• The Research Journal of the Costume Culture
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• v.16 no.5
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• pp.841-851
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• 2008

The purpose of the study was to examine the effect of motif categories and motif colors on evaluating sensory image of fashion materials with the gestalt theory as the background. The research was conducted on a quasi experimental basis, with subjects numbering 187 male and 207 female college students. Data were collected in the period from march 19th to march 31st, 2007. A set of fabric stimuli and semantic differential scales were developed. The stimuli were thirteen fabric species(each measuring 12 by 13cm). Variables included; (a) motif colour(white, grey, pink and blue) (b) motif categories(plain, paisley, flower, stripes and zebra effect). The semantic differential scale to measure sensory image of fabric stimuli included 23 sets of bi-polar adjectives. The data were analysed by factor analysis and ANOVA and the major finding were as follows. 1) Four sensory dimensions emerged of importance: salience, attractiveness, comfort and softness. 2) The motif category effected on the four sensory image dimensions while the motif colour effected on salience, comfort and softness sensory dimensions. 3) An interaction effect was founded between motif category and motif colour. 4) Motif category showed significant effects on the preference and liking of the fashion, however the motif colour did not show any significant effects on the preference and liking. As a whole the results supported the gestalt theory and the results can be used for the marketing strategy for developing fashion fabrics.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1062-1070
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• 1996

Concept of the comfort and fitness has become a major concern in the basic function of the ready-made clothes. Until now, ready-made clothes were not made by on the basis of the bodytype, but by the body size only. This research was performed to classify and characterize the bodytypes of Korean adult males. Sample size was 1290 subjects and their age range was from 19 to 54 years old. 15 variables from the photographic data of 1112 subjects were applied to analyse the bodytype of th\ulcorner lower part of trunk. Data were analyzed by the multivariate method, especially factor and cluster analysis. The groups forming a cluster can be subdivided into 5 sets by crosstabulation extracted by the hierarchical cluster analysis. 5 bodytypes classified by the photographic sources could be combined with the anthropcmetric data and were demonstrated with 5 silhouette. Type 2 and 3 in the lower part of trunk were dominant and were composed of the majority of 56.8% of the subjects. Bodytypes of Korean males were influenced by the degree of posture erectness and of curvature of the front side of the body in waist and abdomen.

• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.365-380
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• 2012

Non-negative matrix factorization (NMF) is a very efficient method to explain the relationship between functions for finding basis information of multivariate nonnegative data. The multiplicative update (MU) algorithm is a popular approach to solve the NMF problem, but it fails to approach a stationary point and has inner iteration and zero divisor. So the elementwisely alternating projected gradient (eAPG) algorithm was proposed to overcome the defects. In this paper, we use the fact that the equilibrium point is stable to prove the convergence of the eAPG algorithm. By using a classic model, the equilibrium point is obtained and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the eAPG algorithm are obtained, which can accelerate the convergence. In addition, the conditions, which satisfy that the non-zero equilibrium point exists and is stable, can cause that the algorithm converges to different values. Both of them are confirmed in the experiments. And we give the mathematical proof that the eAPG algorithm can reach the appointed precision at the least iterations compared to the MU algorithm. Thus, we theoretically illustrate the advantages of the eAPG algorithm.

• Journal of the Korean Chemical Society
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• v.42 no.2
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• pp.161-171
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• 1998

The geometrical parameters, vibrational frequencies, and IR intensities for primary ozonide (POZ), secondary ozonide (SOZ) and carbonyl oxide as the intermediates of alkene-ozone reaction have been predicted using high level ab initio quantum mechanical method with various basis sets. In general, the polarization function decreases bond lengths and bond angles, while the electron correlation effect increases bond lengths slightly. The electronic structure of carbonyl oxide has been predicted to be zwitterionic structure and energy difference between zwitterionic and diradical structure is evaluated to be 22.4 kcal/mol at TZ2P CISD level of theory. The experimental vibrational frequencies and IR intensities of POZ and SOZ will be compared and discussed with our high level theoretical predictions.

• Steel and Composite Structures
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• v.3 no.2
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• pp.123-140
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• 2003

The importance of the research on moment-resistant properties of unstiffened tubular joints and the research background are introduced. The performed experimental research on the bending rigidity and capacity of the joints is reported. The emphasis is put on the discussion of the flexural behavior of the joints including sets of geometrical parameters of the joints and several loading combinations. Procedures and results of loading tests on four full size joints in planar KK and X configuration are described in details at first. Mechanical models are proposed to analyze the joint specimens. Three-dimensional nonlinear FE models are established and verified with the experimental results. By comparing the experimental data with the results of the analysis, it is reported reasonable to carry out the structural analysis under the assumption that the joint is fully rigidly connected, and their bending capacities can assure the strength of the members connected under certain limitation. Furthermore, a parametric formula for inplane bengding rigidity of T and Y type tubular joints is proposed on the basis of FE calculation and regression analysis. Compared with test results, it is shown that the parametric formula developed in this paper has good applicability.

• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• International Journal of Concrete Structures and Materials
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• v.6 no.3
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• pp.177-186
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• 2012

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.63-68
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• 2005

Transcription factors regulate gene expression by binding to gene upstream region. Each transcription factor has the specific binding site in promoter region. So the analysis of gene upstream sequence is necessary for understanding regulatory mechanism of genes, under a plausible idea that assumption that DNA sequence motif profiles are closely related to gene expression behaviors of the corresponding genes. Here, we present an effective approach to the analysis of the relation between gene expression profiles and gene upstream sequences on the basis of kernel canonical correlation analysis (kernel CCA). Kernel CCA is a useful method for finding relationships underlying between two different data sets. In the application to a yeast cell cycle data set, it is shown that gene upstream sequence profile is closely related to gene expression patterns in terms of canonical correlation scores. By the further analysis of the contributing values or weights of sequence motifs in the construction of a pair of sequence motif profiles and expression profiles, we show that the proposed method can identify significant DNA sequence motifs involved with some specific gene expression patterns, including some well known motifs and those putative, in the process of the yeast cell cycle.