• Journal of the Korean Society for Library and Information Science
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• v.40 no.4
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• pp.333-366
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• 2006

The purpose of this study is principle and theory of main class in a Library Classification. According to Sayers, 'The foundation of the library is the book; the foundation of librarianship is classification.' We looked at the between scientific and bibliographic classification, and at the fact that bibliographic scheme is usually an aspect classification. That is to say, the organization of topics is based on areas or activity and the first division of the scheme is into disciplines or subject domains. This first division of classification creates what are called main class. The sequence of main classes is also important. A rough definition of a amin class is that it corresponds to a sin91e notational character. Main classes usually equivalent to traditional disciplines. What constitutes a main class will vary from one classification to another. The order in which the main classes are listed is often discussed at the theoretical level, and some orders are considered to be better than others.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.417-423
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• 2001

This paper proposes a technique for fault detection and classification for both LIF(Low Impedance Fault)s and HIF(High Impedance Fault)s using Adaptive Network-based Fuzzy Inference System(ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square(RMS) values of 3-phase currents and zero sequence current. The performance of the proposed technique is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classily faults including (LIFs and HIFs) accurately within half a cycle.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• Biomedical Science Letters
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• v.23 no.3
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• pp.223-229
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• 2017

Fungal infections by human pathogenic fungi are increasing globally in elderly, children and immune suppressed or deficient patients. Aspergillus fumigatus is one of the well-known pathogenic fungi and causes aspergilloses in human world widely. However, current identification and classification methods based on its phenotypic characteristics still have limitations. Therefore, currently, molecular biological tools using their DNA sequences are used for genotype identification and classification. In the present study, in order to analyze genetic variations of A. fumigatus clinical isolates, a total of six housekeeping genes were amplified by PCR using specific primer pairs and multi-locus sequence typing (MLST) assay. Results from phylogenetic tree analysis showed that most A. fumigatus strains (88.9%) from respiratory specimens were classified into cluster A and B, and approximately half of A. fumigatus strains (46%) from non-respiratory specimens were classified into cluster C and D. Although the sample size was limited, genetic characteristics of A. fumigatus clinical isolates according to their origins were very similar and well-correlated with other clinical data.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.6
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• pp.650-662
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• 1989

In this paper, a new algorithm for feature extraction and classification of recognizing Hanguel patterns is proposed. Inputed patterns classify into six basic formal patterns and divided into subregion of Hanguel phoneme and extract the crook feature from position information of the each subregion. Hanguel patterns are defined and are made of the indexed-sequence file using these crook features points. Hanguel patterns are recognized by retrievignt ehses two files such as feature indexed-sequence file and standard dictionary file. Thi paper show that the algorithm is very simple and easily construct the software system. Experimental result presents the output of feature extraction and grouping of input patterns. Proposed algorithm extract the crooked feature using distance transformation method within the rectangle of enclosure the characters. That uses the informationof relative position feature. It represents the 97% of recognition ratio.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.165-170
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• 1998

This paper presents a new prediction method RBP region-based prediction model where the context used for prediction contains regions instead of individual pixels. There is a meaningful property that RBP can partition a cartoon image into two distinctive types of regions, one containing full-color backgrounds and the other containing boundaries, edges and home-chromatic areas. With the development of computer techniques, synthetic images created with CG (computer graphics) becomes attactive. Like the demand on data compression, it is imperative to efficiently compress synthetic images such as cartoon animation generated with CG for storage of finite capacity and transmission of narrow bandwidth. This paper a lossy compression method to full-color regions and a lossless compression method to homo-chromatic and boundaries regions. Two criteria for partitioning are described, constant criterion and variable criterion. The latter criterion, in form of a linear function, gives the different threshold for classification in terms of contents of the image of interest. We carry out experiments by applying our method to a sequence of cartoon animation. We carry out experiments by applying our method to a sequence of cartoon animation. Compared with the available image compression standard MPEG-1, our method gives the superior results in both compression ratio and complexity.