• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.75-82
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• 1991

We proposed an optimal parallel implementation of an optimization neural network with linear increase of speedup by using multicomputer system and presented performance analysis model of the system. We extracted the temporal-and the spatial-parallelism from the optimization neural network and constructed a parallel pipeline processing model using the parallelism in order to achieve the maximum speedup and efficiency on the CSP architecture. The results of the experiments for the TSP using the Transputer system, show that the proposed system gives linear increase of speedup proportional to the size of the optimization neural network for more than 140 neurons, and we can have more than 98% of effeciency upto 16-node system.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.46-54
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• 2023

To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.

• Korean Management Science Review
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• v.26 no.1
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• pp.161-169
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• 2009

A traffic analysis on the Internet has an advantage for obtaining the characteristics of transferred packets. There were many studies to understand the characteristics of the Internet traffic with mathematical statistical approach. The approach of this study is different from previous studies. We first introduced a virtual network concept to present the Internet as a simplified mathematical model. It also represents each traffic flowing on the Internet as a parallel Gaussian channel on the virtual network. We suggest the optimal capacity of each parallel Gaussian channel using some related studies on the Gaussian channel model.

• 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.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.67-72
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• 2000

A dynamic draft model is necessary to analyze mechanics of tillage and to design optimal tillage tools. In order to deal with draft dynamics, a neural network paradigm was applied to develop dynamic draft models. For the development of the models, three kinds of tillage tools were used to measure drafts in the soil bin and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one-step-ahead prediction. A procedure for network prediction model identification was established. The results show promising modeling of the dynamic drafts with developed neural network.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.772-772
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• 1994

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.51-51
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• 1989

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.352-361
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• 2004

In this paper, we study how to achieve the maximum capacity under delay constraints for large mobile wireless networks. We develop a systematic methodology for studying this problem in the asymptotic region when the number of nodes n in the network is large. We first identify a number of key parameters for a large class of scheduling schemes, and investigate the inherent tradeoffs among the capacity, the delay, and these scheduling parameters. Based on these inherent tradeoffs, we are able to compute the upper bound on the maximum per-node capacity of a large mobile wireless network under given delay constraints. Further, in the process of proving the upper bound, we are able to identify the optimal values of the key scheduling parameters. Knowing these optimal values, we can then develop scheduling schemes that achieve the upper bound up to some logarithmic factor, which suggests that our upper bound is fairly tight. We have applied this methodology to both the i.i.d. mobility model and the random way-point mobility model. In both cases, our methodology allows us to develop new scheduling schemes that can achieve larger capacity than previous proposals under the same delay constraints. In particular, for the i.i.d. mobility model, our scheme can achieve (n-1/3/log3/2 n) per-node capacity with constant delay. This demonstrates that, under the i.i.d. mobility model, mobility increases the capacity even with constant delays. Our methodology can also be extended to incorporate additional scheduling constraints.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.3
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• pp.19-26
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• 1997

This paper is concerned with the design of neural-network based autopilot control system. The back-propagation neural network introduced in the previous paper by authors is applied to the autopilot control system. As a result, two neural-network controllers are developed, which are the model reference adaptive neural controller and the instantaneous optimal neural controller. The model reference adaptive neural controller is the control technique that the heading angle and angular velocity are controlled by the rudder angle to follow the output of the reference model. The instantaneous optimal neural controller optimizes the transition from one state to the next state. These control techniques are applied to a simple ship maneuvering model and their effectiveness is proved by numerical examples.