• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1285-1288
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• 1993

We tried gas identification by using one semiconductive gas sensor. As a method of gas identification, we used the fuzzy reasoning with fuzzy set of slope of gas pattern which is divided into arbitary interval. As a result, we got a good successful rate for hydrogen 66.6%, propane 79.1%, butane 100%, methane 100%, city gas 79.1% and alcohol 91.6%, respectively.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.117-126
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• 2001

This paper presents fuzzy Pr/T nets to represent the fuzzy production rules of a knowledge-based system in the level of first-order predicate logic. The fuzzy Pr/T nets are fuzzy extension of the Pr/T nets. Based on the fuzzy Pr/T net, we propose a fuzzy reasoning algorithm. This algorithm is much closer to human intuition and reasoning than other methods because of using the proper belief functions according to fuzzy concepts in fuzzy production rules.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.3-33
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• 1992

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.36-43
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• 1997

Weld bead shape is an important measure for evaluation of weld quality. Many welding parameters have influence on the weld bead shape. The quantitative relationship between welding parameters and bead shape, however, is not determined yet because of their high complexity and many unknown factors. Fuzzy expert system is an advanced expert system which uses fuzzy rules and approximate reasoning. It is a vert useful tool for welding technology because is can process rationally the uncertain and inexact information such as the welding information. In this paper, the empirical and the qualitative relationship between welding parameters and bead shape are analyzed and represented by fuzzy rules. They are converted to the quantitative relationship by use of approximate reasoning of fuzzy expert system. Weld bead shape is estimated from the welding parameters using fuzzy expert system. The result of comparison between measured values of weld bead by welding experiments and the estimates values by fuzzy expert system shows a good consistancy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1275-1280
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• 2004

This paper presents the construction of an active suspension system of a one-wheel car model by using fuzzy reasoning. The car model is approximately described by a nonlinear two degrees freedom system subject to excitation from a road profile, and the active control force is constructed by actuating a pneumatic actuator, and the degradation of the performance due to the delay of the pneumatic actuator is improved by inserting a compensator. The fuzzy control is obtained by single input rule modules fuzzy reasoning, and the excitation from the road profile is estimated by using a disturbance observer. The experimental result shows that the proposed active suspension system much improves the performance in the vibration suppression of the car model.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.474-483
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• 2003

A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.64-71
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• 1995

The objective of this paper is to realize an analysis system that is capable of controlling the quality of an entire cutting process by including a 3 coordinate measuring machine in the process line. Fuzzy reasoning networks based on fuzzy associative memories has been intro- duced in the measuring process, the control limits for the control process have been obtained, and the efficiency and reliability of the system have been determined by examining the simu- lated reasoning control values.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.21-26
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• 1998

Numerical simulations for various fluid flows require enormous computing time during iterations. In order to solve this problem, several techniques have been proposed. A SOR method is one of the effective methods for solving elliptic equations. However, it is very difficult to find the optimum relaxation factor, the value of this factor for practical problems used to be estimated on the basis of expertise. In this paper, the implication of the relaxation factor are translated into fuzzy control rules on the basis of the expertise of numerical analysers, and fuzzy controller incorporated into a numerical algorithm. From two cases of study, Poisson equation and cavity flow problem, we confirmed the possibility of computational acceleration with fuzzy logic and qualitative reasoning in numerical simulations. Numerical experiments with the fuzzy controller resulted in generating a good performance.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.163-168
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• 2010

A linguistic case-based fuzzy reasoning (LCBFR) based on standardized parametric membership functions (SPMF) is proposed. It provides an efficient mechanism for a fuzzy reasoning within linear time complexity. Thus, it can be used to improve the speed of fuzzy reasoning. In the process of LCBFR, linguistic case indexing and retrieval based on SPMF is suggested. It can be processed relatively fast compared to the previous linguistic approximation methods. From the engineering viewpoint, it may be a valuable advantage.