• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Korean Management Science Review
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• v.14 no.2
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• pp.81-90
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• 1997

Network approach is used to find the shortest paths and transportation time between the subway stations in Seoul, Korea. Because of transfer stations, we reconstruct the subway network to compute the shortest routes and corresponding transportation times. The reconstructed network is useful to obtain desired information because it can handle the transfer time between tracks. Time and route information about the subway system is obtained and it will be displayed in the subway guide board at each station. Then, all passengers can have the information of shortest route to a destination and corresponding transportation time.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.987-994
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• 2000

A Linear Pulse Motor (LPM) is a direct drive motor that has good performance in terms of accuracy, velocity and acceleration compared to the conventional rotating system with toothed belts and ball screws. However, since an LPM needs supporting devices which maintain constant air-gap and has strong nonlinearity caused by leakage magnetic flux, friction and cogging, etc., there are many difficulties in improvement on accuracy with conventional control theory. Moreover, when designing the position controller of LPM, the modeling error and load variations has not been considered. In order to compensate these components, the neural network with conventional feedback controller is introduced. This neural network of feedback error learning type changes the current commands to improve position accuracy. As a result of experiments, we observes that more accurate position control is possible compared to conventional controller.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.203-207
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• 2009

WiBro(Mobile WiMAX) has gained momentum as a top candidate to deliver the dream of full mobile wireless internet. To save cost and time in WiBro network design, simulation tool has to deploy powerful and useful analysis functions. If path loss model is more accurate, the reliability of analysis result of simulation tool will be much improved. So we emphasize on the importance of pathloss model in WiBro network design in this paper. For this, we introduce to three kinds of pathloss models(SUI, SCM, SCM-E) supposed properly models in WiBro RF (Radio Frequency) environment. Also we treat from basic theory to practical substance on the pathloss model to adopt WiBro network design/optimization. Finally, we describe about wireless network analysis tool named 'CellPLAN(R)' and techniques possible to improvethe accuracy of pathloss model.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.127.1-127
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• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.3-102
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• 2001

In this paper, we propose a novel method that can detect face region effectively with fuzzy theory and neural network We make fuzzy rules and membership functions to describe the face color. In this algorithm, we use a perceptually uniform color space to increase the accuracy and stableness of the nonlinear color information. We use this model to extract the face candidate, and then scan it with the pre-built sliding window by using a neural network-based pattern-matching method to find eye. A neural network examines small windows of face candidate, and decides whether each window contains eye. We can standardize the face candidate geometrically with detected eyes.

• IE interfaces
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• v.14 no.4
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• pp.348-355
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• 2001

This paper suggests a detailed design of APS(Advanced Planning & Scheduling) system using the DBR (Drum-Buffer-Rope) which is a finite capacity scheduling logic of TOC(Theory of Constraints). Our design is composed of four modules; Network, Buffer, Drum and Subordination. The Network module defines the Product Network which is built from BOM and routings. The Buffer module inserts the Buffers into the Product Network. The Drum module describes detail procedures to create Drum Schedule on the CCR(Capacity Constraint Resource). The Subordination module synchronizes all non-constraints to the constraints by determining the length of Rope. This design documented by ARIS.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.361-361
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• 2000

In the 2-mass system with flexible shaft, a torsional vibration is often generated because of the elastic elements in torque transmission as the newly required speed response which is very close to the primary resonant frequency. This vibration makes it difficult to achieve quick responses of speed and disturbance rejection. In this paper, 2-mass system is designed by using pole placement based on optimal control theory fur fast speed response and torsional vibration elimination and using neural network for disturbance rejection in particular. The simulation results show that the proposed controller based on neural network and full state feedback controller has better performance than 려ll state feedback controller, especially fur disturbance rejection.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.151-151
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• 2000

The objective of this paper is to develop the new robust and adaptive control system against external environments as applying the probabilistic recognition which is one of the inherent properties of immune system, ability of learning and memorization, and regulation theory of immune network to the system under engineering point of view. In this paper, HIA(Humoral Immune Algorithm) PID controller using Neural Network Identifier was proposed to drive the autonomous guided vehicle(AGV) more effectively. To verify the performance of the proposed HIA PID controller, some experiments for the control of steering and speed of that AGV are performed.