• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.202-210
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• 1994

In this paper, the network management system is implemented based on the analysis of reguirements and network operation and management for Flexible Automation. Network management is necessary, which controls and supervises the network resources in the communication network. By means of both analytical methods and queueing model, the delay time distributions due to the increasement of transmission data are obtained and analyzed. The operations of this network management system are certified through the test environments with the network adaptor and softwares for each layer.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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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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• v.9 no.10
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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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• 2004.08a
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.737-746
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• 2017

In 2017, amid changes in the security market such as integrated security (IS) and convergence security (CS), a variety of security paradigms in terms of operation and management have been suggested. Rather than changing existing network infrastructure and bringing about fluid, multi-dimensional changes, these solutions and technologies focus entire security capacity on a primary protection, leading to network infrastructure suffering from unexpected inherent violations and problems in a continued manner. Therefore, it is time to propose and develop a flexible network section that can protect from attacks of similar pattern and concentrated traffic attacks by applying a new concept of WB (Water-Bubble) to network infrastructure and analyzing on the basis of experiment and installation. Methodology of the WB-based highly secure flexible network section proposed in this study is expected to provide materials for studies on how to achieve network section security taking into account three major limitations and security standards: fluidity, unpredictability, and non-area scalability by contact point ratio, by changing a network area predicted to be the final target of attack into resonant network section (area) with flexible area changes.

• Proceedings of the KSR Conference
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• 2009.05a
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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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• v.26 no.4
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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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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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%.