• 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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• 1993.11a
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• pp.23-25
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• 1993

This paper presents a design technique of self-organizing fuzzy controller using a learning method of fuzzy inference rule by a gradient method for load frequency control of power system. The membership functions in antecedent part and in consequent part of fuzzy inference rules are tuned by the gradient method. The related simulation results show that the proposed fuzzy controller are more powerful than the conventional ones for reduction of undershoot and deviation of load frequency in steady-state, and for minimization of settling time.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.841-847
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• 1999

For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential. Such informations can be acquired from visual sensors. For robotic assembly, the corrective assembly motion to compensate for such misalignments has to be determined from the measured informations. However, this may not be simply derived from the measured misalignment alone because the part deformation progressively occurs during misalignment compensation. Based on the analysis of flexible parts assembly process, this paper presents a neural net-based inference system that can infer the complex relationship between the corrective motion and the measured information of parts deformation and misalignments. And it verifies the performance of the implemented inference system. The results show that the proposed neural net-based misalignment compensation algorithm Is effective in compensating for the lateral misalignment, and that it can be extended to the assembly tasks under more general conditions.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.119-122
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• 2002

The paper proposed the intelligent inference method which keeps MV(Mobile vehicle) a little way off from men and makes it follow them using fuzzy controller Recognizing positions of MV and Men and distance between them was used to infer movement angle and speed of the MV with multi-ultrasonic sensor and USB camera The very important thing Is that the MV needs to obtain surrounding Information from the sensor and the camera, then It needs to represent those circumstances MV was controlled by inference from the speed and angle which are obtained from sensor and camera. Traveling simulation with a real MV was performed repeatedly to verify the usefulness of the fuzzy logic algorithm which was proposed in this paper. And a successful result of the experiment demonstrated the excellence of the fuzzy logic controller.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.354-361
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• 2004

To improve fault isolation performance of the Bayes isolator, this paper proposes the Fuzzy-Bayes isolator, which uses the Fuzzy-Bayes classifier as a fault isolator. The Fuzzy-Bayes classifier is composed of the Bayes classifier and weighting factor, which is determined by fuzzy inference logic. The Mahalanobis distance derivative is mapped to the weighting factor by fuzzy inference logic. The Fuzzy-Bayes fault isolator is designed for the BLDC motor fault diagnosis system. Fault isolation performance is evaluated by the experiments. The research results indicate that the Fuzzy-Bayes fault isolator improves fault isolation performance and that it can reduce the transition region chattering that is occurred when the fault is injected. In the experiment, chattering is reduced by about half that of the Bayes classifier's.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.852-854
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• 1995

This paper proposes a new identification method that utilizes fuzzy inference in parameter identification. The prosed system has an additional control loop where a real plant has replaced by a plant model. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. In this paper, the tuning method which determines parameters of PID controller automatically is described through applying this algorithm to DC servo motor. And we intend to investigate effectiveness of the method by experiments. This method is effective in auto-tuning because the response of the closed loop has verified. The simulated and the experimental results of the dc servo motor are shown to confirm the viability of this method.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.317-323
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• 2007

Quality of Service (QoS) in the Internet depends on queuing and sophisticated scheduling in routers. In this paper, we address the issue of managing traffic flows with different priorities. In our reference model, incoming packets are first classified based on their priority, placed into different queues with different capacities, and then multiplexed onto one router link. The fuzzy nature of the information on Internet traffic makes this problem particularly suited to fuzzy methodologies. We propose a new solution that employs a fuzzy inference system to dynamically and efficiently schedule these priority queues. The fuzzy rules are derived to minimize the selected Lyapunov function. Simulation experiments show that the proposed fuzzy scheduling algorithm outperforms the popular Weighted Round Robin (WRR) queue scheduling mechanism.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.560-567
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• 2007

The objective of this research is to optimize the designing parameters of the parallel manipulator with large orientation workspace at the boundary position of the constant orientation workspace (COW). The method uses a simple genetic algorithm(SGA) while considering three different kinematic performance indices: COW and the global conditioning index(GCI) to evaluate the mechanism's dexterity for translational motion of an end-effector, and orientation workspace of two angle of Euler angles to obtain the large rotation angle of an end-effector at the boundary position of COW. Total fifteen cases divided according to the combination of the sphere radius of COW and rotation angle of orientation workspace are studied, and to decide the best model in the total optimized cases, the fuzzy inference system is used for each case's results. An optimized model is selected as a best model, which shows better kinematic performances compared to the basis of the pre-existing model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.304-309
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• 1998

In this paper, a fuzzy controller for stabilization of the inverted pendulum system is propose. The facility of this fuzzy controller which has a swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point ($\Phi$veq) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed hierarchical fuzzy inference structur made substantially the inverted pendulum system robust and stable.