• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.2
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• pp.263-275
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• 1992

This study intends to provide strategic positioning of brand image analysed from the view point of perceptual dimensions of clothing consumers. Consumers are segmented on the basis of the attributes of brand image, and in each segment, perceptual map is composed according to multidimensional scaling. The results are as follows; 1. According to the Benefit Segmentation, it is statistically significant that the consumers are divided into 'product-factor oriented group 'and' image-factor oriented group'. 2. From the analysis of perceptual map upon the 'similarity of brand image,'image-factor oriented group 'perceives more differently than 'product-factor oriented group' 3. From the analysis of perceptual map with the evaluation of attributes of brand image, price, promotion and design are significant determinants in 'total consumer group'. In addition, store image is significant determinant in' image-factor oriented group' and quality is significant determinant in' product-factor oriented group'. According to the evaluation of consumers on 8 brands with determining attribute-vector, ranks of brands in each segment are similar in the vector of price and promotion but different in the vector of design between segment groups. 4. From the analysis of perceptual map upon the preference of brand image, the distribution of preference and position of ideal point are different between segment groups. 5. With evaluation of purchase habit, statistically significant differences are found between groups segmented in the degree of importance of attributes, purchasing motive, purchasing time, sources of information and expenses for clothes.

• Korean Journal of Microbiology
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• v.29 no.1
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• pp.16-24
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• 1991

The yeast gene THR1 encodes the homoserine kinase (EC 2.7.1.39: HKase) which catalyses the first step of the threonine specific arm at the end of the common pathway for methionine and threonine biosynthesis. A recombinant plasmid pMC3 (12.6 kilobase pairs, vector YCp50) has been cloned into E. coli HB101 from a yeast genomic library through its complementing activity of a thr1 mutation in a yeast recipient strain M39-1D. When subcloned into pMC32 (8.6kbp, vector YRp7) and pMC35 (8.3 kbp, vector YIp5), the HindIII fragment (2.7 kbp) of pMC3 insery was positive in the thrI complementing activity in both yeast and E. coli auxotrophic strains. The linearized pMC35 was introduced into the original recipient yeast strain and the mitotically stable chromosomal integrant was identified among the transformants. Through the tetrad analysis, the integration site of the pMC35 was localized to the region of THR1 structural gene at an expected genetic distance of approximately 11.1 cM from the ARG4 locus on the right arm of the yeast chromosome VIII. When episomically introduced into the auxotrophic cells and cultured in Thr omission liquid medium, the cloned gene overexpressed the HKase in the order of thirteen to fifteenfold, as compared with a wildtype. HKase levels are repressed by addition of threonine at the amount of 300 mg/l and 1, 190 mg/l for pMC32 and pMC3, respectively. Data from genetic analysis and HKase response thus support that the cloned HindIII yeast DNA fragment contains the yeast thr1 structural gene, along with necessary regulatory components for control of its proper expression.

• Korean Journal of Agricultural Science
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• v.28 no.2
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• pp.92-98
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• 2001

Lactofenicin is an antibacterial peptide fragment (about 5 kD) derived from lactoferrin (80 kD) that displays the various biological functions. The production of a human lactoferricin (Lactoferricin H) in mouse HC11 mammary epithelial cells was achieved by placing its cDNA under the control of the bovine ${\beta}$-casein gene. To express lactoferricin H in this cell culture system, constructed a hybride-splice signal consisting of bovine ${\beta}$-casein intron I and rabbit ${\beta}$-globin intron II, and a DNA fragment spanning intron 8 of the bovine ${\beta}$-casein gene. Expression of lactofenicin H from this expression vector was identified by RT-PCR, northern and dot blot analysis. RT-PCR using total RNA of HC11 cells transfected with pBL1-cin expression vector yielded a product identified as having a size of the 150bp. Northern blot analysis was identified about 2.3 kb. In dot blot analysis, recombinant lactofenicin H was recognized with anti-human lactofrrnin polyclonal antibody.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.164-173
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• 2013

In this paper, we propose multi-frame based super resolution algorithm by using motion vector normalization and edge pattern analysis. Existing algorithms have constraints of sub-pixel motion and global translation between frames. Thus, applying of algorithms is limited. And single-frame based super resolution algorithm by using discrete wavelet transform which robust to these problems is proposed but it has another problem that quantity of information for interpolation is limited. To solve these problems, we propose motion vector normalization and edge pattern analysis for 2*2 block motion estimation. The experimental results show that the proposed algorithm has better performance than other conventional algorithms.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.1-10
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• 2012

In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved through experiments using internet news classification with low features.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.15-17
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• 2002

In this paper, we analyse the dynamic characteristic of 3-phase line start permanent magnet motor considering magnetization. Magnetization vector of NdFeB is obtained from the 2-D FEM magnetization analysis. And comparing the proposed analysis with conventional analysis method, we know that it is necessary to consider magnetization in dynamic analysis.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.496-498
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• 2003

Signal is composed of the independent components that can describe itself. These components can distinguish itself from any other signals and be extracted by analysis itself. This algorithm is called Independent Component Analysis (ICA) and image signal is considered as linear combination of independent components and features that is the weighted vector of independent component. This algorithm is already used in order to extract the good feature for image classification and very effective In this paper, we'll explain the method of stereo matching using independent component analysis and show the experimental result.