• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.13-17
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• 1990

In this paper we propose the force controller using a neural network for a redundant manipulator. Jacobian transpose matrix of a redundant manipulator constructed by a neural network is trained by using a feedback torque as an error signal. If the neural network is sufficiently trained well, the kinematic inaccuracy of a manipulator is automatically compensated. The effectiveness of the proposed controller is demonstrated by computer simulation using a three-link planar robot.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.989-995
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• 2017

Distributed Control System (DCS) communication networks, which use Fast Ethernet with redundant networks for the transmission of information, have been installed in digitalized nuclear power plants. Normally, failover tests are performed to verify the reliability of redundant networks during design and manufacturing phases; however, systematic integrity tests of DCS networks cannot be fully performed during these phases because all relevant equipment is not installed completely during these two phases. In additions, practical verification tests are insufficient, and there is a need to test the actual failover function of DCS redundant networks in the target environment. The purpose of this study is to verify that the failover functions works correctly in certain abnormal conditions during installation and commissioning phase and identify the influence of network failover on the entire DCS. To quantify the effects of network failover in the DCS, the packets (Protocol Data Units) must be collected and resource usage of the system has to be monitored and analyzed. This study introduces the use of a new methodology for verification of DCS network failover during the installation and commissioning phases. This study is expected to provide insight into verification methodology and the failover effects from DCS redundant networks. It also provides test results of network performance from DCS network failover in digitalized domestic nuclear power plants (NPPs).

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1073-1081
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• 2000

This paper proposes a new fault-tolerant technique for bitonic sorting networks which can be used for designing ATM switches based on Batcher-Banyan network. The main goal in this paper is to design a cost-effective fault-tolerant bitonic sorting network. In order to recover a fault, additional comparison elements and additional links are used. A Dynamic Redundant Bitonic Sorting (DRBS) network is based on the Dynamic Redundant network and can be constructed with several different variations. The proposed fault-tolerant sorting network offers high fault-tolerance; low time delays; maintenance of cell sequence; simple routing; and regularity and modularity.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.168-173
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• 1991

A neural optimization network is proposed to control the redundant robot manipulators in an environment with the obstacle. The weightings of the network are adjusted by considering both the joint dexterity and the capability of collision avoidance of joint differential motion. The fuzzy rules are proposed to determine the capability of collision avoidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planner type with three degrees of freedom.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.496-498
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• 2000

현재 이동 컴퓨팅 연구 중에서 이동 호스트의 결함을 효율적으로 대처하는 결함 허용 기법에 관한 연구가 미미한 실정이다. 이동 호스트의 결함 복구 기법인 Lazy 기법은 비용 면에서는 효율적이지만 이동 호스트의 체크포인트를 소유하고 있는 기지국의 결함 발생시 이동 호스트가 결함으로부터 복구될 수 없다는 단점을 가지고 있다. Redundant Lazy 기법은 연결 리스트를 중복화 함으로서 Lazy 기법의 문제점을 해결한 기법이다. 그러나 Redundant Lazy 기법도 체크포인트를 가지고 있는 기지국의 결함이 발생하면 이동 호스트가 복구될 수 없는 경우가 발생한다. 본 논문에서는 이중 셀룰라 네트워크에 Redundant Lazy 기법을 적용하여 이러한 문제를 해결할 수 있는 성능 개선 방안을 논의한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.51-52
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• 2011

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.17-29
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• 2006

A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.687-690
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• 2003

This paper presents the development of Programmable Logic Controller (PLC) and network to design the redundant control system in order to control the high priority process. The industrial process that cannot be shutdown or the effect of the shutting down takes abundantly damage. In this article, we say that the high priority process. The redundant systems are designed for controlling the high priority process that the control system must have many controllers to instead the main controller when it has some error. This paper we designed the redundancy control system by the advantage of the high-speed communication on the PLC’s network. The temperature control system and the traffic light control system used as the case study. Each example processes consist of two sets of controller. Our scheme we can increase the reliability prevents process down time and reduces the cost of opportunity to loss also.