• Journal of Communications and Networks
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• v.15 no.5
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• pp.527-537
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• 2013

Improving resource utilization has been a hot issue in heterogeneous wireless networks (HWNs). This paper proposes a radio resource management (RRM) method based on access proportion optimization. By considering two or more wireless networks in overlapping regions, users in these regions must select one of the networks to access when they engage in calls. Hence, the proportion of service arrival rate that accesses each network in the overlapping region can be treated as an optimized factor for the performance analysis of HWNs. Moreover, this study considers user mobility as an important factor that affects the performance of HWNs, and it is reflected by the handoff rate. The objective of this study is to maximize the total throughput of HWNs by choosing the most appropriate factors. The total throughput of HWNs can be derived on the basis of a Markov model, which is determined by the handoff rate analysis and distribution of service arrival rate in each network. The objective problem can actually be expressed as an optimization problem. Considering the convexity of the objective function, the optimization problem can be solved using the subgradient approach. Finally, an RRM optimization scheme for HWNs is proposed. The simulation results show that the proposed scheme can effectively enhance the throughput of HWNs, i.e., improve the radio resource utilization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• ETRI Journal
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• v.37 no.4
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• pp.717-726
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• 2015

In this paper, we study an asymmetric two-way relaying network where two source nodes intend to exchange information with the help of multiple relay nodes. A hybrid time-division broadcast relaying scheme with joint relay selection (RS) and power allocation (PA) is proposed to realize energy-efficient transmission. Our scheme is based on the asymmetric level of the two source nodes' target signal-to-noise ratio indexes to minimize the total power consumed by the relay nodes. An optimization model with joint RS and PA is studied here to guarantee hybrid relaying transmissions. Next, with the aid of our proposed intelligent optimization algorithm, which combines a genetic algorithm and a simulated annealing algorithm, the formulated optimization model can be effectively solved. Theoretical analyses and numerical results verify that our proposed hybrid relaying scheme can substantially reduce the total power consumption of relays under a traffic asymmetric scenario; meanwhile, the proposed intelligent optimization algorithm can eventually converge to a better solution.

• Structural Engineering and Mechanics
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• v.64 no.5
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• pp.567-577
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• 2017

The traditional design procedure of a prestressed concrete (PC) cable-stayed bridge is complex and time-consuming. The designers have to repeatedly modify the configuration of the large number of design parameters to obtain a feasible design scheme which maybe not an economical design. In order to efficiently achieve an optimum design for PC cable-stayed bridges, a multi-parameter optimization technique is proposed. In this optimization technique, the number of prestressing tendons in girder is firstly set as one of design variables, as well as cable forces, cable areas and cross-section sizes of the girders and the towers. The stress and displacement constraints are simultaneously utilized to ensure the safety and serviceability of the structure. The target is to obtain the minimum cost design for a PC cable-stayed bridge. Finally, this optimization technique is carried out by a developed PC cable-stayed bridge optimization program involving the interaction of the parameterized automatically modeling program, the finite element structural analysis program and the optimization algorithm. A low-pylon PC cable-stayed bridge is selected as the example to test the proposed optimization technique. The optimum result verifies the capability and efficiency of the optimization technique, and the significance to optimize the number of prestressing tendons in the girder. The optimum design scheme obtained by the application can achieve a 24.03% reduction in cost, compared with the initial design.

• Advances in Computational Design
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• v.1 no.3
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.796-805
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• 2016

In many wireless sensor network (WSN) applications, the information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in node localization is mostly geared towards multi-hop range-free localization algorithms to achieve accuracy by minimizing localization errors between the node's actual and estimated position. The existing localization algorithms are focused on improving localization accuracy without considering efficiency in terms of energy costs and algorithm convergence time. In this work, we show that our proposed localization scheme, called DV-maxHop, can achieve good accuracy and efficiency. We formulate the multi-objective optimization functions to minimize localization errors as well as the number of transmission during localization phase. We evaluate the performance of our scheme using extensive simulation on several anisotropic and isotropic topologies. Our scheme can achieve dual objective of accuracy and efficiency for various scenarios. Furthermore, the recently proposed algorithms require random uniform distribution of anchors. We also utilized our proposed scheme to compare and study some practical anchor distribution schemes.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.133-140
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor(SynRM) which minimizes the copper and iron losses. Also, this paper presents a speed estimated control scheme of SynRM using artificial neural network(ANN). There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.419-424
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• 1999

In this paper, the comparison of the first order approximation schemes such as SLP (sequential linear programming), CONLIN(convex linearization), MMA(method of moving asymptotes) and the second order approximation scheme, SQP(sequential quadratic programming) was accomplished for optimization of and nonlinear structures. It was found that MMA and SQP(sequential quadratic programming) was accomplished for optimization of and nonlinear structures. It was found that MMA and SQP are the most efficient methods for optimization. But the number of function call of SQP is much more than that of MMA. Therefore, when it is considered with the expense of computation, MMA is more efficient than SQP. In order to examine the efficiency of MMA for complex optimization problem, it was applied to the helicopter tail boom considering column buckling and local wall buckling constraints. It is concluded that MMA can be a very efficient approximation scheme from simple problems to complex problems.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.297-305
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• 2021

Large amount of data is being generated in gaming servers due to the increase in the number of users and the variety of game services being provided. In particular, load balancing schemes for gaming servers are crucial consideration. The existing literature proposes algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading. However, many proposed schemes impose heavy restrictions and assumptions, and such a limited service classification method is not enough to satisfy the wide range of service requirements. We propose a load balancing agent that combines the dynamic allocation programming method, a type of greedy algorithm, and proximal policy optimization, a reinforcement learning. Also, we compare performances of our proposed scheme and those of a scheme from previous literature, ProGreGA, by running a simulation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.47-52
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• 2007

A material mixing method to obtain an optimal topology for a structure in a thermal environment was suggested. This method is based on Evolutionary Structural Optimization(ESO). The proposed material mixing method extends the ESO method to a mixing several materials for a structure in the multicriteria optimization of thermal flux and thermal stress. To do this, the multiobjective optimization technique was implemented. The overall efficiency of material usage was measured in terms of the combination of thermal stress levels and heat flux densities by using a combination strategy with weighting factors. Also, a smoothing scheme was implemented to suppress the checkerboard pattern in the procedure of topology optimization. It is concluded that ESO method with a smoothing scheme is effectively applied to topology optimization. Optimal topologies having multiple thermal criteria for a printed circuit board(PCB) substrate were presented to illustrate validity of the suggested material mixing method. It was found that the suggested method works very well for the multicriteria topology optimization.