• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.271-276
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• 1997

Verification is a process aimed at demonstrating whether a system meets it's specified requirements. As expert systems are used in various applications, the knowledge base verification of systems fakes an important position. The conventional Petri net approach that has been studied recently in order to verify the knowledge base is found that it is inadequate to verify the knowledge base of large and complex system, such as alarm processing system of nuclear power plant. Thus, we propose an improved method that models the knowledge base as enhanced colored Petri net. In this study. we analyze the reachability and the error characteristics of the knowledge base and apply the method to verification of simple knowledge base.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2004.11a
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• pp.193-194
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• 2004

This Interior Design Studio course provide Interior Design major student a new way teaching method based on the theoretical approach for the reasonable thinking. The purpose of this course is to make the foundation of research cooperation through the various knowledge and research activities.

• International Journal of CAD/CAM
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• v.3 no.1_2
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• pp.43-50
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• 2003

This paper presents a methodology in which the knowledge of design intents and change requests is communicated unambiguously cross collaboration partners through features. The domain of application is focused on the plastic part design for enabling effective collaboration between the product design and plastic mold making. The methodology takes the feature-based design approach and allows design features and knowledge to be reused in plastic injection mold design. It shortens the mold design lead-time, reduces mold design efforts, and enables unambiguous and fast design change management between product and mold designers. These contribute to the reduction of product development cycle time.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.35-37
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• 2007

Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

• Proceedings of the Microbiological Society of Korea Conference
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• 1991.04a
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• pp.173-174
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• 1991

• ETRI Journal
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• v.39 no.2
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• pp.191-201
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• 2017

This paper proposes an approach using taxonomic relatedness for answer-type recognition and type coercion in a question-answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule-based approach of IBM's Watson, our LAT detector, which combines rule-based and machine-learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness-based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer-type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.

• Knowledge Management Research
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• v.13 no.1
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• pp.13-24
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• 2012

Unlike the traditional belief that knowledge flows along the formal reporting procedures, recent social network research has reported the importance of informal social networks which may play a critical role as the real knowledge conduits. In fact, as a complementary approach of utilizing knowledge management systems (KMSs), many firms have focused on managing informal knowledge flow through which to acquire and transfer valuable knowledge in a fast and effective way. In a case of global companies that have newly developed foreign branches or subsidiaries, due to cultural or institutional differences and lack of understanding of knowledge management and its benefits, they often have difficulties in activating knowledge sharing in local branches. In these situations, diagnosing organizational knowledge flow through SNA can be a first step to solve the problems. Therefore, in this paper, I report on the result of case study on a foreign branch of "A" global company by identifying organizational knowledge paths. Based on the results of the diagnosis, some implications and insights for building knowledge management (KM) strategy specified for a newly developed foreign branch will also be discussed.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.