• International journal of advanced smart convergence
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• v.11 no.4
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• pp.28-40
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• 2022

Although precise temperature control in the heat treatment process is a key factor in process reliability, there are many cases where there is no separate heat source control optimization system in the field. To solve this problem, the program monitors the temperature data according to the heat source change through sensor communication in a recursive method based on multiple variables that affect the process, and the target heat source value and the actual heat treatment heat source to match the internal air temperature and material temperature. A control optimization system was constructed. Through this study, the error rate between the target temperature and the atmosphere (material surface) temperature of around 10.7% with the existing heat source control method was improved to an improved result of around 0.1% using a process optimization algorithm and system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.5
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• pp.499-505
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• 2003

In the design of mobile wireless communication system, base station location is one of the most important parameters. Designing base station location, the cost must be minimized by combining various, complex parameters. We can solve this problem by combining optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been used extensively fur global optimization. This paper shows the 4 kinds of algorithm to be applied to the optimization of base station location for communication system and then compares, analyzes the results and shows optimization process of algorithm.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.43-52
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• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.267-270
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• 1997

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.4
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• pp.64-73
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• 1992

This paper presents the design of multi-level logic optimization algorithm and the development of the SMILE system based on the algorithm. Considering the fanin constraints in algorithmic level, SMILE performs global and local optimization in a predefined sequence using heuristic information. Designed under the Sogang Silicon Compiler design environment, SMILE takes the SLIF netlist or Berkeley equation formats obtained from high-level synthesis process, and generates the optimized circuits in the same format. Experimental results show that SMILE produces the promising results for some circuits from MCNC benchmarks, comparable to the popularly used multi-level logic optimization system, MIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.251-254
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• 2002

In the design of mobile wireless communication system, the location of BTS(Base Transciver Stations), RSC(Base Station Controllers), and MSC(Mobile Switching Center) is one of the most important parameters. Designing wireless communication system, the cost of equipment is need to be made low by combining various, complex parameters. We can solve this problem by combinatorial optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been extensively used for global optimization. This paper shows the four kind of algorithms which are applied to the location optimization of BTS, BSC, and MSC in designing mobile communication system and then we compare with these algorithms. And also we analyze the experimental results and shows the optimization process of these algorithms. As a the channel of a CDMA system is shared among several users, the receivers face the problem of multiple-access interference (MAI). Also, the multipath scenario leads to intersymbol interference (ISI). Both components are undesired, but unlike the additive noise process, which is usually completely unpredictable, their space-time structure helps to estimate and remove them.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1262-1269
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• 2003

In structural optimization, static loads are generally utilized although real external forces are dynamic. Dynamic loads have been considered in only small-scale problems. Recently, an algorithm for dynamic response optimization using transformation of dynamic loads into equivalent static loads has been proposed. The transformation is conducted to match the displacement fields from dynamic and static analyses. The algorithm can be applied to large-scale problems. However, the application has been limited to size optimization. The present study applies the algorithm to shape optimization. Because the number of degrees of freedom of finite element models is usually very large in shape optimization, it is difficult to conduct dynamic response optimization with the conventional methods that directly threat dynamic response in the time domain. The optimization process is carried out via interfacing an optimization system and an analysis system for structural dynamics. Various examples are solved to verify the algorithm. The results are compared to the results from static loads. It is found that the algorithm using static loads transformed from dynamic loads based on displacement is valid even for very large-scale problems such as shape optimization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.8
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• pp.1363-1370
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• 2003

In structural optimization, static loads are generally utilized although real external forces are dynamic. Dynamic loads have been considered in only small-scale problems. Recently, an algorithm for dynamic response optimization using transformation of dynamic loads into equivalent static loads has been proposed. The transformation is conducted to match the displacement fields from dynamic and static analyses. The algorithm can be applied to large-scale problems. However, the application has been limited to size optimization. The present study applies the algorithm to shape optimization. Because the number of degrees of freedom of finite element models is usually very large in shape optimization, it is difficult to conduct dynamic response optimization with the conventional methods that directly threat dynamic response in the time domain. The optimization process is carried out via interfacing an optimization system and an analysis system for structural dynamics. Various examples are solved to verify the algorithm. The results are compared to the results from static loads. It is found that the algorithm using static loads transformed from dynamic loads based on displacement is valid even for very large-scale problems such as shape optimization.

• Steel and Composite Structures
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• v.37 no.4
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• pp.493-501
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• 2020

The paper describes an optimization method based on the mathematical model of interaction within multibody 'bridge-track-cars" dynamic system. The interaction is connected with considerable dynamic phenomena influenced by high traffic speed (up to 400 km/h) on high-speed railroads. The trend analysis of a structure is necessary to determine the direction and resource of optimizing the system. Thus, scientific methods of decision-making process are necessary. The process requires a great amount of information analysis dealing with behavior and changes of the "bridge-track-cars system" that consists of mechanisms and structures, including transitions. The paper shows the algorithm of multi-criteria optimization that can essentially reduce weight of a bridge superstructure using big data analysis. This reduction is carried out in accordance with the constraints that have to be satisfied in any case. Optimization of real steel-concrete beam is exemplified. It demonstrates possibility of measures that are offered by the algorithm.