• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.243-246
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• 1998

A predictive controller is designed based upon stochastic methods for compensation of network time delay which caused by the spatial separation between controllers and actuators. Current commands are generated by using time varying probability functions which can be defined according to the values of previous control inputs and actual outputs. To demonstrate the effect of this control methodology, simulation experiments are performed. The results show that even an unstabilized system by a long time delay can be stabilized with this predictive controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.144-148
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• 1989

We solve the inverse kinematics problems in robotics by employing a neural network. In the practical situation. it is not easy to obtain the exact inverse kinematics solution, since there are many unforeseen errors such as the shift of a robot base the link's bending, et c. Hence difficulties follow in the trajectory planning. With the neural network, it is possible to train the robot motion so that the robot follows the desired trajectory without errors even under the situation where the unexpected errors are involved. In this work, Back-Propagation rule is used as a learning method.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.303-310
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• 2006

The sorting algorithms have been developed to take advantage of distributed computers. But the speedup of parallel sorting algorithms decrease rapidly with increased number of processors due to parallel processing overhead such as context switching time and inter-processor communication cost. In this paper, we propose a parallel sorting method which provides linear speedup of an optimal serial algorithm for a system with a large number of processors. This algorithm may even provide superlinear speedup for a practical system. The algorithm takes advantage of an interconnection network properties and its protocol.

• 한국시스템다이내믹스연구
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• 제8권2호
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• pp.5-23
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• 2007

According to the system dynamics model of this study, if there is a significant network effect on vehicle operating costs, it is difficult to achieve the shift to AFV even in the long term without a policy intervention because the car market is locked in to the current structure. Network effect can be caused by an increasing return to scale in fuel supply sector as well as in maintenance service sector. It is also related to the fact that the reliability and awareness of consumers on new products increases with the growth of the market share of the new products. There are several possible policy options to break the 'locked in' structure of car market, such as subsidy on vehicle price (capital cost), subsidy on fuel (operating cost) and niche management policy. Combined policy options would be more effective than relying on a single policy option to increase the market share of AFV.

• 제어로봇시스템학회논문지
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• 제10권2호
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• pp.103-111
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• 2004

In this survey paper, we attempt to introduce the subjects of fuzzy logic and neural network technology for flight control systems based on completed and ongoing research programs other developed countries. Also, it is prepared with intention of providing the reader with an overview of related topics and a basic concepts of fuzzy logic and neural network control. The focus is on relatively practical control schemes realistically applicable in the area of flight control system design that could find its usage in the near future in our country. It is hoped that this paper will serve as a useful reference and even concepts provide solutions far current problems and future designs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.111-114
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• 1996

This paper proposes a robust control method using Universal Learning Network(U.L.N.) and second order derivatives of U.L.N.. Robust control considered here is defined as follows. Even if external input (equal to reference input in this paper) to the system at control stage changes awfully from that at learning stage, the system can be controlled so as to maintain a good performance. In order to realize such a robust control, a new term concerning the perturbation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivative of the criterion function with respect to the parameters.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2101-2105
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• 2003

Recently the number of Internet users has very sharply increased, and the number of intrusions has also increased very much. Consequently, security products are being developed and adapted to prevent systems and networks from being hacked and intruded. Even if security products are adapted, however, hackers can still attack a system and get a special authorization because the security products cannot prevent a system and network from every instance of hacking and intrusion. Therefore, the researchers have focused on an active hacking prevention method, and they have tried to develop a traceback system that can find the real location of an attacker. At present, however, because of the characteristics of Internet - diversity, anonymity - the real-time traceback is very difficult. To over-come this problem the Network-based Real-Time Connection Traceback System (NRCTS) was proposed. But there is a security problem that the victim system can be hacked during the traceback. So, in this paper, we propose modified NRCTS with connection redirection technique. We call this traceback system as Connection Redirected Network-based Real-Time Connection Traceback System (CR-NRCTS).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.432-435
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.

• 한국경영과학회지
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• 제24권2호
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• pp.81-94
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• 1999

ATM network design procedure is different from the current circuit or packet networks design procedure because of the variety of the offered services and the variability of requested bandwidth for each connection of ATM network. A number of optimization models for the link dimensioning of ATM network design have been proposed in the literature. However, most of the literature did not consider the modularity of resources allocated to a transmission path and the non-bifurcation of a VP link over the more than one TP, which are standardized in recent ITU-T Recommendations. In thIs paper, we propose a mathematical model for link dimensioning of ATM networks, based on the network synthesis method and a generalized bin-packing problem. The suggested model satisfies the constraints mentioned in the ITU-T Recommendations. We also propose efficient and practical algorithms for the suggested model. Computational experiment shows that the suggested algorithm gives efficient solutions even for moderate and large-sized networks within reasonable time.

• 전기학회논문지P
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• 제52권1호
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.