• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.793-800
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• 2004

The purpose of this study is to develope a servo system with faster and more accurate response. This paper describes a method of approximate reasoning for fuzzy control of servo system based on the decomposition of $\alpha$-level fuzzy sets. We propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion cases where the output variable u directly is generated PWM The effectiveness for robust and faster response of the fuzzy control scheme are verified for a variable parameter by comparison with a PID control and fuzzy control A position control of DC servo system with a fuzzy logic controller is demonstrated successfully.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.29-41
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• 1990

An assessment of risk in industrial and urban environments is essential in the prevention of accident and in the analysis of situations which are hazardous to public health and safety. The risk imposed by a particular hazard increases with the likelihood of occurence of the event, the exposure and the possible consequence of that event. In a traditional approach, the calculation of a quantitative value of risk is usually based on an assignment of numerical values of each of the risk factors. Then the product of the values of likelihood, exposure and consequences called risk score is derived. However vagueness and imprecision in mathematical quantification of risk are equated with fuzziness rather than randomness. In this paper, a fuzzy set theoretic approach to risk analysis is proposed as an alternative to the techniques currently used in the area of systems safety. Then the concept of risk evaluation using linguistic representation of the likelihood, exposure and consequences is introduced. A risk assessment model using approximate reasoning technique based on fuzzy logic is presented to drive fuzzy values of risk and numerical example for risk analysis is also presented to illustrate the results.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.273-278
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• 2009

The determination of the associated weights in the theory of ordered weighted averaging (OWA) operators is one of the important issue. Recently, Wang and Parkan [Information Sciences 175 (2005) 20-29] proposed a minimax disparity approach for obtaining OWA operator weights and the approach is based on the solution of a linear program (LP) model for a given degree of orness. Recently, Liu [International Journal of Approximate Reasoning, accepted] showed that the minimum variance OWA problem of Fuller and Majlender [Fuzzy Sets and Systems 136 (2003) 203-215] and the minimax disparity OWA problem of Wang and Parkan always produce the same weight vector using the dual theory of linear programming. In this paper, we give an improved proof of the minimax disparity problem of Wang and Parkan while Liu's method is rather complicated. Our method gives the exact optimum solution of OWA operator weights for all levels of orness, $0\leq\alpha\leq1$, whose values are piecewise linear and continuous functions of $\alpha$.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.662-665
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• 2001

As the fuzzy control is applied to servo system the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$-level set decomposition of fuzzy sets by quantize $\alpha$-cuts. This method can be easily implemented with analog hardware. The influence of quantization levels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of do servo system. It examined useful with experiment for dc servo system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.674-678
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• 2007

Baldwin defined the approximate reasoning using truth function mapping. In paper [4], we defined two truth function mappings and applied these truth function mappings to generalized modus ponens. In this paper, we introduce the results of generalized modus tollens using these two truth function mappings.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.71-83
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• 1999

Conventional symbolic inference systems lack flexibility because they do not well reflect flexible semantic structure of knowledge and use symbolic logic for their basic inference mechanism. For solving this problem. we have recently proposed the 'Connectionist Semantic Network(CSN)' as a model for flexible knowledge representation and inference based on neural networks. The CSN is capable of carrying out both approximate reasoning and commonsense reasoning based on similarity and association. However. we have difficulties in representing general and structured high-level knowledge and variable binding using the connectionist framework of the CSN. In this paper. we propose a hybrid system called SymCSN(Symbolic CSN) that combines a symbolic module for representing general and structured high-level knowledge and a connectionist module for representing and learning low-level semantic structure Simulation results show that the SymCSN is a plausible model for human-like flexible knowledge representation and inference.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.3-9
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• 2016

This study proposes a approximate cost estimation system based on Excel with VBA using weighted CBR(Case Based Reasoning). One characteristic of this system is that it generates the sheet automatically as many as the number of similar case and new estimation when it performs a case learning and a new estimate and cell formula is automatically entered into each sheet. User can be free to compose a combination of attribute factors because they can select up to ten attribute factors. This paper presents an applicability of estimation model for estimating the soil remediation cost when it use a landfarming method. When compared to a estimation model by using average unit cost and optimum multiple regression, this model shows a better result. This study was aimed at landfarming method, but it is expected that a cost estimation model using CBR will be more likely to apply in soil remediation technologies which various remediation technologies and pollutant species exist.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.261-294
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• 1993

Most of expert systems,as a rule-based system,should be convenient to modify a rule and to insert a new rule, which is called modularity of rules. When we think correlated evidences in expert systems. conventional systems are too local to recognize the common origin of the information, and they would update the belief of the hypothesis as if it were supposed by independence soureces. In this paper to overcome such drawbacks we propose Fuzzy Belief Network which is based on the Beysian Network which provide the modulartiy between rules. To build Fuzzy Belief Network, we define nodes and links and propose algorithms for data fusion in individual node and for propagation belief value obtained as a result of data fusion.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.4
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• pp.136-141
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• 1995

Determination and optimization of proper welding condition are very important tasks to be directly related to weld quality and productivity. On this research the relationship between welding parameters and results is investigated systematically. Theoretical method, statistical analysis of experimental data and analysis of empirical knowledge are applied for this work. These results are represented by empirical equations, fuzzy rules and artificial intelligent knowledge forms in the knowledge base. The approximate reasoning of fuzzy expert system and the information in the knowledge base are used for recommendation of suitable welding condition, and optimization of welding parameter which is based on the evaluation of welding results by user.