• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• Journal of Communications and Networks
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• v.14 no.2
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• pp.151-161
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• 2012

This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional water-filling interpretations. Next, stochastic data arrivals are addressed. Finally, online and/or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.45-53
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• 2010

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.461-472
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• 2013

In this paper, we deal with a simulation-based reliability and maintainability design problem of a warship and want to determine the optimal values of MTBF and MTTR of all units and ALDT of the warship. The system availability and life cycle cost are used as optimization criteria and estimated by simulation. A hybrid genetic algorithm with a heuristic method is proposed to find near-optimal solutions and numerical examples are also studied to investigate the effect of model parameters to the optimal solutions and compare with a general genetic algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.9-18
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• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.217-224
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• 1986

In case of VLSI design, the shuffle-exchange graph is useful for optimal layout. HOEY and LEISERSON proposed the method of drawing a N-nodes shuffle-exchange graph on O(N2/log N) layout area by using the complex plane digram. [2] In this paper, a new algorithm for drawing the shuffle-exchange graph is proposed. This algorithm is not by using the complex plane diabram, but the table of e decimal represented nodes of shuffle-edge relations. And the structural properties for optimal layout of the graph are summarized and verified. By using this more simplified algorithm, a FORTRAN program which can be treated faster is written. Aimed near optimal shuffle-exchange graphs are printed out by giving inputs` the number of nodes.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2187-2195
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• 2017

In order to solve the AC optimal power flow (OPF) problem considering the generators' on/off status, it is necessary to model the problem as mixed-integer nonlinear programming (MINLP). Because the computation time to find the optimal solution to the mixed-integer AC OPF problem increases significantly as the system becomes larger, most of the existing solutions simplify the problem either by deciding the on/off status of generators using a separate unit commitment algorithm or by ignoring the minimum output of the generators. Even though this kind of simplification may make the overall computation time tractable, the results can be significantly erroneous. This paper proposes a novel algorithm for the mixed-integer AC OPF problem, which can provide a near-optimal solution quickly and efficiently. The proposed method is based on a combination of the outer approximation method and the relaxed AC OPF theory. The method is applied to a real-scale power system that has 457 generators and 2132 buses, and the result is compared to the branch-and-bound (B&B) method and the genetic algorithm. The results of the proposed method are almost identical to those of the compared methods, but computation time is significantly shorter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3329-3350
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• 2017

Installing caching and computing facilities in mobile edge networks is a promising solution to cope with the challenging capacity and delay requirements imposed on future mobile communication systems. The problem of optimal facility placement in mobile edge networks has not been fully studied in the literature. This is a non-trivial problem because the mobile edge network has a unidirectional topology, making existing solutions inapplicable. This paper considers the problem of optimal placement of a fixed number of facilities in a mobile edge network with an arbitrary tree topology and an arbitrary demand distribution. A low-complexity sequential algorithm is proposed and proved to be convergent and optimal in some cases. The complexity of the algorithm is shown to be $O(H^2{\gamma})$, where H is the height of the tree and ${\gamma}$ is the number of facilities. Simulation results confirm that the proposed algorithm is effective in producing near-optimal solutions.