• Smart Media Journal
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• v.11 no.5
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1674-1675
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• 2007

This paper presents a stability analysis of network systems with time delay. Time delay problem frequently occurs in network systems. Since it makes network systems unstable and unpredictable, an optimal controller is necessary to network systems. We prove the asymptotical stability of time delayed network systems using LMI optimization method and appropriate Lyapunov-Krasovskii functionals. Simulations show the effectiveness of the method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4016-4027
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• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.393-401
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• 2013

This paper deals with the operation strategy of the Cheju AC network included MIHVDC system (Multi-Infeed HVDC system). In case that where several HVDC systems are located in the vicinity of each other, there are interactions between the different HVDC systems in such network configurations. The interactions which could be generated in multi-infeed HVDC are voltage stability, power stability and inertia stability, to analyze such systems in a systematic way to ensure that there are no risks of adverse interactions is very important. The developed method until now to analyze MIHVDC interaction is extended from MAP(Maximum Available Power) method for analyzing the power stability of the single-infeed HVDC system, this method is to solve the eigenstructure using the identified factors influencing the interactions. Finally, the algorithms which are introduced in this paper, to determine the operation strategy are applied to Cheju island network which is supplied by two HVDCs.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1004-1013
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• 1999

This paper considers the transient stability problem of power system. The power system model is given as interconnected system consisting of many machines which are described by swing equations. We design a transient stability controller using passivity and neural network. The structure of the neural network controller is derived using a filtered error/passivity approach. In general, a neural network cannot be guaranteed to be passive, but the weight tuning algorithm given here do guarantee desirable passivity properties of the neural network and hence of the closed-loop error system. Moreover proposed controller shows good robustness by simulation for uncertainties in parameters, which can not be shown in the speed gradient method proposed by Fradkov[3,7].

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.43-50
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• 2002

In the recent years neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neural network control for robot manipulators based on the radial basis function netwo.k (RBFN). The RBFN is a branch of the neural networks and is mathematically tractable. So we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. The saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.2
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• pp.16-21
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• 2006

In this study, the analytic realization of chatter mechanism using radial basis neural network(RBNN) was introduced and compared with the conventional stability analysis. In this regard, the FFT and time series spectrum analysis was used as a criterion for the existence of chatter in end-milling force. The desired coded outputs of chatter was trained and finally converged to desired outputs. The output of the RBNN match well with the conventional desired stability lobe. Using this trained data, the stability boundary of the radial basis neural network was acquired using the contour plotting. As a result, the proposed stability lobe boundary using RBNN consists well with the conventional analytical boundary that is calculated in characteristic equation of transfer function in chatter dynamics. In this RBNN analysis, two input and three output parameters were used in this paper.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2383-2385
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• 2004

Network uncertainties can vary the stability property of networked control system. Therefore, the performance and the stability variation of networked control system due to network uncertainties must be considered first in designing networked control system. In this paper, we present a new stability analysis method of networked control system with data loss and time delay. The proposed method can determine maximum allowable time delay and allowable transmission rate that preserves stability performance of networked control system. The results of the simulation validate effectiveness of our stability analysis methods.