• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.383-385
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• 1997

Back-propagation is one of the efficient algorithms used to nonlinear optimizations or controls. In spite of its structual simplicity and learning ability, learning time is very long or bad case converge local minimum on complicate input patterns. In order to improve these matters varing learning rate and momentums were proposed. In this paper, to improve its performance fuzzy is adjusted in parameters, learning rate and momentums. Parameters are adjusted by errors and change of errors adaptively. In order to evaluate proposed method simulated with MATLAB on inverted pendulum.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2763-2765
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• 2000

The interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition. operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. The objective of this study is to design a fuzzy logic controller (FLC) which controls the position of excavator's attachment. This approach enables the transfer of human heuristics and expert knowledge to the controller. Expeiments are carried out to check the performance of the FLC.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.8
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• pp.515-522
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• 2010

In order to improve the efficiency of a transformer cooling system, the intelligent algorithm was developed. The intelligent algorithm is composed of a setpoint algorithm and a control algorithm. The setpoint algorithm was developed by the neural network, and the control algorithm was developed by the fuzzy logic. These algorithms were used for the control of a blower and an oil pump of the transformer cooling system. In order to analyse performances of these algorithms, the dynamic model of a transformer cooling system was used. Based on various performance tests, energy savings and stable controls of a transformer cooling system were observed. Therefore, control algorithms developed for this study may be effectively used for the control of a transformer cooling system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1231-1234
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• 2003

A sensor working in outdoor may generate some faulty signal owing to dust and high temperature. This paper describes an intelligent sensor system and controller which has a reconstruction mechanism for faulty signal. The faulty signals are dievided into two types as linear distortion and non linear distortion, respectively. The linear distorted signal is due to dust, and non linear distorted signal is due to physical breakdown of sensor or high temperature. These distorted signal have been reconstructed by the proposed method based on polynomial regression method and principal component analysis approach.. The proposed method has been applied to sun tracking system working in outdoor. For a robust and precision control of sun tracker, a fuzzy controller was also proposed. The fuzzy controller controls the tracker by using the collected sensor signal. The tolerance of the position control is within 1.5 degree. To show the validity of the developed system, some experiments in the field were illustrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.857-864
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• 2004

In this paper, a new speed pattern generation method is proposed to offer various speed patterns for the traffic changers, with the comfortable driving and the rapid transportation speed that are two important factors to determine elevator speed pattern. To reduce the speed shift impulse, acceleration and deceleration times are appropriately adjusted to the elevator system when start and stop. In order to improve transportation capability, the jerk is also adjusted to the traffic change. Using fuzzy inference system with 2 input variables and 1 output, the elevator system controls precisely, with the proposed speed pattern.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1059-1065
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• 2005

In this study, an nutrient solution control system have been designed and implemented, which controls the density of nutrient and the nutrient supply automatically using embedded real time operating system and fuzzy control algorithm. The factors which affect the growth of crop consist of solar radiation, external temperature, and external humidity. Also, nutrient temperature, electric conductivity(ED, and pH are monitored. According to the surveyed results, a fuzzy control algorithm for nutrient control is developed in order to control the density of nutrient and the nutrient supply. The exclusive embedded controller which consists of an embedded real time operating system, a korean LCD, and a graphic is developed for common users.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.180-180
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• 2000

The shape of cold strip for the stainless process has been become issue in quality recently, and hence POSCO (Pohang Iron & Steel Co., Ltd) developed an automatic control system for strip shape in the sendzimir mill. The strip shape is measured by an outward measuring roll and is controlled by As_U roll and first intermediate roll. As_U roll consists of 8 saddles, which are controlled vertically. The fist intermediate rolls, which are controlled horizontally, consist of two pairs of rolls up and down. A developed shape control system is applied to real plant by using fuzzy logic and neural network method to control actuators; As_U roll and first intermediate roll. This system composes mainly of three parts as a real-time system, input to output conditioner board, and man-machine interface. The actual shape is recognized by neural network and converted into symmetric shape. The fuzzy controller, based on the shape from neural network and sensor, controls positions of the As_U roll and first intermediate roll. This paper verifies the shape controller performance. The experiments are made on line for the sendzimir mill. The shape control performance shows very efficient for the target tracking, shape symmetry, and fluctuation of shape.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.105-116
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• 2001

According to increase of the factory-automation's(FA) in the field of production, the autonomous guided vehicle's(AGV) role is also increased, The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study. the research about ac1ion base system to evolve by itself is also being actively considered In this paper. we composed an ac1ive and effective AGV fuzzy controller to be able to do self-organization, For composing it. we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. self-organizing controlled(SOC) fuzzy controller proposed in this paper is capable of Self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.483-486
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• 1999

In this paper, we present a real-time hand gesture recognition system that controls motion of a human avatar based on the pre-defined dynamic hand gesture commands in a virtual environment. Each motion of a human avatar consists of some elementary motions which are produced by solving inverse kinematics to target posture and interpolating joint angles for human-like motions. To overcome processing time of the recognition system for teaming, we use a Fuzzy Min-Max Neural Network (FMMNN) for classification of hand postures