• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.737-744
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• 2000

This paper resorts to rock mass rating and rock mass quality to draw value based on the evaluation of rock and to draw interrelation formula in relation to rock mass quality, A comparative analysis was given of survey values reported in the existing documents. This paper has tried to find out the relationship between RMR and Q System for the sake of choosing rational reinforcing patterns and of the safety of tunnels. The results run as follow: RMR=8.251n(Q)+43.83. This paper has also tried to find out the relationship between RMR and Q System by using Fuzzy Approximate Reasoning Concept. We suggest that those in charge should not depend on a single system only after evaluating the classification of rocks, and compare one result with another for the good of keeping track of the condition of base rocks in a better way.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1239-1241
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• 1999

In this paper, we propose real time transient stability assessment and energy margin estimation using fuzzy approximate reasoning. The proposed method used rotor angle, kinetic energy and acceleration power of generators at clearing time as fuzzy input. In order to calculate energy margin in transient energy function (TEF), we obtained controlling unstable equilibrium point (UEP) using mode of disturbance procedure (MOD). The proposed algorithm is tested on 4-machine, 6-bus, 7-line power system to prove of effectiveness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.532-538
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• 1998

In this paper, a new adaptive fuzzy inference method using neural network based fuzzy reasoning is proposed to make a fuzzy logic control system more adaptive and more effective. In most cases, the design of a fuzzy inference system rely on the method in which an expert or a skilled human operator would operate in that special domain. However, if he has not expert knowledge for any nonlinear environment, it is difficult to control in order to optimize. Thus, using the proposed adaptive structure for the fuzzy reasoning system can controled more adaptive and more effective in nonlinear environment for changing input membership functions and output membership functions. The proposed fuzzy inference algorithm is called adaptive neuro-fuzzy control(ANFC). ANFC can adapt a proper membership function for nonlinear plant, based upon a minimum number of rules and an initial approximate membership function. Nonlinear function approximation and rotary inverted pendulum control system ar employed to demonstrate the viability of the proposed ANFC.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.91-109
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• 2004

This study investigates the effectiveness of a hybrid approach using fuzzy sets that describe approximate phenomena of the real world. Compared to the other existing techniques, the approach handles inexact knowledge in common linguistic terms as human reasoning does it. Integration of fuzzy sets with case-based reasoning (CBR) is important in that it helps to develop a successful system far dealing with vague and incomplete knowledge which statistically uses membership value of fuzzy sets in CBR. The preliminary results show that the accuracy of the integrated fuzzy-CBR approach proposed for this study is higher that of conventional techniques. Our proposed approach is applied to corporate bond rating of Korean companies.

• 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.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.127-129
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• 2008

This paper describes a method of approximate reasoning for fuzzy control of servo system, based on decomposition of -level fuzzy sets. It is propose that logic circuits for fuzzy PI+PD are a body from fuzzy inference to defuzzificaion in cases where the output variable u directly is generated PWM. The effectiveness for robust and faster response of the fuzzy control scheme is 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 successfully demonstrated.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.241-250
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• 2006

Although walking is an important transport mode which should be promoted, realistic studies about walking is not sufficient. Especially, due to the transportation planning oriented toward automobile, there is not realistic analysis method for walking in the Highway Capacity Manual. Therefore, in this study the fuzzy approximate reasoning was employed to build a model for the analysis of walkways service level. For the input variable the noise level and brightness as well as the pedestrian flow rate were employed and the output variable was the walking satisfaction degree. The fuzzy models were constructed for daytime and nighttime separately. The forecastability analysis for the models were conducted using $R^2$, MAE and MSE. The values of them for the daytime model are 0.802, 0.729 and 0.735 respectively and the values for nighttime are 0.893, 0.878 and 0.860 respectively, so it can be said that the models explain the real situation well. As a result of this study, it can be concluded that the noise level has stronger effects to walking satisfaction then the brightness in night.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.