• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.17-22
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• 1999

In this paper we propose a new mixed method of LDA and tri-tone model to predict Korean prosodic break indices(PBI) for a given utterance. PBI can be used as an important cue of syntactic discontinuity in continuous speech recognition(CSR). The model consists of three steps. At the first step, PBI was predicted with the information of syllable and pause duration through the linear discriminant analysis (LDA) method. At the second step, syllable tone information was used to estimate PBI. In this step we used vector quantization (VQ) for coding the syllable tones and PBI is estimated by tri-tone model. In the last step, two PBI predictors were integrated by a weight factor. The proposed method was tested on 200 literal style spoken sentences. The experimental results showed 72% accuracy.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.910
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• pp.1253-1264
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• 2004

The purpose of this study is to develop clothing shopping orientation[CSO] scale reflecting the conceptual structure of CSO. For this purpose, a questionnaire composed of comprehensive 85 CSO items was developed through 2-step preliminary tests. Data was collected from two samples. One sample(n=559) was for scale development and the other sample(n=235) was for cross validity test. Descriptive analysis, correlation analysis, exploratory factor analysis, regression analysis, ANOVA, and confirmatory factor analysis were used for data analysis. For each lower-dimension within the CSO conceptual structure model, 1-2 items were selected based on the quantitative and the qualitative standards. As a result, a CSO scale composed of 31 items was developed, and reliability, construct validity, cross validity, convergent validity, discriminant validity, and criterion validity of the scale were verified. This study has significance in offering the standardized scale to both the academic and the practical fields.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.1
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• pp.55-67
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• 2009

The purpose of this study is to develop well-being lifestyle measurement. Data were collected from a total of 251 Korean females ranging from 20 to 50 years old. The measurement items were developed by focus group interview to well-being consumers. Through a series of exploratory factor analysis and confirmatory factor analysis, the 7 sub-factors and 14 items that construct final measurement model of well-being lifestyle were identified: Health oriented eating habits, social welfare oriented consumption, interest in health policy, self-esteem enhancement, sports activity, volunteer for local community, use of cosmetics made of natural components. Fitness of measurement model and reliability and discriminant validity of measurement variables were accepted as a good level.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Korea Technology Innovation Society
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• v.10 no.1
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• pp.121-142
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• 2007

The purpose of this study is to construct the evaluation model for the major science research facilities and equipments to enhance the competitiveness of the science and technology and also to test the reliability and validity of the model. To achieve the purposes, this study theoretically reviews the concept of the major science research facilities and equipments and their characteristics. Through a review of literature, this study draws 11 criteria for evaluating the priorities of the major science research facilities and equipments. These criteria are categorized as two dimensions - 'science & technology' and 'national policy'. The dimension of science & technology includes scientific importance, technological readiness, utilization rate, common utilization rate, and ability of management and operation. The national policy dimension contains degree of correspondence with national science development, imperativeness of national policy, science and technical effectiveness, economic and industrial effectiveness, responsiveness of research demand, and equity among the related institutions. The competitiveness of the science and technology consists of these two dimensions. The evaluation model is established on the framework of criteria. The 18 major science research facilities and equipments are selected through a series of Delphi. The survey of experts (BT, ET, IT, NT and ST) is also implemented to evaluate the 18 major science research facilities and equipments by 11 criteria. The overall results indicate that the reliability and validity of the model are good. The reliability tests show that the five indicators of science & technology and the six indicators of national policy have high internal consistencies. The confirmatory factor analyses reveal that the two constructs - 'science & technology' and 'national policy' - have high convergent and discriminant validity. The correlational analyses also show that the criteria-related validity between them is high. Furthermore, the results of higher order factor analysis indicate that the fit indices of the model are high and suggest a good fit to the data. Based on these findings, the policy implications of the model are discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.670-676
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• 2009

Bankruptcy of firms in Korea can cause distress of financial institutions because these institutions have disterssed bond. Accordingly, social and economical spill-over effects by these results are very big. Even after the difficult times of IMF crisis had ended, bankruptcy of information-based small-medium companies and venture firms listed on the KOSDAQ has been continued. In this context, this study developed and adopted failure prediction models for which discriminant analysis was used. Samples of this study was 81 firms respectively for both failed and non-failed firms listed on the KOSDAQ between the year of 2000 and 2007. The results of this study are as follows. First, the accuracy of classification of the model by years was $74.5%{\sim}76.5%$, and the accuracy of classification of the mean model was $69.6%{\sim}80.4%$. Among the models, the mean model of -one year, -two years, and -three years was highest in accuracy of classification (80.4%). Second, accuracy of prediction of final model adopted on validation samples showed 85% before one year of bankruptcy. The results of this study may be significant in that the results may be used as early warning system for bankruptcy prediction of KOSDAQ firms.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.267-294
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• 2008

Social workers experience various work commitment forms in the field practice. This study tries to find the discriminant validity of job, organizational, career, and relationship commitments among these work commitment forms. This study also tries to find the interrelationship among these commitment forms and the relationship of each of four commitment forms with the withdrawal intention, which is represented by turnover intention and career change intention. For this purpose, a survey of 417 social workers working for community welfare centers in Busan and Gyeongnam was conducted and the data was analyzed. The results of this study showed that the work commitment forms have discriminant validity. The analysis of interrelationship between commitment forms using SEM revealed that the more a social worker commits to his or her job, the more he/she commits to his/her job and the relationship with the client. In addition, job and organizational and career commitments affect turnover intention while career and relationship commitments affect career change intention. Therefore, to improve organizational management, it is necessary to understand diverse forms of work commitment as well as organizational commitment. And differentiated management strategies are needed to increase either each commitment form or various commitment forms at the same time.

• The Journal of Korean Academy of Sensory Integration
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• v.17 no.1
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• pp.19-29
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• 2019

Objective : The aim of this study was to verify validity of the Activity Participation Assessment for school-age children. Methods : A questionnaire consisting of 30 items from the APA, 75 items from the PACS, and 55 items from the CAPE was administered to elementary school students. A total of 207 questionnaires were analyzed. Confirmatory factor analysis was performed to confirm the construct validity of the APA. Convergence validity and discriminant validity were verified with the average variance extracted (AVE) and the square of the correlation coefficient. The discriminant validity was the Pearson correlation coefficient of the APA, PACS, and CAPE. Results : The results of the analysis were as follows: 1) For construct validity, the goodness of fit of the modified hierarchical second-order factor model was found to be appropriate (p < .001), 2) For convergent validity, the AVE was higher than .50 for all latent variables, 3) For discriminant validity, the AVE of the latent variable was greater than the square of the correlation coefficient (0.239), 4) For concurrent validity, the correlation between the total sum of the APA and PACS scores showed a positive correlation in all domains, and the correlation coefficient ranged from .303 to .647 at a statistically significant level (p < .01), 5) The correlation coefficient between the total sum of the APA and CAPE scores was .490 for recreational activities, .329 for physical activities, .571 for social activities, .401 for skill-based activities, and .390 for self-improvement activities. All domains showed a positive correlation, and were statistically significant (p < .01). Conclusion : APA can be used as a valid assessment tool to measure the participation of school aged children.