• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.3
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• pp.185-192
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• 2002

Prototyping for field inspection safety assessment expert system of bridge structures is presented in this paper. Knowledgebase with production rules is constructed using the semiautomatic method on the basis of bridge inspection manuals and working reports of the related agency. Backward inference method is taken with the aids of external shells as a inference engine of knowledge-based systems. Implementation of the developed prototype system on MS Windows98 will shows inspiring aspects useful to guide and standardize the field works. In the case to be reinforced with abundant knowledge bases, this will be expected to be educate the practicing engineers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• Asia pacific journal of information systems
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• v.4 no.1
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• pp.3-31
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• 1994

Potential employers considering locations for production or service facilities typically equire detailed advance knowledge of the wages they will be expected to offer for workers in various occupational categories. The State of Missouri s Department of Labor and Industrial Relations is often contacted by organizations requesting such information. The current wage rate survey approach, initiated in 1988, allows the Department to predict an appropriate wage rate for a given occupation in certain counties, adjusted for changes in the Consumer Price Index (CPI). However, both Department employees and firms have indicated that improved prediction responsiveness and accuracy are desirable. A major deficiency of the current approach is its inability to predict wages for unsurveyed counties. This paper describes a knowledge-based system (KBS), currently in the prototype testing stage, that is expected to supplement the wage rate survey in the near future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• IE interfaces
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• v.2 no.2
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• pp.51-62
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• 1989

A computer-aided simulation modeling system has been developed to allow the automatic construction of complete discrete simulation models for queueing systems. Three types of knowledge are used in the specification and construction of a simulation modeling: Knowledge of queueing system, simulation modeling, and a target simulation language. This knowledge has been incorporated into the underlying rule base in the form of extraction and construction rule, and implemented via the expert system building tool, OPS5. This paper suggested a knowledge based approach for automatic programming to enable a user who lacks modeling knowledge and simulation language expertize to quickly build executable models.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.532-541
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• 2004

• The Journal of Information Systems
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• v.29 no.4
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• pp.77-99
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• 2020

Purpose The purpose of this study is to investigate the effect of two-way communication and knowledge sharing on the task performance of employees in the interdependent task structure in organizations. Based on the theories of social interdependence and organizational commitment, this study hypothesized the effects of task interdependency and emotional commitment on two-way communication and knowledge sharing, followed by the effects of these variables on the task performance of employees. Design/methodology/approach Survey results show that task interdependence and emotional commitment have positive effects on the degree of two-way communication and knowledge sharing. Two-way communication also has a positive impact on knowledge sharing, and knowledge sharing also improved the task performance of members. However, the relationship between two-way communication and task performance shows no significant impact. Findings Theoretically, this study is meaningful in that the process of task interdependence in relation to emotional commitment leading to task performance is theorized. In practice, this research suggests it is important to improve employees' two-way communication and knowledge sharing in order to lead to increase task performance in the interdependent task environment.

• Journal of Intelligence and Information Systems
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• v.5 no.2
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• pp.79-97
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• 1999

Knowledge management (KM) is emerging as a robust management mechanism with which an organization can remain highly intelligent and competitive in a turbulent market. Organization knowledge is at the heart of KM success. As a vehicle of acquiring organizational knowledge in a distributed decision-making environment, we applied a fuzzy cognitive map (FMM) technique and proved its effectiveness in a distributed knowledge management environment. Our approach was applied to the financial statement analysis problem, yielding a robust result.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.241-245
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• 2001

The existing fuzzy traffic controllers construct the rule-base based on the intuitive knowledge and experience or the standard rule-base, but the rule-base constructed by the above methods has difficulty in representing exactly and detailedly the control knowledge of the export and the operator. Therefore, in this paper, we propose a method that can improve the performance of the fuzzy traffic control by designing the fuzzy traffic controller which represents the control knowledge more exactly. The proposed method so modifies the position and shape of the fuzzy membership function based on the input-output data clustering that the fuzzy traffic controller can represent the control knowledge more exactly. Our method use the rough control knowledge based on intuitive knowledge and experience as the evaluation function for clustering the input-output data. The fuzzy traffic controller designed by the our method could represent the control knowledge of the expert and the operator more exactly, and it outperformed the existing controller in terms of the number of passed vehicles and the wasted green-time.