• The Journal of Information Systems
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• v.13 no.1
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• pp.59-75
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• 2004

Fixed-mobile convergence services can be defined as the combination of previously separate fixed and mobile services, and networks and commercial practices. Examples of fixed-mobile convergence services include single voice-mail box, single number and unified messaging across fixed and mobile networks. Recently as more voice is transferred to mobile networks, convergence services between fixed and mobile become more important. In Korea convergence services are only starting to become established, and are likely to become an important part of any operator's offering. In this paper, we search the different levels of fixed-mobile convergence services and the trends and regulations for fixed-mobile convergence services in major countries. And at the same time, we also suggest the corresponding Marketing strategies by operators related to fixed-mobile convergence services.

• Journal of Communications and Networks
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• v.4 no.2
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• pp.118-127
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• 2002

This paper proposes a new scheme - parallel control scheme with feedback control (PCFC) for ABR services in ATM networks. The information from a source is split into a number of streams, for delivery over separate parallel connections with particular coding. At the receiver, the original information is reconstructed by the received packet from the parallel connections. The effects of PCFC on the network performance are due to two factors: Traffic splitting and load balancing. By combinations of analysis and simulation, this paper studies the implications of PCFC for how the ABR parameters should be scaled and the advantages of PCFC compared with other existing schemes.

• Information and Communications Magazine
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• v.32 no.10
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• pp.31-40
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• 2015

This paper investigates several existing techniques to reduce high-availability seamless redundancy (HSR) traffic. HSR is a redundancy protocol for Ethernet networks that provides duplicated frames for separate physical paths with zero recovery time. This feature makes it very useful for real-time and mission-critical applications, such as military applications and substation automation systems. However, the major drawback of HSR is that it generates too much unnecessary redundant traffic in HSR networks. This drawback degrades network performance and may cause congestion and delay. Several HSR traffic reduction techniques have been proposed to reduce the redundant traffic in HSR networks, resulting in the improvement of network performance. In this paper, we provide an overview of these HSR traffic reduction techniques in the literature. The operational principles, advantages, and disadvantages of these techniques are investigated and summarized. We also provide a traffic performance comparison of these HSR traffic reduction techniques.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1632-1644
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• 2007

Wireless Mesh Networks aim to attain large connectivity with minimum performance degradation, as network size is increase. As such, scalability is one of the main characteristics of Wireless Mesh Networks that differentiates it from other wireless networks. This characteristic creates the need for bandwidth efficiency strategies to ensure that network performance does not degrade as the size of the network increase. Several researches have been done to realize mesh networks. However, the researches conducted were mostly focused on a per TCP/IP layer basis. Also, the studies on bandwidth efficiency and bandwidth improvement are usually dealt with as separate issues. This paper aims to simultaneously study bandwidth efficiency and improvement. Aside from optimizing the bandwidth given a fixed capacity, the capacity is also increased using results of physical layer studies. In this paper, the capacity is improved by using the concept of non-overlapping channels for wireless communication. A channel allocation scheme is conceptualized to choose the transmission channel that would optimize the network performance parameters with consideration of chosen Quality of Service (QoS) parameters. Network utility maximization is used to optimize the bandwidth after channel selection. Furthermore, a routing scheme is proposed using the results of the network utilization method and the channel allocation scheme to find the optimal path that would maximize the network gain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2323-2340
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• 2012

Recent advances in cognitive radio technology have drawn immense attention to higher layer protocols above medium access control, such as transmission control protocol (TCP). Most proposals to improve the TCP performance in cognitive radio (CR) networks have assumed that either all nodes are in CR networks or the TCP sender side is in CR links. In those proposals, lower layer information such as the CR link status could be easily exploited to adjust the congestion window and improve throughput. In this paper, we consider a TCP network in which the TCP sender is located remotely over the Internet while the TCP receiver is connected by a CR link. This topology is more realistic than the earlier proposals, but the lower layer information cannot be exploited. Under this assumption, we propose an enhanced TCP protocol for CR networks called TCP for cognitive radio (TCP-CR) to improve the existing TCP by (1) detection of primary user (PU) interference by a remote sender without support from lower layers, (2) delayed congestion control (DCC) based on PU detection when the retransmission timeout (RTO) expires, and (3) exploitation of two separate scales of the congestion window adapted for PU activity. Performance evaluation demonstrated that the proposed TCP-CR achieves up to 255% improvement of the end-to-end throughput. Furthermore, we verified that the proposed TCP does not deteriorate the fairness of existing TCP flows and does not cause congestions.

• ETRI Journal
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• v.42 no.2
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• pp.175-185
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• 2020

Inter-cell interference (ICI) is a major problem in heterogeneous networks, such as two-tier femtocell (FC) networks, because it leads to poor cell-edge throughput and system capacity. Dynamic ICI coordination (ICIC) schemes, which do not require prior frequency planning, must be employed for interference avoidance in such networks. In contrast to existing dynamic ICIC schemes that focus on homogeneous network scenarios, we propose a novel semi-distributed dynamic ICIC scheme to mitigate interference in heterogeneous network scenarios. With the goal of maximizing the utility of individual users, two separate algorithms, namely the FC base station (FBS)-level algorithm and FC management system (FMS)-level algorithm, are employed to restrict resource usage by dominant interference-creating cells. The distributed functionality of the FBS-level algorithm and low computational complexity of the FMS-level algorithm are the main advantages of the proposed scheme. Simulation results demonstrate improvement in cell-edge performance with no impact on system capacity or user fairness, which confirms the effectiveness of the proposed scheme compared to static and semi-static ICIC schemes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.525-536
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• 2012

This paper proposes methods of securing Smart Grid system against various types of cyber threats by separating AMI networks from the public network, the Internet, and providing an AMI specific authentication framework. Due to the fact that thousands and millions of AMI devices to be deployed would be directly or indirectly connected to the public network without any authentication procedures for access control, currently being developed AMI architectures could be widely exposed to considerable number of penetrating attacks. Furthermore, there have not been a sufficient number of researches on authentication frameworks with basis on the specific circumstances of AMI networking that should support varied authentication protocols among security associations and AMI linking devices. This work makes a proposal of isolating smart meters from HAN devices and the Internet and integrating network/application level authentication frameworks with an EAP-based authentication architecture. These approaches are beneficial to deploy AMI with security and efficiency.

• Computers and Concrete
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• v.19 no.3
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• pp.293-303
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• 2017

Maximum deflection in a beam is a serviceability design criterion and occurs generally at or close to the mid-span. This paper presents a methodology using neural networks for rapid prediction of mid-span deflections in reinforced concrete beams subjected to service load. The closed form expressions are further obtained from the trained neural networks. The closed form expressions take into account cracking in concrete at in-span and at near the interior supports and tension stiffening effect. The expressions predict the inelastic deflections (incorporating the concrete cracking) from the elastic moments and the elastic deflections (neglecting the concrete cracking). Five separate neural networks are trained since these have been postulated to represent all beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions have been verified by comparison with the experimental results reported elsewhere and also by comparison with the finite element method (FEM). The proposed expressions, at minimal input data and minimal computation effort, yield results that are close to FEM results. The expressions can be used in every day design since the errors are found to be small.

• Computers and Concrete
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• v.18 no.6
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• pp.1113-1134
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• 2016

A methodology using neural networks has been proposed for rapid prediction of inelastic bending moments in reinforced concrete continuous beams subjected to service load. The closed form expressions obtained from the trained neural networks take into account cracking in concrete at in-span and at near the internal supports and tension stiffening effect. The expressions predict the inelastic moments (considering the concrete cracking) from the elastic moments (neglecting the concrete cracking) at supports. Three separate neural networks are trained since these have been postulated to represent all the beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions are verified for example beams of different number of spans and cross-section properties and the errors are found to be small. The proposed expressions, at minimal input data and computation effort, yield results that are close to FEM results. The expressions can be used in preliminary every day design as they enable a rapid prediction of inelastic moments and require a computational effort that is a fraction of that required for the available methods in literature.