• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.546-557
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• 2012

In Conventional Combined Vector and Direct Controls (VC-DTC) of induction motor, stator current is very rich in harmonic components. It leads to high torque ripple of induction motor in high and low speed region. To solve this problem, a control method based on the concept of fuzzy logic approach is used. The control scheme proposed uses stator current error as variable. Through the fuzzy logic controller rules, the choice of voltage space vector is optimized and then torque and speed are controlled successfully with a less ripple level in torque response, which improve the system's performance. Simulation results trough MATLAB/SIMULINK${(R)}$ software gave results that justify the claims.

• International Journal of Control, Automation, and Systems
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• 제4권1호
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• pp.17-29
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• 2006

A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.123-123
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• 2003

More elaborated methods allowing the usage of binary classifiers for the resolution of multi-class classification problems are briefly presented. This way of using FSVC to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K FSVC solving a one-per-class decomposition of the general problem.

• 정보처리학회논문지A
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• 제16A권6호
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• pp.411-418
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• 2009

퍼지 클러스터링 기반 벡터 양자화 알고리즘은 퍼지 클러스터링 분석이 벡터 양자화 프로세스 초기단계에서 초기화에 덜 민감하게 하기 때 문에 데이터 압축 분야에서 널리 사용되어 왔다. 하지만, 퍼지 클러스터링 처리는 훈련 벡터 공간에 포함된 불확실한 양적 공식의 복잡한 프레 임워크 때문에 상당한 계산량이 요구된다. 이러한 상당한 계산량 부하를 극복하기위해 본 논문은 4,096 프로세싱 엘리먼트로 구성된 어레이 아 키텍처를 이용하여 퍼지 벡터 양자화 알고리즘의 병렬 구현을 제안한다. 제안하는 병렬 구현은 4,096 프로세싱 엘리먼트를 이용하여 클러스터 링 프로세스 동안 효과적인 벡터 할당 정책을 적용함으로써 계산적으로 효율적인 솔루션을 제공한다. 모의실험 결과, 제안한 병렬 구현은 기존 의 다른 어레이 아키텍처를 이용한 구현보다 성능 및 효율 측면에서 상당한 향상을 보였다. 또한동일한 130nm 기술에서 제안한 병렬 구현은 오늘날의 ARM이나 TI DSP 프로세서를 이용한 구현과 비교하여 약 1000배의 성능 향상 및 100배의 에너지 효율 향상을 보였다. 이 결과들은 향상된 성능 및 에너지효율에서 제안한 병렬 구현의 잠재가능성을 입증한다.

• Nuclear Engineering and Technology
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• 제40권7호
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• pp.607-614
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• 2008

Since pipes with wall-thinning defects can collapse at fluid pressure that are lower than expected, the collapse moment of wall-thinned pipes should be determined accurately for the safety of nuclear power plants. Wall-thinning defects, which are mostly found in pipe bends and elbows, are mainly caused by flow-accelerated corrosion. This lowers the failure pressure, load-carrying capacity, deformation ability, and fatigue resistance of pipe bends and elbows. This paper offers a support vector regression (SVR) model further enhanced with a fuzzy algorithm for calculation of the collapse moment and for evaluating the integrity of wall-thinned piping systems. The fuzzy support vector regression (FSVR) model is applied to numerical data obtained from finite element analyses of piping systems with wall-thinning defects. In this paper, three FSVR models are developed, respectively, for three data sets divided into extrados, intrados, and crown defects corresponding to three different defect locations. It is known that FSVR models are sufficiently accurate for an integrity evaluation of piping systems from laser or ultrasonic measurements of wall-thinning defects.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.964-969
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• 2007

본 논문은 원본 영상과 은닉 영상의 좋은 압축률과 만족할만한 이미지의 질, 그리고 외부공격에 강인한 영상 은닉의 한 방법을 제안한다. 이 워터마킹 방법은 특이치 분해와 퍼지 군집화 기반 벡터양자화를 이용한다. 실험에서는 은닉된 영상의 비가시성과 외부공격에 대한 강인성을 증명하였다. 이 워터마킹기법의 장점은 워터마크된 영상이 이미 압축되어 있으므로 압축과정과 동시에 저작권 보호에 이용할 수 있다는 장점이 있다.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1177-1186
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• 2017

This paper presents a torque ripple reduction method of direct torque control (DTC) using fuzzy controller with optimal selection strategy of voltage vectors in a five-phase induction motor. The conventional DTC method has some drawbacks. First, switching frequency changes according to the hysteresis bands and motor's speed. Second, the torque ripple is rapidly increased in long control period. In order to solve these problems, some/most papers have proposed torque ripple reduction methods by using the optimal duty ratio of the non-zero voltage vector. However, these methods are complicated in accordance with the parameter. If this drawback is eliminated, the torque ripple can be reduced compared with conventional method. In addition, the DTC can be simply controlled without the use of the parameter. Therefore, the proposed algorithm is changing the voltage vector insertion time by using the designed fuzzy controller. Also, the optimized voltage vector selection method is used in accordance with the torque error. Simulation and experimental results show effectiveness of the proposed control algorithm.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.609-617
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• 2006

A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.494-498
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• 1996

Three fuzzy input space partitoining methods, which are grid, tree, and scatter method, are mainly used until now. These partition methods represent good performance in the modeling of the linear system and nonlinear system with independent modeling variables. But in the case of the nonlinear system with the coupled modeling variables, there should be many fuzzy rules for acquiring the exact fuzzy model. In this paper, it shows that the fuzzy model is acquired using transformed modeling vector by linear transformation of the modeling vector.