolutely correct differntiation. The analysis of fatty acid methyl esters (FAMEs) was employed as a quick, simple and reliable method for the identification of 21 B. anthracis strains and closley related strains. The most significant differences were found between B. anthracis and B. anthracis closely related strains in FAMEs profiles. All tested strains of B. anthracis had a branched fatty acid C17:1 Anteiso A, whereas the fraction of unsaturated fatty acid Iso C17:1 w10c was found in B. anthracis closely related strains. By UPGMA clustering analysis of FAMEs profiles, all of the tested strains were classified into two clusters defined at Euclidian distance value of 24.5. The tested strains of B. anthracis were clustered together including Bacillus sp. Kyungjoo 3. However, the isolates of B. anthracis closely related spp. Rho, S10A, 11R1, CAU9910, CAU9911, CAU9912 and CAU9913 were clustered with the other group. On the basis of these results, isolates of B. anthracis Bongchon, Kyungjoo 1, 2 and Bacillus sp. Kyungjoo 3 were reclassified as a B. anthracis. It is concluded that FAMEs analysis provides a sensitive and reliable method for the identification of B. anthracis from closely related taxa.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.