• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.141-164
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• 2018

Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However, in Korea, there is no academic database in which such information is provided. In this paper, we propose an automatic classification system that can classify domestic academic literature into multiple classes. To this end, first, academic documents in the technical science field described in Korean were collected and mapped according to class 600 of the DDC by using K-Means clustering technique to construct a learning set capable of multiple classification. As a result of the construction of the training set, 63,915 documents in the Korean technical science field were established except for the values in which metadata does not exist. Using this training set, we implemented and learned the automatic classification engine of academic documents based on deep learning. Experimental results obtained by hand-built experimental set-up showed 78.32% accuracy and 72.45% F1 performance for multiple classification.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1125-1137
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• 1996

The author reviewed seventy papers on social psychology of clothing and fashion marketing fields, which were published in the Journal of the Korean Society of Clothing and Textiles between 1983 and 1996. The market variables and consumer behavior variables were focused on. This review showed that the market variables had been divided into three groups of variables: 1) product variables (product image and product classification): 2) brand variables (brand image and brand positioning): and 3) store variables (store image, store type, and distribution system) Consumer behavior variables have been studied on the basis of EBM Consumer Behavior Model: 1) purchasing motivation as need recognition: 2) information using as search information: 3) evaluation criteria and choice criteria as alternative evaluatioin : 4) clothing purchase, brand choice and store choice as purchase: 5) degree of wear, satisfaction and dissatisfaction as outcome: and 6) clothing discard. Variables that influence on consumer behavior, including situation variables, clothing attitude variables, personal . social variables were added to develop a variable model of clothing consumer behavior using the EBM Consumer Behavior Model.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.9-26
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• 2001

In some previous papers the author extended two algorithms proposed by Z. Kovarik for approximate orthogonalization of a finite set of linearly independent vectors from a Hibert space, to the case when the vectors are rows (not necessary linearly independent) of an arbitrary rectangular matrix. In this paper we describe combinations between these two methods and the classical Kaczmarz’s iteration. We prove that, in the case of a consistent least-squares problem, the new algorithms so obtained converge ti any of its solutions (depending on the initial approximation). The numerical experiments described in the last section of the paper on a problem obtained after the discretization of a first kind integral equation ilustrate the fast convergence of the new algorithms. AMS Mathematics Subject Classification : 65F10, 65F20.

• Korean Journal of Microbiology
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• v.23 no.3
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• pp.235-240
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• 1985

A total of 10 main characters of 16 species of family Rhodospirllaceae were phenetically and cladistically analyzed by Farris' method. The obtained phenogram and cladistic tree were compared with Bergey's manual and other papers. The results supported that the system of 5 subgroups (genera) is available in family Rhodospirllaceae and indicated that close affinities between Rhodospirllum tenue and Rhodopseudomonas gelatinosa and between Rhodomicrobium vannielii and other species of genus Rhodopseudomonas were proved.

• School Mathematics
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• v.4 no.3
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• pp.513-537
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• 2002

The aim of this study is to investigate the purpose of the performance assess-ment, to develop and to apply it's task. By reviewing previous study, I conclude that the performance assessment is sug-gested to evaluate mathematical thinking and attitude in the purpose of school mathematics. To develop the task to fit the purpose of the performance assessment, I refer to the middle and high level in Van den Heuvel's classification the task "Find the number in the star" and about the assess-ment of school mathematics. Then I apply the performance assessment task developed according to this level, analyse the responses of children to "Let's make the problem" and suggest it's assessment rubric and anchor papers for each level for illustrating the process of developing a rubric. Finally, considerations to improve the performance assessment are discussed.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.1-14
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• 2014

With a steep increase of the global disaster relief efforts around the world, the relief supply chain and humanitarian logistics play an important role to address this issue. A broad overview of operations research ranges from a principle or conceptual framework to analytical methodology and case study applied in this field. In this paper, we provide an overview of this challenging research area with emphasis on the corresponding optimization problems. The scope of this study begins with classification by the stage of the disaster lifecycle system. The characteristics of each optimization problem for the disaster supply chain are considered in detail as well as the logistics features. We found that the papers related to disaster relief can be grouped in three aspects in terms of logistics attributes: facility location, distribution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms proposed in each optimization model in order to discover insights, research gaps and findings. Finally, we offer future research directions based on our findings from the investigation of literature review.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• International Journal of Contents
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• v.12 no.3
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• pp.54-58
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• 2016

In this study, articles of the science and technology field that had been monitored for the period from 2002 to 2013 using GTB (Global Trends Briefing) were analyzed. Specifically, the VOSviewer was used to analyze the annual science and technology trends by keyword and the science and technology standard-classification information indicated in the GTB articles, and the convergence trends were therefore monitored. The findings of this study show that active studies were under way in the fields of material science and new and renewable energy, and that convergence has progressed. This result indicates that the information of the articles on papers and patents is more reliable, as it can reflect the current trends more rapidly in the science and technology field than the paper information or the patent information that is traditionally used in analyses of science and technology information.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.