• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1419-1424
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• 2013

Passive optical networks (PONs) are the emerging solution for access networks since PONs provide high bandwidth and the low operation cost. In recent, the new solution in access networks is TWDM PON which is a mixture of WDM (wavelength-division multiplexing) and TDM (time-division multiplexing). This paper proposes a new MAC (media access control) protocol for TWDM PON. In addition, this paper proposes and evaluates a new dynamic bandwidth allocation method for TWDM PON.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.499-508
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• 2005

In this paper, we address the problem of efficient routing and wavelength assignment (RWA) in multi-fiber wavelength division multiplexing (WDM) networks without wavelength translation, under dynamic traffic. We couple Dijkstra's shortest path algorithm with a suitable weight function which chooses optical paths based both on wavelength availability and multi-fiber segments. We compare our approach with other RWA schemes both for regular and irregular WDM multi-fiber network topologies in terms of blocking probability and overall link utilization.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.7-13
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• 2017

In this paper, we propose a novel dynamic subchannel grouping (DSG) algorithm to maximize the system capacity considering intended proper outage probability for the downlink of enterprise small-cell networks (ESNs). In the proposed DSG scheme, a local gateway (LGW) which is installed in a building dynamically divides the frequency bandwidth into different numbers of subchannel groups (SGs) based on the numbers of small-cell access points (SAPs) and small-cell user equipments (SUEs) per floor. Then, the LGW assigns the SGs to SAPs and the SAPs allocate them to their serving SUEs. Through simulation results, we show that the proposed DSG scheme is appropriate for the ESNs compared to the conventional small-cell networks in which all SAPs use the number of fixed SGs in terms of the system capacity and outage probability.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.38-44
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• 2013

This paper proposes a new dynamic spectrum access (DSA) algorithm for cognitive radio networks. Once initialized, this algorithm works without the need of coordination overhead and hence can be used when no control channel is available. Secondary user (SU) lists and predetermined access control are used in this algorithm. We analyze the probability of no SU collision with primary user and the throughput of our proposed algorithm. Extensive simulations show that our algorithm outperforms the existing DSA algorithm in terms of both the aggregate throughput and the traffic distribution fairness. Furthermore, the validity of our analysis is confirmed by simulation results.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.551-558
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• 2016

In multifiber all-optical networks, optical amplifiers are used for amplifying multiple optical signals with different wavelengths in fibers. An optical amplifier operates when any of lightpaths passes through it. Therefore, it should simultaneously amplify as many lightpaths as possible for efficiently utilizing its power. This paper proposes a dynamic lightpath establishment scheme considering the use efficiency of the optical amplifiers and the depletion of the wavelength resources in multifiber all-optical networks. The proposed scheme provides a routing and wavelength assignment strategy that reduces both the power consumption of the optical amplifiers and the blocking probability of the lightpath establishment. Through simulation experiments, we demonstrate the effectiveness of the proposed scheme.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.197-200
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• 2011

Many routing protocols have been proposed for low-power and ad-hoc networks where energy awareness and reliability are essential design issues. This paper proposes a dynamic routing protocol for low-power and ad-hoc networks. A dynamic path cost function is defined considering the constraints and characteristics of low-power and ad-hoc networks. The cost function can be applied flexibly depending on the characteristics of the networks. The performance of the proposed method is evaluated using a QualNet network simulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.421-433
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• 2015

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.816-824
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• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.