• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.564-568
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• 1992

A neural optimization network is designed to solve the collsion-free inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

• 한국정밀공학회지
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• 제22권7호
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• pp.177-184
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• 2005

Recently, the engineering designer of injection mould has become more and more dependent on the CAE. In the design factors of injection mould, the shrinkage rate should be considered as one of the important performances to produce the reliable products. therefore the shrinkage rate can be mostly calculated by the MoldFlow and Pro-engineering. in the design process. However it is not easy to predict the shrinkage rate of a plastic injection mold in its design process because the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. In this study, the surrogate models, DACE model, based on the Kriging in order to optimize the shrinkage rate of electric microwave oven window is used in lieu of the original models, facilitating design optimization.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.475-480
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• 1997

In order to conduct the effective integration of computer-aided conceptual design for integrated nuclear power reactor, not only is a smooth information flow required, but also decision making fur both conceptual design and construction process design must be synthesized. In addition to the aboves, the relations between the one step and another step and the methodologies to optimize the decision variables are verified, in this paper especially, that is, scaling laws and scaling criteria. In the respect with the running of the system, the integrated optimization process is proposed in which decisions concerning both conceptual design are simultaneously made. According to the proposed reactor types and power levels, an integrated optimization problems are formulated. This optimization is expressed as a multi-objective optimization problem. The algorithm for solving the problem is also presented. The proposed method is applied to designing a integrated sub-critical reactors.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.624-636
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• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 가을 학술발표회논문집
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• pp.40-47
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• 2000

One of the drawbacks of GA-based structural optimization is that the fitness evaluation of a population of hundreds of individuals requiring hundreds of structural analyses at each CA generation is computational too expensive. Therefore, a parallel genetic algorithm is developed for structural optimization on a cluster of personal computers in this paper. Based on the parallel genetic algorithm, a population at every generation is partitioned into a number of sub-populations equal to the number of slave computers. Parallelism is exploited at sub-population level by allocationg each sub-population to a slave computer. Thus, fitness of a population at each generation can be concurrently evaluated on a cluster of personal computers. For implementation of the algorithm a virtual distributed computing system in a collection of personal computers connected via a 100 Mb/s Ethernet LAN. The algorithm is applied to the minimum weight design of a steel structure. The results show that the computational time requied for serial GA-based structural optimization process is drastically reduced.

• International Journal of CAD/CAM
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• 제7권1호
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• pp.11-20
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• 2007

A topology optimization methodology, named "smooth boundary topology optimization," is proposed to overcome the shortcomings of cell-based methods. Material boundary is represented by B-spline curves and their control points are considered as design variables. The design is improved by either creating a hole or moving control points. To determine which is more beneficial, a selection criterion is defined. Once determined to create a hole, it is represented by a new B-spline and recognized as a new boundary. Because the proposed method deals with the control points of B-spline as design variables, their total number is much smaller than cell-based methods and it ensures smooth boundaries. Differences between our method and level set method are also discussed. It is shown that our method is a natural way of obtaining smooth boundary topology design effectively combining computer graphics technique and design sensitivity analysis.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.471-477
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• 2014

The novel permanent magnetic actuator (PMA) and its optimal design method were proposed in this paper. The proposed PMA is referred to as the separated permanent magnetic actuator (SPMA) and significantly superior in terms of its cost and performance level over a conventional PMA. The proposed optimal design method uses the evolutionary strategy algorithm (ESA), the kriging meta-model (KMM), and the multi-step optimization. The KMM can compensate the slow convergence of the ESA. The proposed multi-step optimization process, which separates the independent variables, can decrease time and increase the reliability for the optimal design result. Briefly, the optimization time and the poor reliability of the optimum are mitigated by the proposed optimization method.

• 산업공학
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• 제24권1호
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• pp.40-50
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• 2011

We develop a nonlinear integer programming model which minimizes the total cost with the optimal number of operators to hire and their optimal allocation to the tasks under the diverse constraints such as the weekly, daily, and hourly maximum allowable abandonment rates for the time-varying inbound call volume. We present a case study based on actual data at a call center, in order to prove the validity of applying the optimization method proposed. By the one-sample two-tailed t-test, we confirm that the expected abandonment rates resulting from the optimization method are identical with the ones from the discrete-event simulation within specified confidence intervals.

• 전기학회논문지
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• 제67권6호
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• Journal of the Optical Society of Korea
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• 제14권1호
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• pp.22-27
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• 2010

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.