• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.571-578
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• 2005

Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.239-248
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• 2007

In the highly competitive construction industry, a slight inaccuracy of estimation can easily cause the loss of a project. Erroneous experience-based cost estimates or allocations of on-site supervisory manpower often offset the profit gained from the project and may jeopardize the management processes. To counter these types of problems, we develop a model using mathematical analysis and case-based reasoning to automate the allocation of on-site supervisory manpower and estimate construction site costs. The method is founded upon laborious data collection processes and analysis by matching statistical assumptions, and is applicable to construction projects. In the modeling the costs and allocation of on-site supervisory manpower are quantified for both owners and contractors before initiating or bidding on the projects. The findings confirm that the degree of variation of the model predictions has an accuracy rate at 88.47%. Single-site construction projects can be accurately predicted and the assignment of supervisory manpower feasibly automated.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.125-132
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• 2016

This study intended to exploratively depict the influence of interaction between Environmental Uncertainty and Learning Orientation of Korean IT companies on Marketing Capability which was adopted as one of the organizational performance indicator. Statistical Results based on AMOS and SPSS showed that smaller-sized companies under 500 employees are more inclined to desperately and flexibly meet and adapt to their environmental uncertainty, resulting positive performance, that is, marketing capabilities. On the other hand, larger-sized companies over 500 employees showed no significant interaction effect. This result of the study induces the reasoning that the differences in competitive environment and market leadership accrued by organizational size may also incur differences in environmental adaptive mechanism. However, this reasoning can have some limitation in that the types and traits of IT firms are so different. Therefore, this topic suggests the necessity of follow-up researches using enlarged samples in IT industry and comparative studies in other industries.

• School Mathematics
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• v.13 no.3
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• pp.363-384
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• 2011

This study is based on the recognition that the school mathematics education should reinforce the heuristic and constructional aspects related with discoveries of mathematical rules and understanding of mathematical concepts from real world situations as well as the deductive and formal aspects emphasizing on mathematical contents precisely. The 11th grade students of one class from a city high school with average were chosen. They were given time to learn various functions of Excel in regular classes of "Information Society and Computer" subject. They don't have difficulty using cells, mathematical functions and statistical functions in spreadsheet. Experiment was performed for six weeks and there were two hours of classes in a week. Considering the results of this research, teaching materials using spreadsheets play an important role in helping students to experience probabilistic and statistical reasoning and construct mathematical thinking. This implies that teaching materials using spreadsheet provide students with an opportunity to interact with probabilistic and statistical situations by adopting engineering which can encourage students to observe and experience various aspects of real world in authentic situations.

• Journal of Educational Research in Mathematics
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• v.21 no.4
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• pp.327-344
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• 2011

This study is the first step for us toward improving high school students' capability of statistical inferences, such as obtaining and interpreting the confidence interval on the population mean that is currently learned in high school. We suggest 5 underlying concepts of 'discretion of contingency and inevitability', 'discretion of induction and deduction', 'likelihood principle', 'variability of a statistic' and 'statistical model', those are necessary to appreciate statistical inferences as a reliable arguing tools in spite of its occasional erroneous conclusions. We assume those 5 concepts above are to be gradually developing in their school periods and Korean mathematics textbooks of grades 1-12 were analyzed. Followings were found. For the right choice of solving methodology of the given problem, no elementary textbook but a few high school textbooks describe its difference between the contingent circumstance and the inevitable one. Formal definitions of population and sample are not introduced until high school grades, so that the developments of critical thoughts on the reliability of inductive reasoning could not be observed. On the contrary of it, strong emphasis lies on the calculation stuff of the sample data without any inference on the population prospective based upon the sample. Instead of the representative properties of a random sample, more emphasis lies on how to get a random sample. As a result of it, the fact that 'the random variability of the value of a statistic which is calculated from the sample ought to be inherited from the randomness of the sample' could neither be noticed nor be explained as well. No comparative descriptions on the statistical inferences against the mathematical(deductive) reasoning were found. Few explanations on the likelihood principle and its probabilistic applications in accordance with students' cognitive developmental growth were found. It was hard to find the explanation of a random variability of statistics and on the existence of its sampling distribution. It is worthwhile to explain it because, nevertheless obtaining the sampling distribution of a particular statistic, like a sample mean, is a very difficult job, mere noticing its existence may cause a drastic change of understanding in a statistical inference.

• Industry Promotion Research
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• v.6 no.4
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• pp.31-38
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• 2021

This study analyzed the non-cognitive domains (self-management competency, interpersonal relations competency) and cognitive domains (physical communication competency, comprehensive reasoning ability) among K-CESA for college students in the Division of Architecture at 𐩒𐩒 University and construction company workers, after which a training program for college students was designed. A K-CESA diagnostic evaluation was conducted on 25 construction company workers and 36 students in the senior and junior years of the division of Architecture. To identify the discrepancies among the two groups, "One-way ANOVA", a mean difference test, was performed and the Scheffe verification system was conducted as an after-measure. The empirical analysis of this study was verified at the significance level p <.05, and statistical processing was analyzed utilizing the SPSS WIN. 23.0 program. The major findings are as follows: first, the significant point of difference between the college students and construction company workers were located in five skills (goal-oriented planning and execution skills, cooperative skills, intervention skills, leadership skills, speaking skills, analytical reasoning skills); second, the education program was developed to improve the goal-oriented planning, execution ability and analytical reasoning ability through the expert-required analysis and study research. Through follow-up studies, I suggested that there is a need to develop courses that compare the competencies of various majors and workers in public institutions, corporations and other organizations.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.3
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• pp.224-236
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• 2018

The purpose of this study was to develop a reasoning-based HTE-STEAM program for the development of the cognitive capacity of middle school students and enhancement of their STEAM literacy, and to investigate the effectiveness of this study in the school setting. The subjects of this study were the students of two middle schools located in the central region of Korea. The students participated in the HTE-STEAM program during their free-learning semesters and 202 of them were selected by random sampling method. Main findings were as follows: First, pre- and post-HTE-STEAM program has shown a significant value in statistical verification (p<.05) and the level of logical thinking ability of the research participants improved after the class compared to before the class. Second, the paired samples t-test comparing the difference between the pre and post scores of the STEAM attitude test has shown a significant value in statistical verification (p<.05), and the HTE-STEAM program has turned out to have a positive effect on the STEAM literacy of the research participants. Third, in the HTE-STEAM satisfaction scale test, the mean value of the sub-construct stood at 3.27~4.12, showing a positive overall response. Therefore, the HTE-STEAM program under the topic of earth science of 'Disaster and Safety' developed at the final stage of this study has proven to have a positive influence on the research participants in terms of the development of cognitive capacity by reasoning and collaborative learning, an important quality of communication and consideration necessary for STEAM literacy.

• Journal of Educational Research in Mathematics
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• v.20 no.2
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• pp.163-183
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• 2010

It has long been of controversy what the meanings of probability is. And a century has past after the mathematical probability has been at the center of the school curriculum of it. Recently statistical meaning of probability becomes important for various reasons. However the simple modification of its definition is not enough. The computational reasoning of the probability and its practical application needs didactical changes and new instructional transformations along with the modification of it. Most of the current text books introduce probability as a limit of the relative frequencies, a statistical probability. But when the probability computation of the union of two events, or of the simultaneous events is faced on, they use mathematical probability for explanation and practices. Accordingly there is a gap for students in understanding those. Probability is an intuitive concept as far as it belongs to the domain of the experiential frequency. And frequency distribution must be the instructional bases for the (statistical) probability novices. This is what we mean by the probability in accordance with the distribution concepts. First of all, in order to explain the probability of the complementary event we should explain the empirical relative frequency of it first. These are the case for the union of two events and for the simultaneous events. Moreover we need to provide a logic of probabilistic guesses, inferences and decision, which we introduce with the name “the likelihood principle”, the most famous statistical principle. We emphasized this be done through the problems of practical decision making.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.