• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.95-100
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• 2017

In this paper, we propose a zone based ad hoc network construction scheme which support ad hoc path between nodes in local IoT networks consisting of IoT devices with the constrained feature, such as low power, the limited transmission rate and low computing capacity. Recently, the various routing protocols have been studied to support ad hoc networking of local IoT environments. This is, because basis RPL protocol is deigned to be used for the connecting service with Internet through gateway, ad hoc path between nodes in local IoT networks is not supported in basis RPL protocol. Thus, in this paper, the proposed routing scheme provides both ad hoc path and Infra path through gateway, supporting basis RPL protocol simultaneously. Through simulation, we show that the proposed routing scheme with zone based path selection scheme improves the performance of the success rate of end-to-end data transmission and the end-to-end delay, compared to basis RPL protocol.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.543-552
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• 1994

This paper presents a decentralized quadratic regulation architecture with feedforward neural networks for the control problem of complex systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle the nonlinear interaction variables between subsystems. The decentralized regulating architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using generalized Backprogation Through Time(BTT) algorithm. The proposed neural network based decentralized regulating scheme is applied in the power System Stabilization(PSS) control problem for an imterconnected power system, and compared with that by a conventional centralized LQ regulator for the power system.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.423-444
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• 2013

Tourism in Thoi Son Islet has been the advanced model for rural tourism in the Mekong Delta region since the 1990s. The continuously rising number of tourists, however, has also created problems that affect sustainable rural development. To understand these problems, this research analyzed how rural tourism has been operated through the methodology of a stakeholder network. After investigating the network among key stakeholders (Ho Chi Minh travel agencies (HCMTAs), local travel agencies (LTAs), and local residents, the result showed that in the current model, HCMTAs and LTAs have played the role of connectors, working as hubs to shift tourists (demand) to match local residents (supply), with the networking being dominated by signed contracts (formal networks). The networks between LTAs and local residents are both formal and informal. Inter- and intra-networks among local residents are dominated by informal networks of established working relationships based on networks of family, friends, and neighbors. Moreover, this research has found that there is no cooperating network among LTAs. Among owners of tourist sites was not also found cooperating network. The primary motivating factor for these stakeholders is price competition; this has led to a disproportionately small share of revenue for local stakeholders, with most tourism revenue going to HCMTAs. Additionally, because of the high competition among local stakeholders, this results in local stakeholders having little or no negotiating power when conducting business with HCMTAs. Meanwhile the Tien Giang Tourism Association is inefficient in fostering cooperation among local stakeholders to increase their negotiating power.

• Proceedings of the Korean Magnestics Society Conference
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• 2008.12a
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• pp.314.1-314.1
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• 2008

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1363-1366
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• 2002

The studies of multi-hop wireless networks are active as the new communication media, which does not require any fixed communication infrastructure. One of application of the networks is local information distribution service, which is useful far daily activities in certain geographically restricted region or community within a radius of several kilometers. In this paper, MID-Net, which is the network enabling such distribution service, and effective information distribution algorithm MCMS are proposed. The behavior of the MID-Net is characterized by the waiting time function, three types of the functions are proposed in this paper.

• Nuclear Engineering and Technology
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• v.36 no.5
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• pp.420-429
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• 2004

The local power density should be estimated accurately to prevent fuel rod melting. The local power density at the hottest part of a hot fuel rod, which is described by the power peaking factor, is more important information than the local power density at any other position in a reactor core. Therefore, in this work, the power peaking factor, which is defined as the highest local power density to the average power density in a reactor core, is estimated by fuzzy neural networks using numerous measured signals of the reactor coolant system. The fuzzy neural networks are trained using a training data set and are verified with another test data set. They are then applied to the first fuel cycle of Yonggwang nuclear power plant unit 3. The estimation accuracy of the power peaking factor is 0.45% based on the relative $2_{\sigma}$ error by using the fuzzy neural networks without the in-core neutron flux sensors signals input. A value of 0.23% is obtained with the in-core neutron flux sensors signals, which is sufficiently accurate for use in local power density monitoring.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.131-138
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• 1998

In this paper, we investigate the direction of LAN evolution. We focus rather on the philosophical changes than on the advances of technologies. In terms of topology, channel structure, and medium access control, we categorize major local area networks widely used in the past. We then investigate new directions in the concept and advances in the technology of local area networks. The idea of collapsed backbone is shown to be the most fundamental factor influencing the evolution of local area networks. Asynchronous Transfer Mode that is radically different form the existing LAN technology is now actively applied to LAN. In order to compete with ATM, conventional LANs should evolve to support quality of service for multimedia application

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5B
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• pp.295-303
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• 2007

An algorithm is proposed to seek a local optimal solution of the network utility maximization problem in a wireless mesh network, where the architecture being considered is an infrastructure/backbone wireless mesh network. The objective is to achieve proportional fairness amongst the end-to-end flows in wireless mesh networks. In order to establish the communication constraints of the flow rates in the network utility maximization problem, we have presented necessary and sufficient conditions for the achievability of the flow rates. Since wireless mesh networks are generally considered as a type of ad hoc networks, similarly as in wireless multi-hop network, the network utility maximization problem in wireless mesh network is a nonlinear nonconvex programming problem. Besides, the gateway/bridge functionalities in mesh routers enable the integration of wireless mesh networks with various existing wireless networks. Thus, the rate optimization problem in wireless mesh networks is more complex than in wireless multi-hop networks.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.574-581
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.