• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.734-736
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• 1996

This paper presents a method of calculating a selective number of eigenvalues in power systems, which are rightmost, or are largest modulus. The modified Arnoldi method in conjunction with implicit shift OR-algorithm is used to calculate the rightmost eigenvalues. Algorithm requires neither a prior knowledge of the specified shifts nor the calculation of inverse matrix. The key advantage of the algorithm is its ability to converge to the wanted eigenvalues at once. The method is compared with the modified Arnoldi method combined with S-matrix transformation, where the eigenvalues having the largest modulus are to be determined. The two methods are applied to the reduced Kansai system. Convergence characteristics and performances are compared. Results show that both methods are robust and has good convergence properties. However, the implicit shift OR method is seen to be faster than the S-matrix method under the same condition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.355-363
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• 2012

The Integration of multi-agent architecture and blackboard architecture may lead to a new architecture to cope with new application areas which need some good and strong points of both the architectures. This paper suggests an integrated architecture of blackboard architecture and multi-agent architecture by using event-based implicit invocation pattern and a blackboard event detection mechanism based on Rete network. From the viewpoints of weak couplings of system components and flexible control of knowledge source agents, it is desirable to use the event-based implicit invocation pattern in the integrated architecture. But the pattern itself does not concern the performance of the architecture, and it is very critical to the performance of the integrated architecture to detect efficiently the blackboard events which can activate knowledge source agents which can contribute to the problem-solving processes of the integrated architecture. The integrated architecture suggested in this paper uses a blackboard event detection mechanism based on Rete network to detect efficiently blackboard events which can activate knowledge source agents.

• Journal of Korean language education
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• v.29 no.4
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• pp.93-112
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• 2018

The purpose of this study is to deal with vocabulary in KFL. As a result of this study, learners learned vocabulary on average 43 points through contextual inference and introduction of the class to activate background knowledge. In particular, the implicit method showed the highest learning rate of 52 points, and the thematic method had a 41 point-learning rate. In contrast, the semantic method was the lowest with a 25 point-learning rate. There was no significant difference in the improvement rate of upper vocabulary learners, but in the case of the lower learner, there was significant difference in the improvement rate. The difference was not significant in the post-test relative gain rate of upper learners, but there was significant in lower learners. In the delayed test relative gain rate, the difference was significant in all groups. There was correlation between vocabulary difficulty and score, but there was no correlation with the thematic method. And there was no correlation between vocabulary difficulty, improvement rate and relative gain rate in all three classes. However, content understanding, lexical grade, improvement rate, and relative gain rate showed a significant correlation.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.29-34
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• 2019

Priority queues, one of the fundamental data structures, have been studied for a long time by computer scientists. This paper proposes an implicit double-ended priority queue, called IMI-heap, in which insert operation takes constant amortized time and each of removal operation of the minimum key or the maximum key takes O(logn) time. To the author's knowledge, all implicit double-ended priority queues that have been published, perform insert, removeMin and removeMax operations in O(logn) time each. So, the proposed IMI-heap is superior than the published heaps in terms of insertion time complexity.The abstract should concisely state what was done, how it was done, principal results, and their significance.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.55-65
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• 2008

The strategic decision makers of the firm have faced with uncertainty and complexity. Although they must make decision under these environments, they can't have enough time, man power, budget and knowledge that they need to decide. They can't, therefore, help getting supports by experts who have implicit knowledge about the domain. But it is difficult for them to find any other procedures and methods to create, transform, combine, and apply new knowledges, whenever decision makers face the problem This paper provides a new method to support a strategic decision making by using the knowledge transformation process suggested by Nonaka. We illustrate an application case of the strategic decision making in consulting industry. This paper uses cognitive map as a decision support technology based on the suggested method.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.393-399
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• 2001

The Purpose of this paper is how KM is implemented and executed to reform the organization in the change management aspect and how its current KM can be developed in the future, mainly based on the organizational system, business process, and information system related to KM. This Paper is longitudinal case study of Knowledge Management at LG-EDS Systems. The effective approach to undertake KM is phase. First, it is structured to share explicit knowledge for better performance and then implicit knowledge for best performance. But, This method had some limitations. So, LG-EDS systems integrated KMS-Knowledge Portal-to facilitate cooperation and improve contents quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.798-803
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• 2005

The increasing complexity of modem products requires the effective management of design knowledge, which partly resides in the product itself on the one hand. On the other hand, a lot of knowledge is gathered and/or generated during the design process, but disappears as the design project concludes. This paper describes a knowledge representation method to accommodate the implicit design knowledge. The method is based on the FBS(Function-Behavior-Structure) model and extends the object ontology with constraint entity. An example to represent the injection mold design knowledge is given to show its applicability.

• Knowledge Management Research
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• v.2 no.1
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• pp.25-44
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• 2001

There are two types of organizational knowledge in terms of its creation process: experiential and analytical knowledge. The experiential knowledge is created by repetitive experiences of an individual or team through task execution, while the analytical knowledge is acquired by analyzing accumulated data or information in the organization. The experiential knowledge often remains tacit or implicit in the organization because it is primarily acquired at an individual or team level. Therefore, the issue on the experiential knowledge is to share it actively within the organization. On the other hand, the analytical knowledge is explicit in its nature since it is extracted from data or information. Thus, it is important to guide a systematic creation of the analytical knowledge rather than encourage to share it. The current trend of "knowledge management" mainly focuses on the experiential knowledge - know-how, idea, case, etc - and neglects another important knowledge in the organization. i. e., analytical knowledge. This paper tries to shed a new light on the "knowledge management" arena by introducing rather new perspective in the concept of knowledge. The purpose of this study is to identify the factors affecting the analytical knowledge creation in the organization. We conducted an exploratory case study of three companies with a previously defined research framework and found some critical factors for the analytical knowledge creation. They are "organizational resource", "effectiveness of feedback process", "data source management", and "experimental mind set". Finally, we proposed research model and propositions regarding the analytical knowledge creation in the organization.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.