• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.36-46
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• 2022

It takes a relatively long time for construction companies that lack the ability to finance to adapt to construction policy in the construction industry. However, financial institutions rarely provide financial products to construction companies, particularly small and medium-sized construction companies, because their security capacity and credit rating are low. This study investigates the financial products needed for small and medium construction companies to adapt to policy changes. The demand of small and medium construction companies for financial products is analyzed by experts' advise and survey. And, when the investigated financial products for the construction industry are introduced, the legal systems in need of revision are analyzed. Based on the analyzed demand and the number of legal systems needing revision, the priority for the introduction of financial products to the construction industry is analyzed. Among the financial products investigated, the priority of "Expert consultation, such as accountant, tax accountant, lawyer, etc." is the highest. In future studies, the criteria and method of financial product development for high-priority financial products could be researched.

• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.223-245
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• 2017

The purpose of this study is to examine the qualification system of competency assessor based NCS(National Skill Standards) in Australia and Scotland. To meet the purpose of the study, the characteristics of competency based assessment and the role of assessor was reviewed, and competency assessor qualification system of both countries were analysed. Australia and Scotland have developed the certification system of competency assessor based on national qualification framework. Education and Training Institutes in both countries should meet the requirement of RTOs(Registered Training Organizations) and CRBs(Credit Rating Body), which can develop and operate assessor qualification course based NQF. Also they must ensure all assessors are qualified with competency, currency and professional development and show how they have maintained, upgraded or developed new skills relevant to the current industry needs. In recent years, Korea has been introduced competency based curriculum linked with NCS in education and training sector. Also the introduction of competency assessor qualification system are currently under consideration by government. In this circumstances, the results of comparative analysis about Australia and Scotland can help the Korean government review the policies and strategies qualifying competency assessor. They also provide some implications for exploring and examining assessor qualification system based NCS and KQF(Korean Qualification Framework) in Korea.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Korean Library and Information Science Society
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• v.47 no.2
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• pp.71-98
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• 2016

Librarian certification in Korea is issued by the LIS Departments, the Academy of Librarianship, and the Academic Credit Bank system; however, it has been pointed out that there is a limit to how much these nurture high quality librarians, because the education quality and contents varies from education institutions and there is no verification method for certification issued by educational programs. Therefore, this study investigated certification systems of academic or training programs that are conducted at home and abroad, analyzed how the certification systems are oriented, what the purpose and criteria of the certification systems are, and what the content of assessment is. As a result of this investigation, several areas needing change were identified which if adopted can improve the system. These included making amendments to the library laws related to the certification system, substantially revising the relevant enforcement ordinance, making changes to the selection of the Certification authority, establishing certification standards and procedures, developing contents related to document examination and due diligence audits, rating the effects of the certification system, and setting standards. Improving the Librarian Certification System has been discussed over the past 20 years and should not be delayed any longer because of the university structural reform of the current government, the rapid rise of the qualified librarians, decreasing employment due to the human resources supply and demand imbalances, all of which has resulted in a survival crisis of four-year Department of Library and Information Science.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.248-254
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• 2022

Educational innovations are first created, improved or applied educational, didactic, educative, and managerial systems and their components that significantly improve the results of educational activities. The development of pedagogical technology in the global educational space is conventionally divided into three stages. The role of innovative technologies in Higher School practice is substantiated. Factors of effectiveness of the educational process are highlighted. Technology is defined as a phenomenon and its importance is emphasized, it is indicated that it is a component of human history, a form of expression of intelligence focused on solving important problems of being, a synthesis of the mind and human abilities. The most frequently used technologies in practice are classified. Among the priority educational innovations in higher education institutions, the following are highlighted. Introduction of modular training and a rating system for knowledge control (credit-modular system) into the educational process; distance learning system; computerization of libraries using electronic catalog programs and the creation of a fund of electronic educational and methodological materials; electronic system for managing the activities of an educational institution and the educational process. In the educational process, various innovative pedagogical methods are successfully used, the basis of which is interactivity and maximum proximity to the real professional activity of the future specialist. There are simulation technologies (game and discussion forms of organization); technology "case method" (maximum proximity to reality); video training methodology (maximum proximity to reality); computer modeling; interactive technologies; technologies of collective and group training; situational modeling technologies; technologies for working out discussion issues; project technology; Information Technologies; technologies of differentiated training; text-centric training technology and others.

• Korean Journal of Child Education & Care
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• v.19 no.3
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• pp.57-69
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• 2019

Objective: The purpose of this study is to identify the strengths and problems of the current in-service education system, and suggest ways to improve it in the future by looking at the current status of in-service education to strengthen the expertise of teachers and directors of childcare centers. In particular, I would like to search the current status of in-service education, including on-line special job competency education, which is responsible for one of the pillars of in-service education, and present the problems and measures to improve them. Methods: To that end, the present study conducted an analysis of issues based on the previous research on in-service education of childcare teachers' education, and conducted a literature examination focusing on laws, policies, and foreign cases related to in-service education. Results: In-service education for childcare teachers was categorized into educational process diversification and professionalism, educational method diversification, qualification management, and educational support, which were again organized into 14 core tasks. In addition, as a recent phenomenon that has not been discussed in detail in the preceding study, the phenomenon of increased participation in on-line special job competency education at the site of in-service education was analyzed and the problems were presented. Conclusion/Implications: Based on the results of this study, I proposed development measures such as changing the term 'in-service education' and recognizing the diversity of job competency education, the credit rating banking system for job competency education, the provision of on-line job competency education curriculum (basic courses/enhancing courses) for collective education courses, the expansion of education support for promotion to a higher grade courses and the conversion of the mandatory evaluation system for in-service educational institutions.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.129-139
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• 2012

Recent changes in landscape architectural field, such as keen attention on central and local government, checks of other related fields, circumstances both inside and outside the construction industry, assume hostile attitude towards qualification system in landscape architecture. By securing the original function of qualification meets the environmental changes and accords to the technical development, practicality and serviceability of qualification as well as credit rating and professional status can be enforced. Framework redesign on landscape architecture National Technical Qualifications(NTQ) is required in order to meet the demand in the industrial fields and to reflect the technical changes. National Competency Standards(NCS) was selected as a precedent study to enhance the practicality and serviceability of NTQ as well as to avoid duplication on qualified requirements. It would provide a model to redesign the framework of landscape architecture NTQ. In this study, questions in NCS and in landscape architecture certification are compared and analyzed to review the suitability of the present landscape architecture certification items. In conclusion, the creation of master landscape architect under the present system, and the subdivision of the technician's license level to planting technician and the facility are recommended. The ability units to be qualified for each level, which would be used for future NTQ standards and university curriculums in relevant fields, are also suggested in this study.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.175-183
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• 2018

This study analyzes determinants of bond returns in terms of systematic risk versus idiosyncratic risk by examining relationship among those factors. First we examined the cross-sectional determinants of corporate bond returns with Korean bond market data from 2001 to 2014. This paper uses term factor and default factor for systematic risk, and duration factor and credit rating factor for idiosyncratic risk. The empirical result shows that systematic risk can explain cross-sectional differences of bond returns rather than idiosyncratic risk which is the same result in advanced markets(US or Europe). This result is different from the previous Korean studies which showed that idiosyncratic risk is more important than systematic risk in Korean bond market. The reason for the different result may be the longer sample period which includes the most recent period. It is insisted that Korean bond market is getting more synchronized with the advanced bond market. In conclusion, this empirical result implies that Korean bond portfolio managers should focus on systematic risk, which is contrary to current system in Korean asset management industry.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.