• Journal of Korean society of Dental Hygiene
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• v.20 no.6
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• pp.775-786
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• 2020

Objectives: The purpose of this study was to evaluate the effects that appeared to parents after conducting a dental caries management program for 12 months using a mobile application for systematic caries management of children. Methods: Parents responded to a questionnaire on oral care self-efficacy and oral health knowledge at the baseline, and received feedback on a management program suitable for their child's caries risk group for 12 months through a mobile application. At the end of 12 months, the questionnaire was re-written. Results: The self-efficacy of oral care increased in the low risk group, and oral care knowledge and program satisfaction were highest in the parents of low risk children. Conclusions: Base on the results of the this study, It was confirmed that parents' self-efficacy, knowledge, performance and satisfaction were all positively evaluated through the oral care program using a mobile application.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.179-192
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• 2008

This case study shows constructing the knowledge-based system using a rule-based approach for detecting illegal transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions, the adoption of a knowledge-based system in the KoFIU is essential. Also since different types of information from various organizations are converged into the KoFIU, constructing a knowledge-based system for practical use and data management regarding money laundering is definitely required. The success of the financial information system largely depends on how well we can build the knowledge-base for the context. Therefore we designed and constructed the knowledge-based system for anti-money laundering by committing domain experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation, measured by the empirical ratio of Suspicious Transaction Reports (STRs) reported to law enforcements, shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently, and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• East Asian Journal of Business Economics (EAJBE)
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• v.2 no.3
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• pp.1-17
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• 2014

Although management consulting activities have obtained a considerable growth in terms of economic significance in recent years, these results have not been duly followed by a greater number of conceptual and empirical research in this area. In order to fight the lack of studies on the actual work of management consultants, this article aims at answer some questions that remain open. Is management consulting an intensive and specialized knowledge activity? Are there knowledge and learning key success factors in this area? Are consultants the real experts and the true practitioners of strategy as practice? The results of the empirical analysis in the form of semi-structured interviews and questionnaires given to management consultants and SME managers in Portugal shows that management consulting is founded on a knowledge-intensive base, although consultants cannot be called strategy practitioners as many authors call them. This is illustrated by the Portuguese model of determinants that constitute the management consulting industry presented in this article, which means this proposition is a new direction in strategic thinking in what the field's research strategy-as-practice concerns.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.294-300
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• 2006

This case study shows constructing the knowledge-based system using a rule-based approach for detecting transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions and to conjugate data converged into the KoFIU from various organizations, the adoption of a knowledge-based system is definitely required. We designed and constructed the knowledge-based system for anti-money laundering by committing experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.901-902
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• 2008

• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.18-24
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• 1990

The location of inspection stations is a significant component of production systems. In this paper, a prototype expert system is designed for deciding the optimal location of inspection stations. The production system is defined as a single channel of n serial operation stations. The potential inspection station can be located after any of the operation stations. Nonconforming units are generated from a compound binomial distribution with known parameters at any given operation station. Traditionally Dynamic programming, Zero-one integer programming, or Non-linear programming techniques are used to solve this problem. However a problem with these techniques is that the computation time becomes prohibitively large when t be number of potential inspection stations are fifteen or more. An expert system has the potential to solve this problem using a rule-based system to determine the near optimal location of inspection stations. This prototype expert system is divided into a static database, a dynamic database and a knowledge base. Based on defined production systems, the sophisticated rules are generated by the simulator as a part of the knowledge base. A generate-and-test inference mechanism is utilized to search the solution space by applying appropriate symbolic and quantitative rules based on input data. The goal of the system is to determine the location of inspection stations while minimizing total cost.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.15-40
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• 1994

We designed a knowledge-based decision support system for structuring semi-or unstructured problems. Problem structuring involves extraction of the relevant factors from the identified problem, and model construction that represents the relationships among those factors. In this research, we employed a directed graph called Influence Deiagram as a tool for problem structuring. In particular, our proposed system is designed as a shell. Therefore, a decision maker can change the content of the knowledge base to suit his/her own interested domain.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.85-95
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• 2014

Simulation for product design requires a lot of causal knowledge. Hence, knowledge integration is required for obtaining a new knowledge from existing knowledge. For example, a user requests knowledge for the heating cup. However, the knowledge base only has knowledge for heating and cup, not heating cup. At his situation, knowledge integration can generate a new heating cup knowledge from existing heating and cup knowledge. Therefore, the user can obtain the knowledge for heating cup. Hence, this study aims to propose a novel knowledge integration method for product design.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.