• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.82.4-82
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• 2001

Requirements for device control networks differ greatly from those of data (business) networks. Consequently, any control network which uses a filedbus protocol is, in general, different from If network protocol TCP/IP. One then needs to integrate both fieldbus protocol and TCP/IP to realize distributed control over IP network or internet. LonWorks technology provides networked intelligent I/O and controllers which make it a powerful, expandable solution. Connecting these remote Lon Works networks to the Internet can provide a powerful, integrated, distributed control system. This paper suggests a basic concept that be applied to distributed control over IP network or internet. Specially, Lonworks technology that used LonTalk protocol is reviewed as device network and ...

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.2
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.892-895
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• 1991

In this study, it has been refering that disposal of rapidly international information society and artificial intelligence neural networks of the vanguard software technology. This paper is human brain cell structure modeling in order to neural networks realization for order language and computer embodiment of parallel processing. And it is shown that the usage extreme of time saving and correct judgement for business services, Overviews some of the currently popular neural networks architectures, and describes the current state of the neural networks technology.

• 정보처리학회논문지C
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• 제11C권1호
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• pp.99-108
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• 2004

본 논문에서는 공장자동화를 위한 제어네트워크로서의 스위칭 이더넷 네트워크의 성능을 분석한다. 스위칭 이더넷은 네트워크상에서의 충돌을 없앰으로써 실시간 데이터의 전송을 가능하게 하여 준다. 공장자동화 제어네트워크는 일반 컴퓨터 네트워크와는 달리 전송되는 데이터의 양은 적은 반면 실시간 전송을 요한다. 본 논문에서는 이더넷 스위치를 이용한 선형 및 트리 토폴로지의 네트워크에서 제어시스템이 요구하는 전송지연시간 요구를 만족하는지에 대해 분석한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권8호
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.582-585
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• 2001

Requirements for device networks differ greatly from those of data(business) networks. Consequently, any control network technology which uses a fieldbus protocol is, in general, different from IP network protocol TCP/IP. One needs to integrate fieldbus protocol and TCP/IP to realize distributed control over IP network or internet. This paper suggests a basic concept that can be applied to distributed control over IP network or internet. Specifically, LonWorks technology that uses LonTalk protocol is reviewed as device network. LonWorks technology provides networked intelligent I/O and controllers which make it a powerful, expandable solution. It is also addressed that many hardwired PLCs can be replaced by LonWorks devices. Connecting these remote LonWorks networks to the Internet can provide a powerful, integrated, distributed control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.401-403
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• 1992

For the control of robotic manipulators with unknown or uncertain dynamics, leaming control schemes are very effective control schemes for repeated trajectory following tasks. In this class of controllers, control techniques using neural networks have been gaining much attention in recent years.. In this note, we discuss the leaming control techniques using neural networks and propose a new model-based control scheme using multilayered neural networks. Three-layerd neural network is used as a feedback controller to compensate the mismatched terms between model plant and real plant. Computer simulations are performed to show the applicability and the limitation of the proposed controller.

• ETRI Journal
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• 제39권3호
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• pp.345-352
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• 2017

Traffic management is a highly beneficial mechanism for satisfying quality-of-service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end-to-end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi-Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.