• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4595-4610
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• 2020

In the practical communication environment, the accurate channel state information (CSI) is difficult to obtain, which will cause the mismatch of resource and degrade the system performance. In this paper, to account for the channel uncertainties, a robust power allocation scheme for a downlink Non-orthogonal multiple access (NOMA) heterogeneous network (HetNet) is designed to maximize energy efficiency (EE), which can ensure the quality of service (QoS) of users. We conduct the robust optimization model based on worse-case method, in which the channel gains belong to certain ellipsoid sets. To solve the non-convex non-liner optimization, we transform the optimization problem via Dinkelbach method and sequential convex programming, and the power allocation of small cell users (SCUs) is achieved by Lagrange dual approach. Finally, we analysis the convergence performance of proposed scheme. The simulation results demonstrate that the proposed algorithm can improve total EE of SCUs, and has a fast convergence performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1117-1124
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• 2010

PMIPv6 is a network-based mobility support scheme, proposed and standardized by NetLMM WG of IETF. It is proposed to solve problems of conventional mobility schemes, and to improve inefficiency of those. The standard document describes network components and detailed procedures to provide mobility to MN. But it describes only a handover procedure between MAGs, not between LMAs. In order to support seamless connectivity of MN efficiently, a handover procedure between LMAs is necessary. The proposed scheme in this paper utilizes a route optimization procedure to prevent inefficiency of inter-LMA tunneling scheme. At the same time, the proposed scheme utilizes a pre-authentication scheme to reduce handover latency. According to the result of performance evaluations, the proposed scheme greatly reduces handover latency, compared to conventional mobility support schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4268-4289
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• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.57-67
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• 1999

Optimization scheme is presented for the design of precast prestressed double-tee beams used as slabs in the parking or market structures. The objective considered is defined by a function that minimizes the hight of the double-tee beam, including the prefabricated element and the concrete topping poured in a second phase. The Sequential Quadratic Programming method is adopted to solve the problem. As an example 12 double-tee beams are designed with the design loads of the current design code of our country. The results from optimization process show that at least 29cm less in overall height than that designed by PCI design handbook. The section determined from the optimization process was refined for practical considerations. A MathCad 7.0 Pro Spreadsheet was prepared to verify all ACI requirements for flexure, shear and deflections. Flexural tests are performed on four full-scale 12.5m prototype models and show that all the specimens are fully comply the flexural strength requirements as specified by ACI 318-95. The present optimization scheme can be used for wider application of the design of precast prestressed double-tee beams with different materials and configurations particularly for in a large scale or for important designs.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.759-777
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• 2013

In this paper, a topology optimization method based on the element independent nodal density (EIND) is developed for continuum solids with multiple load cases and multiple constraints. The optimization problem is formulated ad minimizing the volume subject to displacement constraints. Nodal densities of the finite element mesh are used a the design variable. The nodal densities are interpolated into any point in the design domain by the Shepard interpolation scheme and the Heaviside function. Without using additional constraints (such ad the filtering technique), mesh-independent, checkerboard-free, distinct optimal topology can be obtained. Adopting the rational approximation for material properties (RAMP), the topology optimization procedure is implemented using a solid isotropic material with penalization (SIMP) method and a dual programming optimization algorithm. The computational efficiency is greatly improved by multithread parallel computing with OpenMP to run parallel programs for the shared-memory model of parallel computation. Finally, several examples are presented to demonstrate the effectiveness of the developed techniques.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.203-210
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• 2008

Because BSP(Basic Support Protocol) of NEMO(Network Mobility) has important limitation of not providing route optimization, several route optimization schemes have been proposed. By analyzing and improving the limitations of the existing schemes. we Propose an advanced integrated route optimization scheme for the communication through both the internal and external routing of nested NEMO. Our proposal includes a secure route optimization protocol which connects TLMR directly to an external node CN without passing through any HAs. and allows TLMR to control the internal path without passing through the internet. Thus, our scheme can strengthen the security as well as improve the path and delay of NEMO communication.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.99-106
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• 1999

An accelerated optimization technique combined with a stepwise deflation procedure is presented for the efficient evaluation of a few of the smallest eigenvalues and their corresponding eigenvectors of the generalized eigenproblems. The optimization is performed on the Rayleigh quotient of the deflated matrices by the aid of a preconditioned conjugate gradient scheme with the incomplete Cholesky factorization.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.656-666
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• 2014

In delay tolerant networks (DTNs), delay is inevitable; thus, making better use of buffer space to maximize the packet delivery rate is more important than delay reduction. In DTNs, epidemic routing is a well-known routing protocol. However, epidemic routing is very sensitive to buffer size. Once the buffer size in nodes is insufficient, the performance of epidemic routing will be drastically reduced. In this paper, we propose a buffer scheme to optimize the performance of epidemic routing on the basis of the Lagrangian and dual problem models. By using the proposed optimal buffer scheme, the packet delivery rate in epidemic routing is considerably improved. Our simulation results show that epidemic routing with the proposed optimal buffer scheme outperforms the original epidemic routing in terms of packet delivery rate and average end-to-end delay. It is worth noting that the improved epidemic routing needs much less buffer size compared to that of the original epidemic routing for ensuring the same packet delivery rate. In particular, even though the buffer size is very small (e.g., 50), the packet delivery rate in epidemic routing with the proposed optimal buffer scheme is still 95.8%, which can satisfy general communication demand.

• Journal of IKEEE
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• v.25 no.2
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• pp.388-398
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• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.