• 대한전기학회논문지
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• 제39권7호
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• pp.720-728
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• 1990

A learning control method is proposed in this paper, using a knowledge base which contains control rules, data, and patterns of the past experience of a plant. The knowledge for plant control is retrieved from measurement data during operation and continually modified after control performance evaluation. A control method is proposed using tinually modified after control performance evaluation. A control method is proposed using fuzzy model of the plant and a recursive statistic decision method of fuzzy subset for control rule generation. Also, the resulting knowledge-based control algorithm has been applied to aprocess and its performance improvement and proper generation of appropriate control rules have been verified.

• 지식경영연구
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• 제9권4호
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• pp.65-75
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• 2008

Knowledge workers' workload to register knowledge can cause quality defects in the quality as well as the quantity of knowledge that must be accumulated in a knowledge management system(KMS). To enhance the availability of a KMS by acquiring more quality-guaranteed knowledge, autonomous knowledge acquisition which outdoes the automated acquisition must be initiated. Adopting the capabilities of context-awareness and inference in the field of context-aware computing, this paper intends to autonomously identify and acquire knowledge from knowledge workers' daily lives. Based on knowledge workers' context information, such as location, identification, schedule, etc, a methodology to monitor, sense, and gather knowledge that resides in their ordinary discussions is proposed. Also, a prototype systems of the context-based knowledge acquisition system(CKAS), which autonomously dictates, analyzes, and stores dialogue-based knowledge is introduced to prove the validity of the proposed concepts. This paper's methodology and prototype system can support relieving knowledge workers' burden to manually register knowledge, and hence provide a way to accomplish the goal of knowledge management, efficient and effective management of qualified knowledge.

• 지식경영연구
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• 제7권1호
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• pp.13-30
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• 2006

This study aims at exploring the relationships between knowledge activity, knowledge activity performance, and organizational performance. By adopting the balanced scorecard perspective, organizational performance was measured by product/service, customer and internal performances. Using data collected from the 36 Korean firms, this study found that knowledge creation activity was positively and significantly related to the organizational performance such as product/service performance, customer performance and internal performance. We also found that knowledge activity performance such as knowledge quality and user knowledge satisfaction mediated the positive relationship between knowledge sharing activity and internal performance.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.595-598
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• 2004

In recognizing knowledge as a new resource in gaining organizational competitiveness, knowledge management suggests a method in managing and applying knowledge for improving organizational performance. Much knowledge management research has focused on identifying, storing, and disseminating process related knowledge in an organized manner. Applying knowledge to decision making has a significant impact on organizational performance than solely processing transactions for knowledge management. In this research, we suggest a method of knowledge-based decision-making using system dynamics, with an emphasis to strategic problems. The proposed method transforms individual mental models into explicit knowledge by translating partial and implicit knowledge into an integrated knowledge model. The scenario-based test of the organized knowledge model enables decision-makers to understand the structure of the target problem and identify its basic cause, which facilitates effective decision-making. This method facilitates the linkage between knowledge management initiatives and achieving strategic goals and objectives of an organization.

• Asia pacific journal of information systems
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• 제25권3호
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• pp.519-539
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• 2015

Customers are now demanding ever better service from customer service representatives (CSRs) to create superior customer service. Accordingly, CSRs are required to have more specialized knowledge and abilities of customer service. This study examines the roles of accumulated customer knowledge in electronic knowledge repositories (EKRs), which a firm has developed for customer service over time to enhance CSRs' work capabilities and work performance, in the context of call centers. To test the proposed research model and hypotheses, we conducted LISREL analysis using 261 responses collected on CSRs working for inbound call centers. The key results are as follows. First, accumulated customer knowledge in EKRs enhances CSRs' knowledge utilization and service expertise during the customer contact. Second, CSRs' knowledge utilization reinforces service expertise. Finally, service quality depends on CSRs' knowledge utilization and service expertise, but it is not directly related to accumulated customer knowledge. Overall, the findings suggest that accumulated customer knowledge in EKRs enhances CSRs' knowledge utilization and service expertise, and thereby leading to superior service quality.

• 지식경영연구
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• 제10권1호
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• pp.1-15
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• 2009

As pervasive computing technology, which aims to get linked to useful knowledge, information or services anytime, anywhere, using any devices and/or artifacts, is proliferating, desirable impacts on knowledge management systems are now available. The pervasive computing technology will potentially enable the knowledge management systems to realize individualization and socialization and ultimately increase the knowledge processing productivity. However, researchers who apply the pervasive computing methodologies to novel way of knowledge management have been very few. These result in unsatisfactory consideration of establishing pervasive knowledge management systems. Hence, the purpose of this paper is to cast the vision of pervasive knowledge management and search for a couple of possible research issues and possibilities. This paper suggests a framework of ubiDSS, an amended knowledge management system for the next generation deploying pervasive and autonomous knowledge acquisition capabilities of ubiquitous computing technologies. Also the CKAM, context-based knowledge acquisition module, is illustrated as a prototype of future knowledge management systems.

• 한국CDE학회논문집
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• 제11권2호
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• pp.88-96
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• 2006

This paper introduces an approach to ontology-based framework for knowledge management in a product development domain. The participants in a product life cycle want to share the product knowledge without any heterogeneity. However, previous knowledge management systems do not have any conceptual specifications of their knowledge. We suggest the three levels of knowledge framework. First level is an axiom, which specifies the semantics of concepts and relations. Second level is a product development knowledge map. It defines the common domain knowledge which domain experts agree with. Third level is a specialized knowledge for domain, which includes three knowledge types; expert knowledge, engineering function and data-analysis-based knowledge. We propose an ontology-based knowledge framework based on the three levels of knowledge. The framework has a uniform representation; first order logic to increase integrity of the framework. We implement the framework using prolog and test example queries to show the effectiveness of the framework.

• 지능정보연구
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• 제9권2호
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• 대한간호학회지
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• 제39권1호
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• pp.33-43
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• 2009

Purpose: This study was done to develop a pregnancy nutrition knowledge scale and to examine the relationships between pregnancy nutrition knowledge and eating habits in pregnant women. Methods: With convenient sampling, 189 pregnant women who used community health centers for their ante-natal care were recruited. Data were collected using a self administered questionnaire including items on pregnancy nutrition knowledge (18 items) developed by researcher and items on eating habits (14 items). Cronbach's alpha and exploratory factor analysis were examined to test reliability and construct validity of the scale. Pearson's correlation coefficients were used to identify the relationship between pregnancy nutrition knowledge and eating habits. Results: Cronbach's alpha of 18 items was .80. In factor analysis using principal components, 6 factors explained 65% of the total variance. The level of pregnancy nutrition knowledge was not sufficient but correlations between pregnancy nutrition knowledge and some of eating habits were significant. Specifically, pregnancy nutrition knowledge was positively correlated with good eating habits and negatively with bad eating habits. Conclusion: The pregnancy nutrition knowledge scale developed in this study is acceptable for nutrition education led by nurses. Pregnancy nutrition knowledge and eating habits are considered as major variables for ante-natal nutrition education. In future studies, explorations are needed on dietary intake and physiological indices in pregnant women, comparison of women at risk with those not at risk, and development of nutritional education programs for pregnant women.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.