• The Journal of Korean Institute for Practical Engineering Education
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• v.5 no.1
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• pp.1-13
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• 2013

For a basic engineering education the confirmation and verification of the deductive Inference was studied and the principle of probability inference was applied. The background of introduction of deductive Inference and its test method was mentioned, and historic arguments on the compatibility of deductive statistical inference was summarized and analyzed. Philosophical arguments on the deductive confirmation for engineering experiments was introduced. Premise, procedure, and control of the experiments are studied. As an example of the deductive probability inference three groups of experimental data were used in order to find successful inferences respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.3-96
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• 2001

In this paper the self-organized and fuzzy inference used stochastic gradient ascent method is proposed. Fuzzy rule and fuzzy set increase as occasion demands autonomously according to the observation information. And two rules(or two fuzzy sets)becoming to be similar each other as progress of learning are unified. This unification causes the reduction of a number of parameters and learning time. Using fuzzy inference and making a rule with an appropriate state division, our proposed method makes it possible to construct a robust reinforcement learning system.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.863-868
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• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.1-7
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• 2014

The variable control in the inductive inference for the confirmation and verification when the experimental data are collected is studied by applying the principle of probability inference. The control in engineering experiments is to protect any effect by of intervening variable except primary independent variable on the dependent variable. By the special condition the possibility for developing a phenomenon will be maximized; otherwise, by the extraneous condition the possibility for developing a phenomenon will be minimized. By doing so, the control may provide insurance for the causal relationship between the certain prior event (independent variable) and the post-event (the dependent variable). Some experiments by using both elliptical trainer and tread mill under the variable control are performed in order to find the relations between the energy expenditure, the respiratory exchange ratio (RER), and the heart rate (HR) against the exercise speed.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.209-218
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• 2001

Ultrasonic motors are newly developed motors which are expected to be useful as actuators in many practical systems such as robot arms or manipulators because of several advantages against the electromagnetic motors. However, the precise control of the ultrasonic motor is generally difficult due to the absence of appropriate and rigorous mathematical model. Furthermore, owing to heavy nonlinearity, the position control of a pendulum system driven by the ultrasonic motor has a problem that control method using multiple neural network controllers based on a fuzzy inference system that can determine the initial position of the pendulum in the beginning of control operation. In addition, and appropriate neural network controller that has been learned to operate well at the corresponding initial position is adopted by switching schemes. The effectiveness of the proposed method was verified and evaluated from real experiments.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1104-1106
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• 1996

In this paper, a hierarchical fuzzy controller is proposed for the stabilization control of the inverted pendulum system. The design of controller for that system is difficult because of its complicated nonlinear mathematical model with unknown parameters. Conventional fuzzy control strategy based only on dynamics of pendulum made have failed to stabilize. However, proposed control strategies are to swing pendulum from natural stable up equilibrium point to an unstable equilibrium point and are to transport a cart from an arbitrary position toward a center of rail. Thus, the proposed fuzzy stabilization controller have a hierarchical fuzzy inference structure; that is, the lower level is for inference interface for the virtual equilibrium point and the higher level one for the position control of cart according to the firstly inferred virtual equilibrium point.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.4
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• pp.75-81
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• 2001

Fuzzy supervisory control method is studied for the active control of earthquake excited structures. The proposed algorithm supervises and tunes previously designed control gains by evaluating the state of a structure through the fuzzy inference mechanism, which uses the information of relative displacements and velocities. Example designs and numerical simulations of earthquake exited three degrees of freedom structures are performed to prove the validity of the proposed control algorithm. Comparative results with conventional LQR method show that the proposed method is effective for the vibration suppression of earthquake excited structures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1270-1273
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• 1993

Fuzzy optimal control is considered. An optimal sequence of controls is sought best satisfying fuzzy constraints on the controls and fuzzy goals on the states (outputs), with a fuzzy system under control Control over a fixed and specified, implicitly specified, fuzzy, and infinite termination time is discussed. For computational efficiency a small number of reference fuzzy staters and controls is to be assumed by which fuzzy controls and stated are approximated. Optimal control policies reference fuzzy states are determined as a fuzzy relation used, via the compositional rule of inference, to derive an optimal control. Since this requires a large number of overlapping reference fuzzy controls and states implying a low computational efficiency, a small number of nonoverlapping reference fuzzy states and controls is assumed, and then interpolative reasoning is used to infer an optimal fuzzy control for a current fuzzy state.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.674-679
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• 1987

Process failures can occur at any time during operation, so a continuous effort of fault detection, diagsis, and correction is required. Expert system paridigm has been regarded as a promising approach to real time process supervisory control especially to fault diagnosis. The most important aspects of fault diagnostic expert systems(FDES) are the problem-solving inference strategy and knowledge organizations. The necessity of FDES, the nature of diagnostic knowledge, the representation of knowledge, and the inference mechanism of FDES, et al. are described, which are announced by previous researchers. And the existing FDES are categorized and critically reviewed in this work.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.921-924
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• 1993

For the purpose of applying to a high-speed control system, such as engine control for automobile application, we propose an architecture of a fuzzy inference processor, which can realize high-speed inference, high-resolution, and can be implemented with small chip area. We have designed a single chip based on the architecture, and confirmed the performance, such as 140 kFLIPS with 8-bit resolution.