• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• Journal of the Korean Society of Clothing and Textiles
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• v.11 no.2 s.24
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• pp.1-11
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• 1987

The purpose of this study was to develope a questionaire measuring clothing behavior of elementary school children. At first, after pretest, the clothing behavior questionaire consisted of 70 items which were devidad. into 7 subscales i.e. Clothing interest. Clothing satisfaction. Clothing management, Clothing sex-role. Clothing comfort. Clothing conformity. and Clothing independence. Each item was rated on a 3-point scale. Samples were 447 boys and girls (4 th, 5 th, 6 th grade) of three elementary schools in Seoul. Korea. The data were analyzed by item analysis and factor analysis. Factor analysis was useful in attempting to establish contruct validity. Item validity was examined based on the correlation coefficients and item discriminating power through the chi-square. Reliability was examined based on the Cronbach's Alpha Reliability Coefficient and test-retest method. With this analysis the subscales were reconstructed to following 6 factors. Clothing conformity items were not clustered by the factor analysis. 52 items of 6 factors selected by the analysis. The factors characteristics were as follows: 1. Clothing interest (10 items) 2. Clothing satisfaction (11 items) 3. Clothing management (8 items) 4. Clothing sex-role (12 items) 5. Clothing comfort (6 items) 6. Clothing independence (5 items)

• Journal of Welding and Joining
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• v.16 no.4
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• pp.125-135
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• 1998

The shielded metal arc welding (SMAW) by AC power source was performed to evaluate the arc stability by arc monitoring and analysing. In this study, the arc stability index was evaluated quantitatively by using he coefficient of resistance variation for welding time. This coefficient was obtained for the long time (20sec.) by analysing the waveforms of welding current, voltage and resistance. The coefficient was applied to indicate numerically the variation level of arc length and the degree of arc extinction. Using the coefficient of resistance variation in practical welding, the arc stability of the high titanium oxide electrode (KS E4313) turned out to be better than that of the low hydrogen electrode (KS E4316). In evaluating the skill level of welders by the coefficient, the horizontal fillet weaving welding became clear to be very discriminating because the higher level welder could weave in keeping constant arc length, but the lower level welder showed the characteristics of weaving with the unstable arc length. And it was confirmed that the welding defects as blow holes was formed when the arc stability index were high.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.72-80
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• 2006

In this paper, we propose a new descriptor for 3D model retrieval based on shape information. The proposed method consists of two steps including ray casting method and spherical harmonic function, considering geometric properties of model. In the ray casting method, an adaptive sampling is performed for external shape information. By increasing shape information included in the descriptor, we improve the discriminating power of the proposed descriptor. The coefficients of spherical harmonic function are adaptively calculated, considering geometric frequency characteristics. This makes the descriptor more compact and concise without decreasing the retrieval performance. By combining two methods, we achieve more improved retrieval results.

• Tuberculosis and Respiratory Diseases
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• v.76 no.2
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• pp.59-65
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• 2014

Background: Variable-number tandem repeat (VNTR) typing is a promising method to discriminate the Mycobacterium tuberculosis isolates in molecular epidemiology. The purpose of this study is to determine the optimal VNTR combinations for discriminating isolated M. tuberculosis strains in Korea. Methods: A total of 317 clinical isolates collected throughout Korea were genotyped by using the IS6110 restriction fragment length polymorphism (RFLP), and then analysed for the number of VNTR copies from 32 VNTR loci. Results: The results of discriminatory power according to diverse combinations were as follows: 25 clusters in 83 strains were yielded from the internationally standardized 15 VNTR loci (Hunter-Gaston discriminatory index [HGDI], 0.9958), 25 clusters in 65 strains by using IS6110 RFLP (HGDI, 0.9977), 14 clusters in 32 strains in 12 hyper-variable VNTR loci (HGDI, 0.9995), 6 clusters in 13 strains in 32 VNTR loci (HDGI, 0.9998), and 7 clusters in 14 strains of both the 12 hyper-variable VNTR and IS6110 RFLP (HDGI, 0.9999). Conclusion: The combination of 12 hyper-variable VNTR typing can be an effective tool for genotyping Korean M. tuberculosis isolates where the Beijing strains are predominant.

• Research in Mathematical Education
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• v.7 no.3
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• pp.163-189
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• 2003

The purpose of this study is to develop a test, which can be used in creative problem solving ability in mathematics of the mathematically gifted and the regular students. This test tool is composed of three categories; fluency (number of responses), flexibility (number of different kinds of responses), and originality (degree of uniqueness of responses) which are the factors of the creativity. After applying to 462 middle school students, this test was analyzed into item analysis. As a results of item analysis, it turned out to be meaningful (reliability: 0.80, validity: item 1(1.05), item 2(1.10), item 3(0.85), item 4(0.90), item 5(1.08), item difficulty: item 1(-0.22), item 2(-0.41), item 3(0.23), item 4(0.40), item 5(-0.01), item discriminating power: item 1(0.73), item 2(0.73), item 3(0.67), item 4(0.51), item 5(0.56), over the level of a standard basis. This means that the test tool was useful in the test process of creative problem solving ability in mathematics

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.196-204
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• 2008

In this paper, we propose an efficient method to classify human facial impression using frontal face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. PCA is used to project the feature space to a low dimensional subspace. LDA produces well separated classes in a low dimensional subspace even under severe variation. This results in good discriminating power for classification. SVM is used to classify the data. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.

• Korean Journal of Childcare and Education
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• v.15 no.3
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• pp.175-201
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• 2019

Objective: This study aims to develop and validate the musicality level of an individual child, based on the on performance tasks rubrics. Methods: The survey was conducted on 284 children(ages 3-5years old from kindergartens and day care centers), their parents, and their 51 teachers. The collected data were calculated and analyzed using SPSS 22.0 and AMOS 22.0. Results: Consisted of two components, two task types, 17 performance tasks, and 41 items in three dimensions. Rubrics were determined and based on the child's best performance, and categorized into five levels. Lastly, the item difficulty and item discriminating power were defined in order to comprehend the item quality analysis, which showed that average scores varied depending on the performance. Conclusion/Implications: The musicality rating scale for young children is significant in order to comprehend musicality levels through the performances of children aged three to five. This study has educational implications in that teachers can connect the results of the ratings to curriculum and promote the development of teaching and learning methodologies based on the musicality levels of individual children.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.