• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.479-485
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• 2000

This paper presents a analysis method for dynamic characteristics of power system using a Genetic-based Power System Stabilizer(PSS). The proposed PSS parameters are optimized using Genetic Algorithm(GA) in order to maintain optimal operation of generator under the various operating conditions. To decrease the computational time, real variable string is adopted. The results tested on a single machined infinite bus system verify that the proposed controller has better dynamic performance than conventional controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1871-1875
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• 1997

Crane operation is manually accomplished by skilled operators. Recently, the concept of automation is widely introduced in shipping and unloading operation using the overhead crane for the enhanced productivity. In this regards, we designed an angle detector and 3D position detectro which are key evices for this operation. As well as an intellignet control algorithm is developed for the implementation of swing free crane. The performance of the presented algorithm is tested for the swing angle and the position of the overheas crand. The control scheme adopts a feedback control of an angular velocity of swing in initial phase and then the fuzzy controller whose rule base is optimized by a genetic algorithm.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.369-376
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• 2007

This paper proposes a hybrid genetic algorithm(HGA) for the classifier ensemble selection. HGA is added a local search operation for increasing the fine-turning of local area. This paper apply hybrid and simple genetic algorithms(SGA) to the classifier ensemble selection problem in order to show the superiority of HGA. And this paper propose two methods(SSO: Sequential Search Operations, CSO: Combinational Search Operations) of local search operation of hybrid genetic algorithm. Experimental results show that the HGA has better searching capability than SGA. The experiments show that the CSO considering the correlation among classifiers is better than the SSO.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.969-976
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• 1999

This paper is concerned with the generation of a balancing trajectory for improving the walking performance. The balancing motion has been determined by solving a second -order differential equation. However, this method caused some difficulties in linearizing and approximating the equation and had restrictions on using various balancing trajectories. The proposed difficulties in linearizing and approximating the equation and had restrictions on using various balancing trajectories. The proposed method i this paper is based on the genetic algorithm for minimizing the motins of balancing joints, whose trajectories are generated by the fifth-order polynomial interpolation after planning leg trajectories. The real walking experiments are made on the biped robot IWR-III, developed by our Automatic Control Laboratory. The system has 8 degrees of freedom and the structure of three pitches in each leg, and one roll and one prismatic joint in the balancing joints. The experimental result shows the validity and applicability of the new proposed algorithm.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.781-784
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAx clusterng algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action. But current segmentation methods based edge extraction methods generally need the mask information for the algebraic model, and take long run times at mask operation, wheras the proposed algorithm has single operation ccording to active searching of fuzzy worms. In addition, we also genetic min-max clustering using genetic algorithm to complete clustering and fuzyz searching on grey-histogram of image for the optimum solution, which can automatically determine the size of rnages and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.429-434
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• 2002

For companies assembling end products from sub assemblies or components, MRP (Material Requirement Planning) logic is frequently used to synchronize and pace the production activities for the required parts. However, in MRP, the planning of operational-level activities is left to short term scheduling. So, we need a good scheduling algorithm to generate feasible schedules taking into account shop floor characteristics and multi-level job structures used in MRP. In this paper, we present a GA (Genetic Algorithm) solution for this complex scheduling problem based on a new gene to reflect the machine assignment, operation sequences and the levels of the operations relative to final operation. The relative operation level is the control parameter that paces the completion timing of the components belonging to the same branch in the multi-level job hierarchy. In order to revise the fixed relative level which solutions are confined to, we apply large step transition in the first step and GA in the second step. We compare the genetic algorithm and 2-phase optimization with several dispatching rules in terms of tardiness for about forty modified standard job-shop problem instances.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1816-1822
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• 2010

In this research TPS (Train Performance Simulation) blocks are designed using Simulink and applied to generate speed profiles for energy-efficient train operation. With a train operation mode of maximum powering, coasting, and maximum breaking, a breaking point is calculated from forward-backward running profiles. Then, GA (Genetic Algorithm) is used to solve a running time constraint, and a coasting point is produced from the searching process of GA. With the breaking point and the coasting point a speed profile is plotted. Train performance under a speed limit and gradient variations is simulated and resultant speed profiles are analyzed.

• IE interfaces
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• v.14 no.4
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• pp.385-393
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• 2001

In this paper, a scheme of scheduling a bottleneck operation is presented for production planning of make-to-order. We focus on the problem of capacity-dependent processing time in which processing time of the bottleneck operation is not fixed, but varies with job sequence or equipment capacity. For this, a genetic algorithm is applied for job sequencing with an objective function of mean square of weighted deviation. An experimental study is implemented in power transformer plant and results are compared with those of the EDD rule. It shows that the genetic algorithm is relatively good for most cases.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.658-668
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• 2009

Earth-moving operation has a great impact on the overall budget and schedule of any heavy civil projects. More often than not, the operational decisions are made largely based on field personnel's experience and judgment. In particular, decisions on earth moving operations by scraper-dozer fleets have been heavily influenced by the following belief: "The longer a dozer pushes a scraper for loading, the better earth-moving productivity is gained by the fleet." Even though there is some truth to this notion, scraper-dozer earth moving operations involve a much complex process that requires a systematic analysis for predicting the maximum production. To this end, this paper presents a spreadsheet-based scraper-dozer fleet operation model for its production optimization. Various optimization techniques, including a genetic-algorithm method, are presented for comparison and each technique's pros and cons are discussed.