• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.483-485
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• 1998

Neural Network has advantages of learning and normalizing capabilities. Fuzzy controller is based on a fuzzy logic that is so effective to represent uncertain phenomena of real world and make its approximation. In this paper, Fuzzy Neural Network controller which equipped with adaptive control algorithm is described. Proposed Fuzzy Neural Network Controller applied to a ball on beam system which have nonlinear characteristics shows a good performance.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.630-632
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• 1997

A fuzzy controller is based on o fuzzy logic that is so effective to represent uncertain phenomena of real world end mote its approximation. In this paper, A fuzzy controller which equipped with adaptive control algorithm is described. Proposed fuzzy controller applied to a boll on boon system shows a good performance.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.69-78
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• 2002

This paper addresses a method to automatically detect out a person's face from a given image that consists of a hair and face view of the person and a complex background scene. Out method involves an effective detection algorithm that exploits the spatial distribution characteristics of human skin color via an adaptive fuzzy color classifier (AFCC), The universal skin-color map is derived on the chrominance component of human skin color in Cb, Cr and their corresponding luminance. The desired fuzzy system is applied to decide the skin color regions and those that are not. We use RGB model for extracting the hair color regions because the hair regions often show low brightness and chromaticity estimation of low brightness color is not stable. After some preprocessing, we apply convex-hull to each region. Consequent face detection is made from the relationship between a face's convex-hull and a head's convex-hull. The algorithm using the convex-hull shows better performance than the algorithm using pattern method. The performance of the proposed algorithm is shown by experiment. Experimental results show that the proposed algorithm successfully and efficiently detects the faces without constrained input conditions in color images.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1865-1869
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• 2004

A new model for the propagation prediction for mobile communication network inside building is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.

• Journal of Ocean Engineering and Technology
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• v.12 no.3 s.29
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• pp.96-102
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• 1998

This paper presents to create a SAC(Sectional Area Curve) using an ANFIS(Adaptive-Network-based Fuzzy Inference System). First, it defines SACs of parent ships by using a B-spline approximation and a genetic algorithm and accumulates a database about SAC's control points. Second, it learns an ANFIS from parent ship data, which are related with principal dimensions and SAC's control points. This process is to model an ANFIS for SAC inferreice. When an ANFIS modeling is completed, we can determine a SAC through an ANFIS inferring.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1118-1120
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• 1998

In this study, an adaptive evolutionary computation (AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. We applied the AEC to design of fuzzy logic controllers for a PSS (power system stabilizer). FLCs for PSS controllers are designed for damping the low frequency oscillations caused by disturbances such as tile sudden changes of loads, outages in generators, transmission line faults, etc. The membership functions of FLCs is optimally determined by AEC.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.25-29
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• 2005

무선 센서 네트워크에서 센서 노드는 배터리와 같은 제한적인 전원을 가지고 있기 때문에, 센서 노드의 수명을 연장하기 위하여 에너지 효율성을 고려한 다양한 라우팅 프로토콜이 연구되고 있다. 하지만 기존에 제안된 라우팅 프로토콜들은 특정 상황 및 응용에 특화되어 있기 때문에, 하드웨어에 내장시킨 단일 라우팅 프로토콜만으로는 동적으로 변화하는 네트워크 상에서 에너지 효율성을 보장할 수 없다는 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하기 위하여 퍼지 추론 시스템을 기반으로, 다양한 후보 라우팅 프로토콜 중 현재 네트워크 상황에 적합한 라우팅 프로토콜을 선택하여, 이를 동적으로 센서 노드에 적재 혹은 교체하도록 하는 퍼지 기반의 적응형 라우팅 알고리즘을 제안한다. 또한 시뮬레이션을 수행하여 동적인 네트워크 상황 하에서 제안된 라우팅 알고리즘을 사용한 경우가 기존의 단일 라우팅 프로토콜만을 사용한 경우보다 에너지 효율적임을 검증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.426-435
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• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.

• Journal of Korean Association for Spatial Structures
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• v.15 no.2
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• pp.105-112
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• 2015

A shared tuned mass damper (STMD) was proposed in previous research for reduction of dynamic responses of the adjacent buildings subjected to earthquake loads. A single STMD can provide similar control performance in comparison with two traditional TMDs. In previous research, a passive damper was used to connect the STMD with adjacent buildings. In this study, a smart magnetorheological (MR) damper was used instead of a passive damper to compose an adaptive smart STMD (ASTMD). Control performance of the ASTMD was investigated by numerical analyses. For this purpose, two 8-story buildings were used as example structures. Multi-input multi-output (MIMO) fuzzy logic controller (FLC) was used to control the command voltages sent to two MR dampers. The MIMO FLC was optimized by a multi-objective genetic algorithm. Numerical analyses showed that the ASTMD can effectively control dynamic responses of adjacent buildings subjected to earthquake excitations in comparison with a passive STMD.