• Journal of KSNVE
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• v.11 no.1
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1063-1069
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• 2011

Today, tire performance becomes better as vehicle performance increases. Driviability, endurance, comfortability, noise, and antiwear performance is influenced by tire contour. Tire design method is developed by high-tech engineering technology. Among theses studies, tire performance improvement through tire contour optimization is performed by many vehicle investigator. Therefore, in the present study, an optimum contour design system satisfying the tire performance requirements is constructed by regression analysis and genetic algorithm by using design of experiments.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.3
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• pp.89-97
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• 1997

A genetic algorithm (GA) is a stochastic direct search strategy that mimics the process of genetic evolution. The GA applied herein works on a population of structural designs at any one time, and uses a structured information exchange based on the principles of natural selection and wurvival of the fittest to recombine the most desirable features of the designs over a sequence of generations until the process converges to a "maximum fitness" design. Principles of genetics are adapted into a search procedure for structural optimization. The methods consist of three genetics operations mainly named selection, cross- over and mutation. In this study, a method of finding the optimum topology of truss/beam structure is pro- posed by using the GA. In order to use GA in the optimum topology problem, chromosomes to FEM elements are assigned, and a penalty function is used to include constraints into fitness function. The results show that the GA has the potential to be an effective tool for the optimal design of structures accounting for sizing, geometrical and topological variables.variables.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.5
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• pp.361-368
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• 2015

This paper deals with a robust fine seek controller design problem with multiple constraints using a genetic algorithm. A robust $H\infty$ constraint is introduced to attenuate effectively velocity disturbance caused by the eccentric rotation of the disk. A weighting function is optimally selected based on the estimation of velocity disturbance and the estimated minimum velocity loop gain. A robust velocity loop constraint is considered to minimize the variances of the velocity loop gain and bandwidth against the uncertainties of fine actuator. Finally, a robust fine seek controller is obtained by solving a genetic algorithm with an LMI condition and an appropriate objective function. The proposed controller design method is applied to the fine seek control system of a DVD recording device and is evaluated through the experimental results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.4
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• pp.398-405
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• 2016

A shape of loop-pipe in a compressor affects the vibration of compressor. In this paper, optimal design of shape of loop-pipe to decrease the stress was carried out. Body and shell were assumed to be rigid, while loop-pipe is considered to be flexible. The finite element model was derived and programmed. Genetic algorithm was used for optimization. Locations of 18 point in loop-pipe were considered as shape variables, while the shapes of loop-pipe were interpolated as polynomials or ellipses. Maximum stress of loop-pipe was used as a fitness function for optimization. The spatial constraints and acceleration response of shell were also considered in optimization. The maximum stress and acceleration could be reduced by 79 % and 49 % respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.191-199
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• 2001

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.10
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• pp.677-684
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• 2015

It is often necessary to predict the vibration patterns of the structures from the signals of finite number of vibration sensors. This study presents the optimal placement of vibration sensors by applying the genetic algorithm and the modal expansion method. The modal expansion method is used to estimate the vibration response of the whole structure. The genetic algorithm is used to estimate the optimal placement of vibration sensors. Optimal sensor placement can be obtained so that the fitness function is minimized in the genetic algorithm. This paper discusses the comparison of the performances of two types of fitness functions, modal assurance criteria(MAC) and condition number( CN). As a result, the estimation using MAC shows better performance than using CN.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.197-200
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• 1998

본 논문에서는 유전자 알고리즘(genetic Algorithm)을 사용하여 벡터 양자화(vector quantization : VQ)를 수행하는 방법을 제안하고자 한다. 벡터 양자화를 수행하여 코드북(codebook)을 생성할 때 생성된 코드북과 학습벡터(training vector)사이에는 반드시 양자화 오차(quantization error)가 발생하는데 기존의 K-means 알고리듬을 사용하여 코드북을 생성했을 경우 양자화 오차를 줄이는데 한계가 있었다. 본 논문에서 제안하는 유전자 알고리즘을 이용한 벡터 양자화는 이 양자화 오차를 감소시키기 위해서 연구되었다. 제안한 방법의 성능을 평가하기 위해 음성데이터를 기존의 K-means 알고리즘에서 클러스터의 중심을 선택하는 방법중의 하나인 Minimax방법으로 코드북을 생성하여 제안한 방법과 양자화 오차를 비교한 결과 양자화 오차가 감소됨을 알 수 있었다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.330-340
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• 2007

IEEE 802.11 wireless LANs employ the backoff algorithm to avoid contentions among STAs when two or more STAs attempt to transmit their data frames simultaneously. The MAC efficiency can be improved if the CW values are adaptively changed according to the channel state of IEEE 802.11 wireless LANs. In this paper, we propose a dynamic contention window control algorithm using the genetic algorithm to improve the MAC throughput of IEEE 802.11 wireless LANs.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.797-802
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• 2000

Flow shop scheduling problem involves processing several jobs on common facilities where a setup time Is incurred whenever there is a switch of jobs. Practical aspect of scheduling focuses on finding a near-optimum solution within a feasible time rather than striving for a global optimum. In this paper, a hybrid meta-heuristic method called tabu-genetic algorithm(TGA) is suggested, which combines the genetic algorithm(GA) with tabu list. The experiment shows that the proposed TGA can reach the optimum solution with higher probability than GA or SA(Simulated Annealing) in less time than TS(Tabu Search). It also shows that consideration of setup time becomes more important as the ratio of setup time to processing time increases.