• 한국통신학회논문지
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• 제19권2호
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• pp.202-210
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• 1994

본 논문에서는 Flexible Automation을 위한 네트워크 운영과 관리체제를 분석하고 이에따른 요구사항을 고려하여 네트워크 관리 시스템을 설계하였다. 네트워크 관리기능은 자원을 제어하고 관리하는 필수적인 기능으로, 관리 서비스를 제공할 수 있는 방법과 관리구조를 제안하고 해석적 방법과 큐잉모델을 이용하여 전송 데이터의 증가에 따른 지연시간 분포를 구하고 성능을 분석한다. 성능관리를 위한 성능을 측정하고 그 결과를 분석하여 설계된 관리시스템의 운영에 대한 타당성을 확인하였다.

• 제어로봇시스템학회논문지
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• 제4권6호
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• pp.738-746
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• 1998

This paper presents a neural network-based adaptive controller for a single flexible-link. The control for feedback-error loaming of neural network is designed by using the re-definition approach. The neural network controllers are implemented on an single flexible-link experimental test-bed. The tip response is significantly improved and the vibrations of the flexible modes are damped very fast. Experimental and simulation results are presented of the proposed tip position tracking controllers over the conventional PD-type, passive controllers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3789-3809
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• 2015

In this paper, we deal with the problem of M2M gateways' network selection for different types of M2M traffic in heterogeneous wireless networks. Based on the difference in traffic's quality of service (QoS) requirements, the M2M traffic produced by various applications is mainly classified as two categories: flexible traffic and rigid traffic. Then, game theory is adopted to solve the problem of network-channel selection with the coexistence of flexible and rigid traffic, named as flexible network access (FNA). We prove the formulated discrete game is a potential game. The existence and feasibility of the Nash equilibrium (NE) of the proposed game are also analyzed. Then, an iterative algorithm based on optimal reaction criterion and a distributed algorithm with limited feedback based on learning automata are presented to obtain the NE of the proposed game. In simulations, the proposed iterative algorithm can achieve a near optimal sum utility of whole network with low complexity compared to the exhaustive search. In addition, the simulation results show that our proposed algorithms outperform existing methods in terms of sum utility and load balance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• 한국전자통신학회논문지
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• 제12권5호
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• pp.737-746
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• 2017

2017년 통합보안(IS, Integrated Security), 융합 보안(CS, Convergence Security) 등과 같은 새로운 보안시장의 변화 속에서 운영과 관리 차원의 다양한 보안 패러다임이 제시되고 있다. 이러한 솔루션과 기술은 현존하는 네트워크 인프라의 변경과 유동적인 다차원적인 변화를 이끌어 내기보다는 보안성을 높이는 1차원적인 방어에 모든 보안 역량이 집중되어짐으로써 예상치 못한 침해와 장애를 지속적으로 내제하고 있는 네트워크 인프라를 유지해 오고 있다. 따라서 WB(Water-Bubble)이라는 새로운 아이디어를 네트워크 인프라에 접목하고 실험과 구현 기반의 분석을 진행함으로써 유사패턴 공격과 집중화 트래픽 공격을 방어할 수 있는 탄력적 네트워크 구간을 제안하고 개발할 수 있는 기회이기도 한다. 또한 본 논문에서 제안하는 WB 기반의 강한 보안성을 갖는 탄력적 네트워크 구간에 관한 연구기법은 공격의 최종 목적지로 예상되는 네트워크 영역을 울림형태의 탄력적 영역변화를 갖는 네트워크 구간(구역)으로 유동성과 비예측성, 상호 접점비율에 따른 비 영역 확장성 등의 3대 주요 제한 및 보안 기준을 바탕으로 네트워크 구간 보안성 확보를 위한 연구 자료를 제공하고자 한다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.141-147
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• 2009

This paper reports on a study to increase the flexibility of previous TMDS(Train Monitoring and Diagnosis System) network communication in case of both multiple units and variable train sets. The previous TMDS network configuration has been applied using various field-BUS by the TMDS manufacturers using their own intrinsic method. But recently, there has been a demand for flexible train formations such as multiple units and variable train set formations, hence the TMDS had to be adapted to offer flexible network communication technology capability. Therefore, Hyundai-Rotem needed its intrinsic method of network configuration, and develop a network configuration method applicable to both multiple units and variable train set formations. The TMDS was integrated into the Irish Rail new Diesel Multiple Units from an early stage of the project and subsequently fully tested on a finished train.

• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 한국공작기계학회논문집
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• 제17권5호
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• pp.123-130
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• 2008

In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.

• 한국생산제조학회지
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• 제18권6호
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• pp.681-689
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• 2009

Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.

• 한국생산제조학회지
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• 제19권6호
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• pp.760-767
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• 2010

Even though a flexible disk grinding process was often applied to enhance the product quality, it produced non-flat zone in the beginning and the exit (end) area. Since latter area is susceptible to poor product quality with burn mark, careful analysis is required to cope with such degradation. The flexible disk grinding exit stage was analyzed for workpiece length, wheel speed, depth of cut and feed. The exit stage qualities defined as exit stage ratio and exit stage angle or slope was characterized. A neural network application results reveled that exit stage characteristics was predicted more accurately without workpiece dimension with minimum error of 1.3%.