• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.714-716
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• 1996

This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.166-168
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• 1996

This paper presents an power system optimization method which solves real and reactive power dispatch problems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods, applied to the IEEE 30-bus system, were run for 12 other exogenous parameters. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.84-90
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• 2019

Tremendous computation of full search or lossless motion estimation algorithms for video coding has led development of many fast motion estimation algorithms. We still need proper control of computation and prediction quality. In the paper, we suggest an algorithm that reduces computation effectively and controls computational amount and prediction quality, while keeping prediction quality as almost the same as that of the full search. The proposed algorithm uses multiple thresholds for partial block sum and times of counting unchanged minimum position for each step. It also calculates the partial block matching error, removes impossible candidates early, implements fast motion estimation by comparing times of keeping the position of minimum error for each step, and controls prediction quality and computation easily by adjusting the thresholds. The proposed algorithm can be combined with conventional fast motion estimation algorithms as well as by itself, further reduce computation while keeping the prediction quality as almost same as the algorithms, and prove it in the experimental results.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1066-1070
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• 1990

This paper presents a method for the optimal control of the distributed parameter systems (DPSs) by a hierarehical computational procedure. Approximate lumped parameter systems (LPSs) are derived by using the Galerkin method employing the Legendre polynomials as the basis functions. The DPSs however, are transformed into the large scale LPSs. And thus, the hierarchical control scheme is introduced to determine the optimal control inputs for the obtained LPSs. In addition, an approach to block pulse functions is applied to solve the optimal control problems of the obtained LPSs. The proposed method is simple and efficient in computation for the optimal control of DPSs.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.1-26
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• 2008

This study presents a new estimation of optimal amount of B/L for infantry rifles and artillery weapons which are currently using in Korean Army. To do that, we analyzed current concept of B/L and methodologies used to compute B/L before, and re-calculate optimal amount of B/L to solve some problems founded. We also applied ARENA simulation model to propose estimated values of optimal B/L. The proposed optimal B/L will certainly help to improve efficiency for wartime readiness in the future.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1075-1085
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• 1990

This paper presents a new method for the optimal control of the distributed parameter systems by a decentralized computational procedure. Approximate lumped parameter models are derived by using the Galerkin method employing the Legendre polynomials as the basis functions. The distributed parameter systems, however, are transformed into the large scale lumped parameter models. And thus, the decentralized control scheme is introduced to determine the optimal control inputs for the obtained lumped parameter models. In addition, an approach to block pulse functions is applied to solve the optimal control problems of the obtained lumped parameter models. The proposed method is simple and efficient in computation for the optimal control of distributed paramter systems. Illustrative examples given to demonstrate the validity of the presently proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.519-528
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• 2000

This pater presents a new fractal coding scheme to find more optimal transformation using the reference images which I determined by some criteria. After finding the transformations to minimize the distance between the original image and the reference images. we choose one of them which has the best performance. Conventional fractal coding schemes based on the collage theorem obtain the transformation to minimize the distance between an original image and its collage image because of heavy computation. In other words, it is because the optimal transformation and be obtained after the attractors of all the possible transformations are generated and then compared with an original image. As such a procedure is practically difficult to implement. the collage coding schemes using the theorem have widely used, We introduce the new scheme to overcome the complexity problem for the optimal transformation and be obtained, our scheme is evaluated. as compared with the optimal one, In general cases that the optimal one is unavailable. our scheme is also evaluated, as compared with the conventional schemes.