• IMMUNE NETWORK
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• v.9 no.6
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• pp.285-288
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• 2009

The expression pattern of immunoproteasomes in human thymus has not been analyzed but may have important consequences during thymic selection. Here we examined the expression patterns of immunoproteasome subunits in fetal and adult thymic tissues by immunohistochemistry and found that all three subunits are expressed in both cortical and medullary stromal cells. These data suggest that thymic selection in human can be affected by peptide repertoires generated by immunoproteasomes.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.94-99
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• 2008

In the pattern classification problem, feature selection is an important technique to improve performance of the classifiers. Particularly, in the case of classifying with a large number of features or variables, the accuracy of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. In this paper we propose a feature selection method using genetic algorithm and information theory. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.231-235
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• 2004

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

• BMB Reports
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• v.51 no.1
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• pp.1-2
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• 2018

Positive selection on a new beneficial mutation generates a characteristic pattern of DNA sequence polymorphism when it reaches an intermediate allele frequency. On genome sequences of African Drosophila melanogaster, we detected such signatures of selection at 37 candidate loci and identified "sweeping haplotypes (SHs)" that are increasing or have increased rapidly in frequency due to hitchhiking. Based on geographic distribution of SH frequencies, we could infer whether selective sweeps occurred starting from de novo beneficial mutants under simple constant selective pressure. Single SHs were identified at more than half of loci. However, at many other loci, we observed multiple independent SHs, implying soft selective sweeps due to a high beneficial mutation rate or parallel evolution across space. Interestingly, SH frequencies were intermediate across multiple populations at about a quarter of the loci despite relatively low migration rates inferred between African populations. This invokes a certain form of frequency-dependent selection such as heterozygote advantage. At one locus, we observed a complex pattern of multiple independent that was compatible with recurrent frequency-dependent positive selection on new variants. In conclusion, genomic patterns of positive selection are very diverse, with equal contributions of hard and soft sweeps and a surprisingly large proportion of frequency-dependent selection in D. melanogaster populations.

• Korea Journal of Hospital Management
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• v.12 no.4
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• pp.1-21
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• 2007

The relaxation of the regulation in selection of medical institution allows patients to use their own judgement in choosing proper institution for their diseases. Since the change of the regulation, there should have been many changes in medical institution selection behavior. The analysis of the change in disease specific selection pattern is critical because there be an optimal selection criteria that ensure the efficient and effective utilization of medical resources. This study analysis the institution selection factors by comparing the choice among the cases of acute diseases, the cases of chronic diseases, inpatient services, outpatient services, and emergency medical service. The comparisons performed in terms of size, class and other characteristics of medical institutions. For the study the nationally surveyed database was used and the data were analyzed using logistic regression procedure. The results indicates that the primary care facilities were not properly utilized. This study speculates that the reason for the undesirable pattern of utilization is that the roles of primary care facilities in the healthcare delivery system was not clearly defined. Based on the results, the medical policy implications are discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.502-507
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• 2009

We present a real-time photomosaics method for small image set optimized by feature selection method. Photomosaics is an image that is divided into cells (usually rectangular grids), each of which is replaced with another image of appropriate color, shape and texture pattern. This method needs large set of tile images which have various types of image pattern. But large amount of photo images requires high cost for pattern searching and large space for saving the images. These requirements can cause problems in the application to a real-time domain or mobile devices with limited resources. Our approach is a genetic feature selection method for building an optimized image set to accelerate pattern searching speed and minimize the memory cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Journal of Nutrition and Health
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• v.23 no.7
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• pp.504-512
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• 1990

In experiment 1, dietary self-selection of the 3 macronutrients, protein, fat, and carbohydrate were examined in male rats given 3 food cups of 80% carbohydrate, 80% protein, and 70% fat diets simultaneously. All the rats showed normal growth pattern and organ weight, which means they have ability to select just right kinds and amounts of nurients in order to support their growth and development. Mean values of caloric intake, body weight gain, serum lipid values and empty carcass compositions were not significantly differ between the upper and lower quartile groups of fat proportion of empty carcass compared to the lower quartile group(LF). Same feeding design was employed in experiment 2 where the effect of mild electric stress on food selection was studied. The rats in both control and electric stress group revealed a normal growth curve and organ weights. The rats in both control and electric stress group revealed a normal growth curve and organ weights. The stress group showed higher caloric intake and body weight gain than control group, but no significant effects of stress on serum and empty carcass components was found. Even though normal rats seemed to select macronutrients according to their physiolosical needs, there were individual differences in food selection whether they were exposed to stress or not. Therefore life long individual food selection pattern may have a great influence on nutritional status and chronic degenerative diseases of eldery, and on aging process.