• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.853-858
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• 2019

One of the methods which can enhance the efficiency of railroad network is construction of interconnection branch line for several route to share one railway. In Korea, this method already has been implemented or excuted as project level. This study suggests a network design model and a solution algorithm to choice most proper site to construction it and determine the priority of branch lines which can be considered in planning level, not project level. The model is a non-linear optimization program which minimize total cost-construction cost, operating cost and passengers' travel cost. The decision variables are a binary variable to explain whether construction or not and its direction and a integer variable of the frequencies of travel routes. The solution algorithm-problem solution and route choice and also the result of implementation for example network are suggested. This result can be more advanced after application in real network and calibration of parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.483-503
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• 2015

This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.413-428
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• 2013

In this study, Ant Colony Algorithm(ACO) was used for optimal model. ACO which are metaheuristic algorithm for combinatorial optimization problem are inspired by the fact that ants are able to find the shortest route between their nest and food source. For applying the model to water distribution systems, pipes, tanks(reservoirs), pump construction and pump operation cost were considered as object function and pressure at each node and reservoir level were considered as constraints. Modified model from Ostfeld and Tubaltzev(2008) was verified by applying 2-Looped, Hanoi and Ostfeld's networks. And sensitivity analysis about ant number, number of ants in a best group and pheromone decrease rate was accomplished. After the verification, it was applied to real water network from S water treatment plant. As a result of the analysis, in the Two-looped network, the best design cost was found to $419,000 and in the Hanoi network, the best design cost was calculated to $6,164,384, and in the Ostfeld's network, the best design cost was found to $3,525,096. These are almost equal or better result compared with previous researches. Last, the cost of optimal design for real network, was found for 66 billion dollar that is 8.8 % lower than before. In addition, optimal diameter for aged pipes was found in this study and the 5 of 8 aged pipes were changed the diameter. Through this result, pipe construction cost reduction was found to 11 percent lower than before. And to conclusion, The least cost design model on water distribution system was developed and verified successfully in this study and it will be very useful not only optimal pipe change plan but optimization plan for whole water distribution system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.4
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• pp.10-18
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• 2002

Network topology design is a multiobjective problem with various design components. The components such as cost, message delay and reliability are important to gain the best performance. Recently, Genetic Algorithms(GAs) have been widely used as an optimization method for real-world problems such as combinatorial optimization, network topology design, and so on. This paper proposed a method of Multi-objective GA for Design of the network topology which is to minimize connection cost and message delay time. A common difficulty in multiobjective optimization is the existence of an objective conflict. We used the prufer number and cluster string for encoding, parato elimination method and niche-formation method for the fitness sharing method, and reformation elitism for the prevention of pre-convergence. From the simulation, the proposed method shows that the better candidates of network architecture can be found.

• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.35-42
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• 2008

Mobile IP is a solution to support mobile nodes, however, it does not handle NEtwork MObility (NEMO). The NEMO Basic Support (NBS) protocol ensures session continuity for all the nodes in the mobile network. Since the protocol is based on Mobile IP, it inherits the same fundamental problem such as tunnel convergence, when supporting the multicast for NEMO. In this paper, we propose the multicast route optimization scheme for NEMO environment. We assume that the Mobile Router (MR) has a multicast function and the Nested Mobile Router Information (NeMRI) table. The NeMRI is used to record o list of the CoAs of all the MRs located below it. And it covers whether MRs desire multicast services. Any Route Optimization (RO) scheme can be employed here for pinball routing. Therefore, we achieve optimal routes for multicasting based on the given architecture. We also propose cost analytic models to evaluate the performance of our scheme. We observe significantly better multicast cost in NEMO compared with other techniques such as Bi-directional Tunneling, Remote Subscription, and Mobile Multicast based on the NBS protocol.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.39-45
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• 2008

This paper considers the multi-objective optimization of a multi-service arrayed waveguide grating-based single-hop WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. This paper presents a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Genetic algorithm based methodology provides the network architecture parameters and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with this methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.781-787
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• 2007

Network Mobility (NEMO) basic support protocol doesn't execute the process of route optimization and has not presented the particular security mechanism in other blocks except hi-directional tunnel between Mobile Router (MR) and its Home Agent (HA). Therefore in this paper we process secure route optimization courses through authenticated binding update protocol between MR and its Correspondent Node (CN) and the protocol of the competency of mandate between MR and its Mobile Network Node (MNN); its block also uses an bi-directional tunnel as the block between MR and its HA. The address of each node are generated by the way of Cryptographically Generated Address (CGA) for proving the ownership of address. Finally we analyze the robustness of proposed protocol using security requirements of MIPv6 and existing attacks and the efficiency of this protocol using the connectivity recovery and end-to-end packet transmission delay time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.121-128
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• 2003

In recent years, various artificial neural network algorithms are used in the damage assessment of civil infrastructures. So far, many researchers have used the artificial neural network as a pattern classifier for the structural damage assessment but, in this paper, the neural network is used as a structural reanalysis tool not as a pattern classifier. For the model updating using the optimization algorithm, the summation of the absolute differences in the structural vibration modes between undamaged structures and damaged ones is considered as an objective function. The stiffness of structural components are treated as unknown parameters to be determined. The structural damage detection is achieved using model updating based on the optimization techniques which determine the estimated stiffness of components minimizing the objective function. For the verification of the proposed damage identification algorithm, it is numerically applied to a simply supported bridge model.