• Journal of the Korean Society of Industry Convergence
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• v.16 no.3
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• pp.61-68
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• 2013

Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.133-138
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• 2011

This paper presents an information theoretic outage analysis for physical layer of a cooperative multihop wireless network. Our analysis shows that cooperation by selecting a proper relay at each hop increases the coverage or data rate of the network. In our analysis we consider both symmetric and asymmetric network model. We also investigate the availability of cooperative relay at each hop and show that end-to-end performance of the network depends on the relay selection procedure at each hop. We also verify our analytical results with simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.201-204
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• 1998

This paper presents a new construction method of candidate controllers using Multi-modal Neural Network(MNN). To improve a control performance of multiple controller, we construct, candidate controllers which consist of MNN. MNN can learn more complicated function than multilayer neural network. MNN consists of preprocessing module and neural network module. The preprocessing module transforms input signals into spectra which are used as input of the following neural network module. We apply the proposed method to multiple control system which controls the cart-pole balancing system and show the effectiveness of the proposed method.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.829-836
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• 2002

The paper presents a multi-objective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer's desire of providing excess power at nodes and designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and applicable to water distribution systems is presented. The present model is applied to two example problems, which were widely reported. Pipe failure analysis carried out on some of the solutions obtained revealed that the network resilience based approach gave better results in terms of network reliability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1046-1048
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• 2009

Cooperative transmission is an effective technique to combat multi-path fading and reduce transmitted power. Relay selection and power allocation are important technical issues to determine the performance of cooperative transmission. In this paper, we proposed a new multi-relay selection and power allocation algorithm to increase network lifetime. The proposed relay selection scheme minimizes the transmitted power and increase the network lifetime by considering residual power as well as channel conditions. Simulation results show that proposed algorithm obtains much longer network lifetime than the conventional algorithm.

• ETRI Journal
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• v.42 no.6
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• pp.846-858
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• 2020

The non-orthogonal multiple access (NOMA) technique offers throughput improvement to meet the demands of the future generation of wireless communication networks. The objective of this work is to further improve the throughput by including an underlay cognitive radio network with an existing multi-carrier NOMA network, using cooperative communication. The throughput is maximized by optimal resource allocation, namely, power allocation, subcarrier assignment, relay selection, user pairing, and subcarrier pairing. Optimal power allocation to the primary and secondary users is accomplished in a way that target rate constraints of the primary users are not affected. The throughput maximization is a combinatorial optimization problem, and the computational complexity increases as the number of users and/or subcarriers in the network increases. To this end, to reduce the computational complexity, a dynamic network resource allocation algorithm is proposed for combinatorial optimization. The simulation results show that the proposed network improves the throughput.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.279-293
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• 2023

This study investigates network effects to measure how strongly the early adopters affect the brand choice of the potential consumer. By using the Gumbel-Hougaard (GH) copula, this study checks the magnitude of network effects varied from country to country. To consider consumer heterogeneity and network effects in the growth market, this study proposes the multi-brand Gamma/Shifted-Gompertz (m-G/SG) model based on the GH copula. Out of eighteen Western European cellular phone market data and South Korea smartphone data sets, the m-G/SG model provides an improvement in the estimation accuracy over the Libai, Muller, and Peres model. The results show that network effects enhance (i) the polarization of brand choice probabilities as time elapses; (ii) the dominance of the more preferred and the earlier entered brand; and (iii) the deceleration of category-level diffusion. Potential followers can analyze their relationship with earlier entrants through the m-G/SG model and also establish an optimal market entry strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.975-980
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• 2019

In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.1-7
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• 2009

In this paper, we obtain the optimal number of interfaces/channels in wireless mesh networks by simulation. The simulation study is done in static multi-channel multi-interface environment. When many nodes use a single interface and channel and contend for the channel, collisions of RTS/CTS results in network performance degradation. To avoid such degradation and reduce interferences between the adjacent nodes, use of multi-interface/channel is considered. 802.11a and 802.11b systems offer 12 and 3 orthogonal channels respectively and multi-interface/channel scheme could be applied. But rare research about the optimal number of interfaces/channels has been studied. Therefore, in this paper, simulation study for the optimal number of interfaces/channels in wireless mesh network is made.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.301-314
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• 2013

In a parallel processing system, Multi-stage Interconnection Networks (MINs) play a vital role in making the network reliable and cost effective. The MIN is an important piece of architecture for a multiprocessor system, and it has a good impact in the field of communication. Optical Multi-stage Interconnection Networks (OMINs) are the advanced version of MINs. The main problem with OMINs is crosstalk. This paper, presents the (1) Destination Based Modified Omega Network (DBMON) and the (2) Destination Based Scheduling Algorithm (DBSA). DBSA does the scheduling for a source and their corresponding destination address for messages transmission and these scheduled addresses are passed through DBMON. Furthermore, the performance of DBMON is compared with the Crosstalk-Free Modified Omega Network (CFMON). CFMON also minimizes the crosstalk in a minimum number of passes. Results show that DBMON is better than CFMON in terms of the average number of passes and execution time. DBSA can transmit all the messages in only two passes from any source to any destination, through DBMON and without crosstalk. This network is the modified form of the original omega network. Crosstalk minimization is the main objective of the proposed algorithm and proposed network.