• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.281-285
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• 2001

The design of injection molded polymeric parts has been done empirically, since it requires profound knowledge about the moldability and causal effects on the properties of the part, which are not available to designers through current CAD systems. An interactive computer-based design system is developed in order to realize the concept of rational design for the productivity and quality of mold making. The knowledge-based CAD system is constructed by adding the knowledge-base module for mold feature synthesis and appropriate CAE programs for mold design analysis in order to provide designers, at the initial design stage, with comprehensive process knowledge for feature synthesis performance analysis and feature-based geometric modeling. A knowledge-based CAD system is a new tool which enables the concurrent design with integrated and balanced design decisions at the initial design stage of injection molding.

• Journal of Korean Society of Rural Planning
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• v.16 no.4
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• pp.139-145
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• 2010

Agricultural information was discussed for suggesting tasks and solutions of knowledge based information system. States of agricultural information systems in rural development administration were described and problems and tasks were summarized in knowledge needs survey of farmers who have to decide many alternatives for farming. Hard to access information are soil state, disease and insect pest. Important information in agriculture are water, soil fertility, soil physical property, and accessibility from main road. In conclusion, knowledge based agricultural information system can be developed based on surveyed needs.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Journal of Korean Biological Nursing Science
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• v.22 no.4
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• pp.260-270
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• 2020

Purposes: Sepsis is a critical condition in which nurses should detect clinical manifestations and provide early intervention to prevent unwanted serious conditions in the patients. The initial occurrence and management of sepsis take place in general units, but there is a lack of knowledge in nurses. The purpose of this study was to examine the effects of a case-based sepsis education program and compare the case-based education program with and without smartphone applications. Methods: A quasi-experimental pre-test-post-test design with a control group was used. We provided a case-based education program with and without smartphone applications to the nurses and tested the effects of the program on knowledge, the accuracy of sepsis assessment, and self-efficacy as outcome variables. A total of 60 nurses in general units participated. To test differences in knowledge, the accuracy of sepsis assessment, and self-efficacy regarding sepsis between the groups over time, a mixed-design ANCOVA was used for parametric analysis, and generalized estimating equations (GEE) were used for nonparametric analysis. Results: There were significant differences in knowledge, the accuracy of sepsis assessment, and self-efficacy between the groups and within the groups over time. The intervention groups treated with the case-based education program showed improved outcome variables compared to the control group. There was no difference between case-based education with the smartphone application or without the application. Conclusions: The case-based education improved knowledge, the accuracy of sepsis assessment, and self-efficacy in the care of sepsis by nurses working in the general wards. The results suggest that the case-based education program for nurses was effective and eventually improved patient health outcomes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.731-740
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• 1991

This paper presents a knowledge based system to solve reactive power/voltage control problem in a power system. The methods to reduce inference time are proposed in inferring the solution of problem in the knowledge base which consists of heuristic rules and inowledge of experts. A set of indices drawn from the heuristic knowledge on the power system is utilized to make up for the defect of existing knowledge based systems which determine both the location and the amount of reactive power compensation devices. The concept of set of indices developed in this paper makes it possible to infer the amount of reactive power source only since the bus order list representing priority for the location of reactive power compensator to be switched on can be determined in advance. From the fact that there exists a relationship between the system voltage pattern and the reactive power pattern in operation, the pattern recognition technique is introduced to reduce the inference time in solving the severe voltage problem. To demonstrate the usefulness of the proposed knowledge based system, the IEEE 30 bus system is chosen as a sample system. The results of case study are also presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.705-712
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• 2008

Recently, interests in the notion of PKMS(Process based on Knowledge Management System) utilizing BPMS(Business Process Management System) and KMS(Knowledge Management System) have been significantly increased. Specially, most public organizations require their own effective knowledge management strategies since public business service needs various knowledge types. Based on a comprehensive framework that reflects lifecycle requirements of KMS and BPMS, we propose an PKMS system architecture, which performs step-by-step knowledge-providing strategy in public organization. To propose a PKMS architecture, this paper first investigates inter-relationships between public business and various knowledge types, and classifies knowledge types into three groups and then we suggest knowledge management strategies considering priority order among knowledge groups. Based on the proposed knowledge management, a PKMS system architecture can combine the advantages of the KM and BPM paradigms. This paper presents the PKMS system applied to employment insurance business part in real environment and demonstrated the advantages via inter-relationships between KM and BPM requirements.

• Knowledge Management Research
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• v.12 no.4
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• pp.77-90
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• 2011

This study examined the effects of knowledge management strategy and structural capital on organizational performance. Structured capital was classified into three dimensions: organizational culture, knowledge process, and information technology. Data were collected from 251 employees in a public institution. Results indicated that organizational knowledge-based strategy was positively related to employees' job satisfaction, and that information technology-based strategy was positively related to customers' satisfaction and institutional image. Results also indicated that organizational culture and knowledge process were positively related to customers' satisfaction, employees' job satisfaction, and institutional image, whereas informational technology was negatively related to customers' satisfaction and institutional image. Finally, the theoretical and practical implications of the results were discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.290-293
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• 2000

Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• Knowledge Management Research
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• v.15 no.2
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• pp.129-145
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• 2014

As a community of practice (CoP) is known to facilitate team learning, it is increasingly important to understand the mechanisms of CoP, thereby enabling organizations to fully utilize it and optimize its benefits. To explain how CoP improves organizational performance, we focus on its effects on social capital and knowledge management activities, and propose a research model suggesting that shared goals and trust in CoP improve its performance through knowledge integration. Our analysis uses structural equation modeling, with field data collected from 372 members of 46 CoPs in three companies; the analysis validates our research model. Our findings also suggest that CoP diversity can strengthen the link between knowledge integration and CoP performance.