• Computers and Concrete
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• 제21권1호
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• pp.103-110
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• 2018

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

• Journal of Biosystems Engineering
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• 제20권1호
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.93-99
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• 2003

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권3호
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.490-493
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• 1996

This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.

• 한국정밀공학회지
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• 제15권9호
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• pp.56-67
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• 1998

This paper introduces a friction compensation fuzzy logic controller, which utilizes a rule-based approach. The paper explains the algorithm of the proposed controller and compares it with a conventional PID controller in simulations and experiments. For the experiments, the two control algorithms were implemented on a 3-axis milling machine in contour milling. These simulation and experimental analyses show that the proposed fuzzy logic controller has superior performance over conventional PID controllers In terms of part contour accuracy.

• 한국정보전자통신기술학회논문지
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• 제5권3호
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• pp.158-163
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• 2012

본 논문에서 나이브 베이지안 알고리즘, 데이터 마이닝, Fuzzy logic을 이용하여 이상 공격과 오용 공격을 탐지하는 하이브리드 침입탐지시스템인 FHIDS(Fuzzy logic based Hybrid Intrusion Detection System)을 설계하였다. 본 논문에서 설계한 FHIDS의 NB-AAD(Naive Bayesian based Anomaly Attack Detection)기법은 나이브 베이지안 알고리즘을 이용해 이상 공격을 탐지하고, DM-MAD(Data Mining based Misuse Attack Detection)기법은 데이터 마이닝 알고리즘을 이용하여 패킷들의 연관 규칙을 분석하여 새로운 규칙기반 패턴을 생성하거나 변형된 규칙 기반 패턴을 추출함으로써, 새로운 공격이나 변형된 공격을 탐지한다. 그리고 FLD(Fuzzy Logic based Decision)은 NB-AAD과 DM-MAD의 결과를 이용하여 정상인지 공격인지를 판별한다. 즉, FHIDS는 이상과 오용공격을 탐지 가능하며 False Positive 비율을 감소시키고, 변형 공격 탐지율을 개선한 하이브리드 공격탐지시스템이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.532-535
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• 1997

This paper describes the design of a fuzzy-logic controller for a differential-drive mobile robots. This controller uses absolute position information to modify control parameters to compensate the orientation error. CC-Control method is compensated for the internal error by wheel encoders and the fuzzy-logic control provides compensation for external errors. The validities of the proposed scheme is evaluated using simulation.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.352-357
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• 2007

This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can't deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. We proposed Real-time Fuzzy Temporal Logic Framework(RFTLF) to model Fuzzy Discrete Event System. It combines Temporal Logic Framework with Fuzzy Theory. We represented the model of traffic signal systems for intersection to have the property of Fuzzy Discrete Event System with Real-time Fuzzy Temporal Logic Framework and designed a traffic signal controller for smooth traffic flow. Moreover, we proposed the method to find the minimum-time route to reach the desired destination with information obtained in each intersection. In order to evaluate the performance of Real-time Fuzzy Temporal Logic Framework model proposed in this paper, we simulated unit-time extension traffic signal controller model of the latest signal control method on the same condition.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.180-183
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• 1996

This work discusses simulation results for the fuzzy logic controller tested the project“Fuzzy Ramp Metering Algorithm Implementation.”The performance objectives were, in order of priority, to maximize total vehicle-miles, maximize mainline speeds, and minimize delay per vehicle while maintaining an acceptable ramp queue. In the fuzzy logic controller, the sensors from the on-ramps were helpful in maintaining reasonable ramp queue and mainline congestion because it considered these factors simultaneously. Each metered ramp had a parameter input file, which allowed the controller to be modified without recompiling the software. Consequently, maintenance costs should be minimal.