• Korean Journal of Biological Psychiatry
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• v.2 no.1
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• pp.115-122
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• 1995

This study was performed to know the relationship between neurologic soft signs (NSS) and clinical variables such as psychopathology. history of illness, and premorbid social adjustment in patients with schizophrenia. The authors evaluated NSS in 31 patients with schizophrenia using the structured tool for measuring neurologic abnormalities, Neurological Evaluation Scale- Korean Version(NES-K). Relationships between NSS and clinical variables such as duration of illness, intensity of precipitating stressors, duration of outpatient treatment, schooling, peer relationship, total duration of unemployment, total days of psychiatric admission, age, total days of being medicated, age at the first psychiatric admission, frequency of admissions, content of treatment, social adjustment, and severity of symptoms were analyzed. Differences between paranoid and non-paranoid schizophrenics were examined. In addition, Differences between patients with schizophrenia who have predominant positive symptoms and who have predominant negative symptoms were examined too. Total scores of NES-K were correlated with lower schooling (${\gamma}$=0.44, p<0.01). Scores of motor coordination subcategory were correlated with poor peer relationship(${\gamma}$=0.67, p<0.001). Other clinical variables were not correlated with any scores of NES-K. Paranoid and non-paranoid schizophrenics were not different in scores of NES-K. Also positive and negative schizophrenics were not different in scores of NES-K. Most clinical variables except schooling and peer relationship were not related with NSS. This results indicated that the meaning of these signs was not fully be understood. Introduction of the new classification concepts such as deficit or non-deficit syndrome will be helpful to elucidate the meaning of NSS in patients with schizophrenia.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.179-190
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• 2009

In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.82-96
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• 2019

A semi-submersible structure has been widely used for offshore drilling and production of oil and gas. The small water plane area makes the structure very sensitive to weight increase in terms of payload and stability. Therefore, it is necessary to lighten the substructure from the early design stage. This study aims at an optimization of hull structure based on a sophisticated yield and buckling strength in accordance with classification rules. An in-house strength assessment system is developed to automate the procedure such as a generation of buckling panels, a collection of required panel information, automatic buckling and yield check and so on. The developed system enables an automatic yield and buckling strength check of all panels composing the hull structure at each iteration of the optimization. Design variables are plate thickness and stiffener section profiles. In order to overcome the difficulty of large number of design variables and the computational burden of FE analysis, various methods are proposed. The steepest descent method is selected as the optimization algorithm for an efficient search. For a reduction of the number of design variables and a direct application to practical design, the stiffener section variable is determined by selecting one from a pre-defined standard library. Plate thickness is also discretized at 0.5t interval. The number of FE analysis is reduced by using equations to analytically estimating the stress changes in gradient calculation and line search steps. As an endeavor to robust optimization, the number of design variables to be simultaneously optimized is divided by grouping the scantling variables by the plane. A sequential optimization is performed group by group. As a verification example, a central column of a semi-submersible structure is optimized and compared with a conventional optimization of all design variables at once.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• Korean Journal of Child Studies
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• v.16 no.1
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• pp.99-112
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• 1995

This study aimed (1) to classify Korean kindergarten childrens' attachment to their mothers based on a system for classifying attachment organization developed by Main and Cassidy (1988), and (2) to investigate the relationship of attachment and temperament and mothers' child rearing behavior. 76 kindergarteners and their mothers were observed and videotaped in the strange situation. The modified PTQ(Parent and Teacher Temperament Questionnaire) for children 3-7 years of age and the IPBI(Iowa Parent Behavior Inventory: Mother Form) were administered respectively to 76 mothers to assess their parenting behavior and their children's temperament. The data were analyzed by percentiles, Pearson's correlations, and canonical correlation analysis. Results indicated that there was a little difference between the attachment classification of Main and Cassidy(1988) and that of Korean kindergarten children. There were significant correlations between children's temperament and the attachment to their mother. And mothers' parenting behavior was significantly related to the security of attachment. The canonical correlation analysis indicated that independent variables all together accounted for about 7.5% of the variation in attachment-variables.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.113-141
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• 2016

This study identifies and validates the factors that affect sales performance of salespersons in the business software industry. In the study, in order to measure the dependent variable (performance of the salesperson) more comprehensively, multiple items are utilized and both outcome and behavior indicators are used. Independent variables are identified based on the classification of Verbeke et al. [(2011] including sales related knowledge, degree of adaptiveness, role ambiguity, and work engagement. Results of the hypotheses testing show that 'sales related knowledge' and 'work engagement' are statistically significant factors, but 'degree of adaptiveness' and 'role ambiguity' are not. This study has a few limitations and future research direction to overcome the limitation is suggested : use of both perceptions of the salesperson and objective measures in measuring the related variables; study including cognitive ability; analyses of the factors across various types of software companies; and analyses of the factors on the team level.

• Journal of the Korea Fashion and Costume Design Association
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• v.5 no.3
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• pp.99-110
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• 2003

The purpose of this study is to examine the relationship among body cathexis, preference in style, clothing behavior and clothing purchase behavior. Based upon the effect of body cathexis and preference in style on adolescence clothing purchase behavior, this study suggests classification of the future adolescence consumers for fashion marketing. The questionnaire were administered to 1400 middle school and high school students living in Seoul and Inchon. Data were analyzed by Frequency, Factor analysis, Correlation analysis, Regression analysis, SPSS. This study used body cathexis and preference in style as Independent variables, clothing behavior and clothing purchase behavior as Dependent variables.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.39-50
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• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. CHAID(Chi-square Automatic Interaction Detector) uses the chi-squired statistic to determine splitting and is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose CHAID algorithm by cube-based proportional sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.803-816
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• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. CHAID uses the chi-squired statistic to determine splitting and is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose CHAID algorithm by cube-based proportional sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.