• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.307-324
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• 2018

Recently, recurrent neural networks have been attracting attention in solving prediction problem of sequential data through structure considering time dependency. However, as the time step of sequential data increases, the problem of the gradient vanishing is occurred. Long short-term memory models have been proposed to solve this problem, but there is a limit to storing a lot of data and preserving it for a long time. Therefore, research on memory-augmented neural network (MANN), which is a learning model using recurrent neural networks and memory elements, has been actively conducted. In this paper, we describe the structure and characteristics of MANN models that emerged as a hot topic in deep learning field and present the latest techniques and future research that utilize MANN.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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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 support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly 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 SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.4
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• pp.19-35
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• 2007

This study is for detecting the Braess Paradox by stable dynamics in general transportation networks. Stable dynamics, suggested by Nesterov and de Palma[18], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with user equilibrium model based on link latency function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on the congestion. Therefore it is expected to be an useful analysis tool for transportation planners. The phenomenon that increasing capacity of a network, for example creating new links, may decrease its performance is called Braess Paradox. It has been studied intensively under user equilibrium model with link latency function since Braess[5] demonstrated a paradoxical example. However it is an open problem to detect the Braess Paradox under stable dynamics. In this study, we suggest a method to detect the Paradox in general networks under stable dynamics. In our model, we decide whether Braess Paradox will occur in a given network. We also find Braess links or Braess crosses if a network permits the paradox. We also show an example how to apply it in a network.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.244-250
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• 2011

Gossip is a well-known protocol which was proposed to implement broadcast service with a high reliability in an arbitrarily connected network of sensor nodes. The probabilistic techniques employed in gossip have been used to address many challenges which are caused by flooding in wireless sensor networks (WSNs). However, very little work has yet been done on real-time wireless sensor networks which require not only highly reliable packets reception but also strict time constraint of each packet. Moreover, the unique energy constraining feature of sensor makes existing solutions unsuitable. Combined with unreliable links, redundant messages overhead in real-time wireless sensor networks is a new challenging issue. In this paper, we introduce a Reliable Gossip Zone, a novel fine-tailored mechanism for real-time wireless sensor networks with unreliable wireless links and low packet redundancy. The key idea is the proposed forwarding probability algorithm, which makes forwarding decisions after the realtime flooding zone is set. Evaluation shows that as an oracle broadcast service design, our mechanism achieves significantly less message overhead than traditional flooding and gossip protocols.

• Structural Engineering and Mechanics
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• v.58 no.1
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• pp.21-38
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• 2016

Many studies have been carried out to investigate the important factors in calculating the realistic entropy amount of water distribution networks, but none of them have considered both mechanical and hydraulic characteristics of the networks. Also, the entropy difference in various networks has not been calculated exactly. Therefore, this study suggested a modified entropy function to calculate the informational entropy of water distribution networks so that the order of demand nodes and entropy difference among various networks could be calculated by taking into account both mechanical and hydraulic characteristics of the network. This modification was performed through defining a coefficient in the entropy function as the amount of outflow at each node to all dissipated power in the network. Hence, a more realistic method for calculating entropy was presented by considering both mechanical and hydraulic characteristics of network while keeping simplicity. The efficiency of the suggested method was evaluated by calculating the entropy of some sample water networks using the modified function.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1423-1426
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• 2007

The goal of this research is to develop and apply the integrational model for the pan evaporation and the alfalfa reference evapotranspiration in Republic of Korea. Since the observed data of the alfalfa reference evapotranspiration using lysimeter have not been measured for a long time in Republic of Korea, PM method is used to assume and estimate the observed alfalfa reference evapotranspiration. The integrational model consists of staochastics and neural networks processes respectively. The stochastics process is applied to extend for the short-term monthly pan evaporation and alfalfa reference evapotranspiration. The extended data of the monthly pan evaporation and alfalfa reference evapotranspiration is used to evaluate for the training performance. For the neural networks process, the generalized regression neural networks model(GRNNM) is applied to evaluate for the testing performance using the observed data respectively. From this research, we evaluate the impact of the limited climatical variables on the accuracy of the integrational operation of stochastics and neural networks processes. We should, furthermore, construct the credible data of the pan evaporation and the alfalfa reference evapotranspiration, and suggest the reference data for irrigation and drainage networks system in Republic of Korea.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.378-384
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• 2014

Replication routing can greatly improve the data delivery performance by enabling multiple replicas of the same packet to be transmitted towards its destination simultaneously. It has been studied extensively recently and is now a widely accepted routing paradigm in delay tolerant networks (DTNs). However, in this field, the issue of how to maximize the utilization efficiency of limited replication quota in a resource-saving manner and therefore making replication routing to be more efficient in networks with limited resources has not received enough attention. In this paper, we propose a DTN routing protocol with back-pressure based replica distribution. Our protocol models the replica distribution problem from a resource allocation perspective and it utilizes the idea of back-pressure algorithm, which can be used for providing efficient network resource allocation for replication quota assignment among encountered nodes. Simulation results demonstrate that the proposed protocol significantly outperforms existing replication routing protocols in terms of packet delay and delivery ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• Journal of Wetlands Research
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• v.12 no.3
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• pp.79-97
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• 2010

The purpose of this research is to apply the neural networks models for the hydrologic disaggregation of the yearly pan evaporation(PE) data in Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model(MLP-NNM) and support vector machine neural networks model(SVM-NNM), respectively. And, for the evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. The application of MLP-NNM and SVM-NNM for the hydrologic disaggregation of nonlinear time series data is evaluated from results of this research. Four kinds of the statistical index for the evaluation are suggested; CC, RMSE, E, and AARE, respectively. Homogeneity test using ANOVA and Mann-Whitney U test, furthermore, is carried out for the observed and calculated monthly PE data. We can construct the credible monthly PE data from the hydrologic disaggregation of the yearly PE data, and the available data for the evaluation of irrigation and drainage networks system can be suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3967-3983
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• 2014

The performance of an partial relay selection on the decode-and-forward (DF) mode cognitive radio (CR) relay networks is studied, with some important factors, including the outage probability, the bit error ratio (BER), and the average channel capacity being analyzed. Different from the conventional relay selection schemes, the impact of spectrum sensing process as well as the spectrum utilization efficiency of primary users on the performance of DF-based CR relaying networks has been taken into consideration. In particular, the exact closed-form expressions for the figures of merit such as outage probability, BER, and average channel capacity over independent and identically distributed (i.i.d.) Rayleigh fading channels, have been derived in this paper. The validity of the proposed analysis is proven by simulation, which showed that the numerical results are consistent with the theoretical analysis in terms of the outage probability, the BER and the average channel capacity. It is also shown that the full spatial diversity order can always be obtained at the signal-to-noise ratio (SNR) range of [0dB, 15dB] in the presence of multiple potential relays.