• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1123-1125
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• 1999

This paper proposes an optimization technique using Genetic Algorithm(GA) for service restoration in the distribution system. Restoration planning problem can be treated as a combinatorial optimization problem. So GA is appropriate to solve the service restoration problem in the distribution network. But searching capabilities of the GA can be enhanced by developing relevant repairing operation and modifying GA operations. In this paper, we aimed at finding appropriate open sectionalizing switch position for the restoration of distribution networks after disturbances using enhanced GA with repairing operation and modified mutation. Simulation results show that proposed method found the open sectionalizing switches with less out of service area and minimize transmission line losses and voltage drop.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.34-38
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• 2012

The recent increase in use of the IEEE 802.15.4 standard for wireless connectivity in personal area networks makes of it an important technology for low-cost low-power wireless personal area networks. Studies showed that voice communications over IEEE 802.15.4 networks is feasible by Guaranteed Time Slot (GTS) allocation; but there are some constraints to accommodate voice transmission beyond two hops due to the excessive transmission delay. In this paper, we propose a GTS allocation scheme for bidirectional voice traffic in IEEE 802.15.4 multihop networks with the goal of achieving fairness and optimization of resource allocation. The proposed scheme uses a greedy algorithm to allocate GTSs to devices for successful completion of voice transmission with efficient use of bandwidth while considering closest devices with another factor for starvation avoidance. We analyze and validate the proposed scheme in terms of fairness and resource optimization through numeral analysis.

• Bulletin of the Korean Chemical Society
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• v.26 no.3
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• pp.399-408
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• 2005

We have utilized neural network (NN) studies to predict impact sensitivities of various types of explosive molecules. Two hundreds and thirty four explosive molecules have been taken from a single database, and thirty nine molecular descriptors were computed for each explosive molecule. Optimization of NN architecture has been carried out by examining seven different sets of molecular descriptors and varying the number of hidden neurons. For the optimized NN architecture, we have utilized 17 molecular descriptors which were composed of compositional and topological descriptors in an input layer, and 2 hidden neurons in a hidden layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4799-4818
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• 2015

In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.4
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• pp.171-183
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• 2008

Nowadays, wireless packet data services are provided over Wireless MAN (WMAN) at a high data service rate, while 3G cellular networks provide wide-area coverage at a low data service rate. The integration of mobile WiMAX and 3G networks is essential, to serve users requiring both high-speed wireless access as well as wide-area connectivity. In this paper, we propose a cross-layer optimization scheme for a vertical handover between mobile WiMAX and 3G cellular networks. More specifically, L2 (layer 2) and L3 (layer 3) signaling messages for a vertical handover are analyzed and reordered/combined, to optimize the handover procedure. Extensive simulations using ns-2 demonstrate that the proposed scheme enhances the performance of a vertical handover between mobile WiMAX and 3G networks: low handover latency, high TCP throughput, and low UDP packet loss ratio.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2367-2370
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• 2003

A fast pattern classification algorithm with Cellular Parallel Processing Network-based dynamic programming is proposed. The Cellular Parallel Processing Networks is an analog parallel processing architecture and the dynamic programming is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast Pattern classification with optimization is formed. On such CPPN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.165-167
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• 2006

In this paper we present a new-generation sensor network processor which is not optimized in circuit level, but in system architecture level. The new design build on a conventional processor architecture, improving the design by focusing on application oriented specification, ISA, and micro-architectural optimization that reduce overall design size and advance energy-per-instruction. The design employs harvard architecture, 8-bit data paths, and an compact 19 bit wide RISC ISA. The design also features a unique interrupt handler which offloads periodical monitoring jobs from the main part of CPU. Our most efficient design is capable of running at 300 KHz (0.3 MIPS) while consuming only about few pJ/instruction.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.395-398
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In order to maximize the efficiency in such applications, this paper proposes the FNN(Fuzzy Neural-Network)-Pl controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the error back propagation algorithm(EBPA). This paper considers the parameter variation about the motor operation. The operating characteristics controlled by efficiency optimization control are examined in detail.

• Computers and Concrete
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• v.21 no.5
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.82-88
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• 1999

In this paper, we propose a new structure optimization method of multilayer neural networks which begin and carry out learning from a bigger network. This method redundant links and neurons according to the rough set theory. In order to find redundant links, we analyze the variations of all weights and output errors in every step of the learning process, and then make the decision table from their variation of weights and output errors. We can find the redundant links from the initial structure by analyzing the decision table using the rough set theory. This enables us to build a structure as compact as possible, and also enables mapping between input and output. We show the validity and effectiveness of the proposed algorithm by applying it to the XOR problem.