• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• ETRI Journal
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• 제34권1호
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• pp.102-105
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• 2012

To serve the growing demand of the bidirectional information exchange, we propose a single relay selection (RS) scheme for physical-layer network coding (PNC) in a bidirectional cooperative network consisting of two sources and multiple relays. This RS scheme selects a single best relay by maximizing the bottleneck of the capacity region of both information flows in the bidirectional network. We show that the proposed RS rule minimizes the outage probability and that it can be used as a performance benchmark for any RS rules with PNC. We derive a closed-form exact expression of the outage probability for the proposed RS rule and show that it achieves full diversity gain. Finally, numerical results demonstrate the validity of our analysis.

• 전자통신동향분석
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• 제36권2호
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• pp.83-92
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• 2021

5G networks are rapidly expanding. Simultaneously, the need for a testbed-not a commercial network-is increasing to verify aspects such as 5G network security vulnerabilities. Open-source projects in 4G networks already exist and are implemented similarly in commercial networks. Due to the compatibilty between 5G and 4G networks, 5G networks are being developed and implemented as open-source projects on the basis of 4G networks. In this study, we review the development trends of 5G open-source projects and simulators that can be used for 5G research.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.105-116
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• 2019

Recently, there has been an increase in research on wireless sensor networks (WSNs) because they are easy to deploy in applications such as internet-of-things (IoT) and body area networks. However, WSNs have constraints in terms of power, quality-of-service (QoS), computation, and others. To overcome the power constraint issues, wireless energy harvesting has been introduced into WSNs, the application of which has been the focus of many studies. Additionally, to improve system performance in terms of achievable rate, cooperative networks are also being explored in WSNs. We present a review on current research in the area of energy harvesting in WSNs, specifically on the application of simultaneous wireless information and power transfer (SWIPT) in a cooperative sensor network. In addition, we discuss possible future extensions of SWIPT and cooperative networks in WSNs.

• 인터넷정보학회논문지
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• 제10권5호
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• pp.1-12
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• 2009

BGP 커뮤니티 기반의 라우팅 정책은 특정 네트워크에 대한 경로를 사용자 정의 라우팅 정책에 따라 선택할 수 있는 특성을 갖는다. 다양한 속성의 단위 네트워크가 상호 연결된 글로벌연구망 환경의 전체적인 성능향상을 위해 최근 커뮤니티 기반의 라우팅 정책에 대한 관심이 증대하고 있다. 본 논문은 기존 글로벌연구망에서 적용되고 있는 커뮤니티 라우팅을 전체 네트워크 성능향상의 관점에서 분석하고, 새로운 글로벌연구망에서 라우팅 성능에 관련된 문제점을 도출한다. 이를 바탕으로 새로운 글로벌연구망의 라우팅 경로상의 잘못된 경유 트래픽 개선, 비대칭 라우팅 등을 해결하는 네트워크 상호연동 구조와 최적의 커뮤니티 라우팅 정책 모델을 제시한다. 제시된 결과는 향후 개발될 국가연구망과 상업망의 성능향상에 크게 기여할 수 있을 것으로 기대된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 정보기술과데이타베이스저널
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• 제2권2호
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• Journal of Communications and Networks
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• 제12권5호
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• pp.510-517
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• 2010

In this paper, an adaptive MAC protocol (variable load adaptive (VLA)-MAC) is proposed for wireless sensor networks. This protocol can achieve high energy efficiency and provide low latency under variable-traffic-load conditions. In the case of VLA-MAC, traffic load is measured online and used for adaptive adjustment. Sensor nodes transmit packets in bursts under high load conditions to alleviate packet accumulation and reduce latency. This also removes unnecessary listen action and decreases energy consumption in low load conditions. Simulation results show that the energy efficiency, latency, and throughput achieved by VLA-MAC are higher than those achieved by some traditional approaches.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2452-2454
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• 2001

KEPRI, the research institute for KEPCO, has started developing a DAS using wireless communication networks since 1999. The wireless networks adopted Radio Link Protocol (RLP) of Personal Communication Service (PCS) as communication protocol. It is the first time that PCS is applied to data networks for DAS. The communication protocol, RLP, makes the DAS networks simple and economically affordable when they are installed at widely dispersed small cities. But, RLP has problem when it send unsolicited message. This paper describes the implementing method of a wireless network using RLP and TCP/IP Network to cope with unsolicited message problem.

• Bulletin of the Korean Chemical Society
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• 제29권1호
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• pp.252-254
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• 2008