• Smart Structures and Systems
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• 제15권4호
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• 한국기계가공학회지
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• 제15권2호
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.457-464
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• 2004

Hard Disk Drive (HDD) test system is an equipment for the final test of HDD product by iterative read/write/seek test. This paper proposes an expert system for the fault diagnosis of HDD test systems. The purposed expert system is composed with two cascade inference, fuzzy logic and conventional binary logic. The fuzzy logic determines the possibility of the system fault using the test history data, then, the binary logic inferences the fault location of the test system. The proposed expert system is tested in SAMSUNG HDD production line, KUMI, KOREA, and shows satisfactory results.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2011년도 추계학술대회
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• pp.34-36
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• 2011

In recent years, shipping at the sea has been rapidly grown in marine nations and vessel's collisions are increasing as well. The collision avoidance is one of issues maritime safety. To reduce vessels' collisions, the fuzzy inference system is one of popular algorithms for collision avoidance. In this paper we aim to implement Matlab. Fuzzy logic toolbox software for collision avoidance algorithm. For this we used an original Matlab fuzzy logic toolbox and customized the toolbox for the collision avoidance algorithm.

• 대한의용생체공학회:의공학회지
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• 제18권4호
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• pp.491-498
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• 1997

본 연구에서는 졸음운전 방지를 위한 방법으로 기존의 3단계 경고음법과 fuzzy logic을 이용한 가성도 측정 및 제어법을 시뮬레이션으로 비교 및 분석하였다. 각성상태를 제어하는 방법으로 사용되었던 기존의 각성제어지표는 실 차에는 사용될 경우 효과적이지 못하므로 각성상태에 따른 영역별 Nz와 IRI의 상관분포도를 분석하여 기존의 각성제어지표를 수정하였다. Fuzzy 추론으로는 Sugeno의 방법을 사용하였고 멤버쉽함수와 제어규칙 베이스는 수정된 각성제어지표로부터 결정하였다. 시뮬레이션 결과 60이하의 IRI가 발생되는 경우, Nz의 변화에 따라 두 제어방법 모두 small, medium, big의 경고음이 발생되었으나 3단계 경고음법은 다음 단계의 발생영역이 될 때까지 같은 출력만을 발생한다. 그러나 퍼지추론의 출력은 피검자의 각성수준의 변화에 잘 추종하여 변화되었으므로 3단계 경고음법의 문제점을 해겨할 수 있었고 더욱이 퍼지 추론의 출력과 Nz와의 상관계수(r=0.99)가 매우 높았으므로, 실제 운전시 퍼지추론 방법을 이용한 각성도 평가 및 제어에 적용할 경우 3단계 경고음법 보다 효과적일 것으로 기대된다.

• 한국항해학회지
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• 제8권1호
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• pp.15-42
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• 1984

Many studies have been done in the field of fuzzy logic theory, but it's application to the ship's steering system is few until this date. This paper is to survey the effect of application of fuzzy logic control by new compositional rule of Inference to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and output, and take the inputs derived from deviation angle and it's angular velocity. The Linguistic Control Rules are implemented on the digital computer to verify the performance of the fuzzy logic controller and simulations have been done in six cases of initial condition and disturbance type. Consequently, it was proved that the ship's steering system by introducing the F.L.C. is performed efficiently and less energy loss system compared with the conventional autopilot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.256-267
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• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• 한국정밀공학회지
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• 제19권4호
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• pp.132-139
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• 2002

In GMAW(Gas Metal Arc Welding) processes, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWD (contact-tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using neuro-fuzzy algorithm. Neural networks was applied to design FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks. On the developed inference system of bead geometry using neuro-furzy algorithm, the inference error percent of bead width was within $\pm$4%, that of bead height was within $\pm$3%, and that of penetration was within $\pm$8%. Neural networks came into effect to find the parameters of input membership functions and those of consequence in FL. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• Journal of Advanced Marine Engineering and Technology
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• 제28권2호
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• pp.200-209
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• 2004

The fuzzy control based on $\alpha$-level fuzzy set decomposition. It is known to produce quick response and calculating time of fuzzy inference. This paper derived the embodiment computational algorithm for defuzzification by min-max fuzzy inference and the center of gravity method based on $\alpha$-level fuzzy set decomposition. It is easy to realize the fuzzy controller hardware. based on the calculation formula. In addition. this study proposed a circuit that generates PWM actual signals ranging from fuzzy inference to defuzzification. The fuzzy controller was implemented with mixed analog-digital logic circuit using the computational fuzzy inference algorithm by min-min-max and defuzzification by the center of gravity method. This study confirmed that the fuzzy controller worked satisfactorily when it was applied to the position control of a dc servo system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.50-55
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• 2004

We propose an intgrated Gill environment system having only integer fuzzy operations in the consequent part and the defuzzification stage. In this paper, we also propose an integrated Gill environment system with 4 parallel fuzzy processing units to be operated in parallel on the classification of the sensed image data. In this, we solve the problems of taking longer times as the fuzzy real computations of [0, 1] by using the integer pixel conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. This procedure is performed automatically in the GUI application program. As a Gill environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be operated in parallel manner for MIMO or MISO systems.