• 제어로봇시스템학회논문지
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• 제6권8호
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• pp.666-671
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• 2000

With the current advances in optical WDM (Wavelength Division Multiplexing) networking technologies and the increasing demand for network bandwidth the Next Generation Internet is expected to be a network that runs IP(Internet Protocol) directly over WDM-based optical networks. The network control architecture for the IP over WDM networks is different from that of traditional Internet since the underlying WDM devices have more constraints than electronic IP routers such as the lack of optical buffers and wavelength continuity property etc. In this paper we introduce several architectural models for implementing IP over WDM networks and propose an integrated network control framework for the IP over WDM networks. This framework leverages the traffic engineering control architecture for the MPLS (Multi-Protocol Label Switching) networks and is mainly developed for the IP over packet-switched WDM networks. We also report several preliminary simulation results of contention resolution schemes in the packet-switched WDM networks.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.103-110
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• 2021

본 논문에서는 5G 무선 네트워크 D2D 시스템에서 수행될 수 있는 두 가지 액세스 제어 메커니즘들을 비교해 보고 효과적인 액세스 제어 기법을 제안한다. 현재 5G D2D 시스템에서는 액세스 제어 기법이 표준 규격에 제정 되어 있지 않고 있으나, 두 가지 액세스 제어 기법들이 가능하다. 하나의 방식으로는 UE-to-Network Relay 기반의 액세스 제어 기법이고, 다른 하나의 방식으로는 Remote UE 기반의 액세스 제어 기법이다. 전자의 경우는 UE-to-Network Relay가 액세스 제어 검사를 수행하는 것이며, 후자의 경우는 Remote UE가 액세스 제어 검사를 직접 수행하는 방식이다. 실험 평가 결과, 본 논문에서는 5G 무선 네트워크 D2D 시스템에서 효율적인 액세스 제어 방안으로써 Remote UE 기반의 액세스 제어 기법을 최종 제안한다. Remote UE 기반의 액세스 제어 기법은 UE-to-Network Relay 기반의 액세스 제어 기법이 비하여, 신호 오버헤드를 최소화하고, 서로 액세스 제어 기능들이 다른 경우 보다 효율적인 액세스 제어 검사를 수행할 수 있다.

• 한국항해학회지
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• 제23권3호
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권3호
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• pp.911-923
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• 2014

Ad hoc cognitive radio wireless sensor networks allow secondary wireless sensor nodes to recognize spectrum opportunities and transmit data. Most existing protocols proposed for ad hoc cognitive radio wireless sensor networks require a dedicated common control channel. Allocating one channel just for control packet exchange is a waste of resources for channel-constrained networks. There are very few protocols that do not rely on a common control channel and that exchange channel-negotiation control packets during a pre-allocated time on the data channels. This, however, can require a substantial amount of time to access the channel when an incumbent is present on the channel, where the nodes are intended to negotiate for the data channel. This study examined channel access delay on cognitive radio wireless sensor networks that have no dedicated common control channel.

• 제어로봇시스템학회논문지
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• 제6권4호
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• pp.313-319
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• 2000

As the interest of flexible manufacturing systems and computer integrated manufacturing systems increase, the distribution of centralized control systems using industrial control networks is getting more attention. In this paper, we investigate the rate-based traffic control of industrial control networks to improve the performance regarding the throughput, fairness, and error rates. Especially, we consider the protocol of Lon-$Works^{(TM)}$ which consists of all OSI 7-layers and supports various communication media at a low cost. Basically, the proposed rate-based traffic control system is closed loop by utilizing the feedback channel errors, which shows improved performance when compared with other industrial control networks commonly operated in open loop. To this end, an additional network node called monitoring node is introduced to check the channel status without increasing the channel load. The Proposed control loop is in effect whenever the feedback channel error becomes greater than an admittable value. We demonstrate the improved performance of the controlled network system in view of throughput and fairness measures by implementing the lab-scale network system based on LonWorks and through the experimentation upon it.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1017-1021
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• 1991

This paper is to explore the potential use of the modeling capacity of neural networks for control applications. The tasks are carried out by two neural networks which act as a plant identifier and a system controller, respectively. Using information stored in the identification network control action has been developed. Without supervising control signals are generated by a gradient type iterative algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• 정보보호학회지
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• 제26권6호
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• pp.42-50
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• 2016

Critical infrastructure (CI) such as the electrical grid, transportation systems and water resource systems are controlled by Industrial Control and SCADA (Supervisory Control and Data Acquisition) networks. During the last few years, cyber attackers have increasingly targeted such CI systems. This is of great concern because successful attacks have wide ranging impact and can cause widespread destruction and loss of life. As a result, there is a critical requirement to develop enhanced algorithms and tools to detect cyber threats for SCADA networks. Such tools have key differences with the tools utilized to detect cyber threats in regular IT networks. This paper discusses key factors which differentiate network security for SCADA networks versus regular IT networks. The paper also presents various approaches used for SCADA security and some of the advancements in the area.