• 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 Electrical Engineering and Technology
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• v.4 no.4
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• pp.457-466
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• 2009

This paper presents efficient parallel genetic algorithm (EPGA) based decomposed network for optimal power flow with various kinds of objective functions such as those including prohibited zones, multiple fuels, and multiple areas. Two coordinated sub problems are proposed: the first sub problem is an active power dispatch (APD) based parallel GA; a global database generated containing the best partitioned network: the second subproblem is an optimal setting of control variables such as generators voltages, tap position of tap changing transformers, and the dynamic reactive power of SVC Controllers installed at a critical buses. The proposed approach tested on IEEE 6-bus, IEEE 30-bus and to 15 generating units and compared with global optimization methods (GA, DE, FGA, PSO, MDE, ICA-PSO). The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time.

• 전기의세계
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• v.30 no.5
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• pp.297-305
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• 1981

The problem of finding shortest paths between every pair of points in a network is solved employing and optimal network decomposition in which the network is decomposed into a number of subnetworks minimizing the number of cut-set between them while each subnetwork is constrained by a size limit. Shortest path computations are performed on individual subnetworks, and the solutions are recomposed to obtain the solution of the original network. The method when applied to large scale networks significantly reduces core requirement and computation time. This is demonstrated by developing a computer program based on the method and applying it to 30-vertex, 160-vertex, and 273-vertex networks.

• 전기의세계
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• v.22 no.2
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• pp.40-44
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• 1973

A method for obtaining state equation for nonlinear time-varying RLC networks is presented. The nonlinear time-varying RLC elements are decomposed by using Murata method to formulate nonlinear state equation. A nonlinear time-varying RLC network containing twin tunnel diode is solved as an example. In consequence of solving the examjple, simple methods are presented for revising the original network model so that the formulation of state equation is simplified.

• Journal of Communications and Networks
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• v.1 no.3
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• pp.189-200
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• 1999

For admitting a hard real-time connection to an ATM network, it is required that the end-새둥 delays of cells belong-ing to the connection meet their deadlines without violating the guarantees already provided to the currently active connections. There are two kinds of methods to analyze the end-to-end delay in an ATM network. A decomposed method analyzes the worst case delay for each switch and then computes the total delay as the sum of the delays at individual switches. On the other hand, an integrated method analyzes all the switches involved in an inte-grated manner and derives the total delay directly. In this paper, we present an efficient and effecitive integrated method to compute the end-to-end delay. We evaluate the network performance under different system parameters and we compare the performance of the proposed method with the conventional decomposed and other integrated methods [1], [3], [5]-[9].

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.131-134
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• 1990

A back-propagation neural network based system for a fault diagnosis of a chemical process is developed. Training data are acquired from FCD(Fault-Consequence Digraph) model. To improve the resolution of a diagnosis, the system is decomposed into 6 subsystems and the training data are composed of 0, 1 and intermediate values. The feasibility of this approach is tested through case studies in a real plant, a naphtha furnace, which has been used to develop a knowledge based expert system, OASYS (Operation Aiding expert SYStem).

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.55-65
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• 2009

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed was tested on the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at a critical situation and within a reasonable time.

• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.4
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• pp.21-27
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• 1975

In this paper, a procedure for deriving of multiple fault detection test sets is presented for fan-out reconvergent combinational logic networks. A fan-out network is decomposed into a set of fan-out free subnetworks by breaking the internal fan-out points, and the minimal detecting test sets for each subnetwork are found separately. And then, the compatible tests amonng each test set are combined maximally into composite tests to generate primary input binary vectors. The technique for generating minimal test experiments which cover all the possible faults is illustrated in detail by examples.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.86-94
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• 1993

This paper presents the two-stage neural network method for efficient channel assignment of cellular mobile radio network. The first stage decomposes the region into non-adjacent groups of cells and the second stage assigns channels to the decomposed groups. The neural network model is tested with an experimental system of eighteen channels dedicated for nineteen hexagonal-cell region. When radom call requests of average density of 2 Erl/Cell to 8 Erl/Cell are presented, the real-time channel assignment method reduces the call-blocking rate up to 16% against the existing SCA(Static Channel Assignment) method.