• Structural Engineering and Mechanics
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• 제70권2호
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• pp.209-219
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• 2019

By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2006년도 정기학술발표대회 논문집
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• pp.511-514
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• 2006

자재 관리는 건설프로젝트에서 소요되는 전체 비용의 50%이상에 해당하는 가장 중요한 현장 관리 중 하나이다. 건설 분야의 자재 관리 방법 증 하나로 무선 주파수 인식(Radio Frequency Identification)을 사용한 관리가 시도되고 있다. 하지만 현재 RFID의 트랜스폰더(Transponder)를 어떻게 그리고 어디에 설치하여야 효율적인 자재 관리가 이루어지는 지에 대한 연구가 미비한 실정이고 어떠한 방법으로 최적화를 하여야 효율적인 설치가 될 것인지에 대한 연구도 이루어 지지 않고 있다. 따라서 본 연구에서는 RFID 트랜스폰더를 공사 현장에서 어떻게 그리고 어디에 설치하여야 효율적인 위치 설정이 될 수 있는지 유전자 알고리즘(Genetic Algorithm)을 이용한 최적화 방법에 대하여 알아본다.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 대한기계학회논문집A
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• 제24권6호
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• pp.1633-1642
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• 2000

Like many other mechanical dynamic systems, flexible manipulator systems experience stiction or sticking friction, which may cause input-dependent instabilities. Manipulator performance can be enha nced by identifying friction but it is hard and expensive to measure friction by direct and precise sensing of contact displacements and forces. This study addresses the problem of identifying flexible manipulator joint friction. A dynamic model of a two-link flexible manipulator based upon finite element and Lagrange's method is constructed. The dynamic model includes the effects of joint compliances and actuator dynamics. Friction is also incorporated in the dynamic model to account for stick-slip at the joints. Next, the friction parameters are to be determined. The identification problem is posed as an optimization problem to be solved using nonlinear programming methods. A genetic algorithm is used to increase the convergence rate and the chances of finding the global optimum. The identified friction parameters are experimentally verified and it is expected that the identification technique is applicable to a system parameter identification problem associated with a wide class of nonlinear systems.

• 산업기술연구
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• 제31권B호
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• pp.45-50
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• 2011

Recently, machine olfactory systems that have been proposed as an artificial substitute of the human olfactory system are being studied by many researchers because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. The design method adopted the sequential combination between genetic algorithms and TSK fuzzy logic system. First, the proposed method allowed the designed gas identification system effectively performing the pattern analysis because it was able to avoid the curse of dimensionality caused by use of a large number of sensors. Secondly, the method led the gas identification system to good performance because it was able to deal with drift characteristics of the sensor data by using description ability of the fuzzy system for nonlinear data. In simulation, we demonstrated the effectiveness of the designed gas identification system by using the simulation results of five types of gases.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Earthquakes and Structures
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• 제13권4호
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• pp.397-407
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• 2017

With the growing demand for metallic dampers in engineering practice, it is urgent to establish a reasonable approach to evaluating the mechanical performance of metallic dampers under seismic excitations. This paper introduces an effective method for parameter identification of the modified Bouc-Wen model and its application to evaluating the fatigue performance of metallic dampers (MDs). The modified Bouc-Wen model which eliminates the redundant parameter is used to describe the hysteresis behavior of MDs. Relations between the parameters of the modified Bouc-Wen model and the mechanical performance parameters of MDs are studied first. A modified Genetic Algorithm using real-integer hybrid coding with relative fitness as well as adaptive crossover and mutation rates (called RFAGA) is then proposed to identify the parameters of the modified Bouc-Wen model. A reliable approach to evaluating the fatigue performance of the MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010) is finally proposed based on the research results. Experimental data are employed to demonstrate the process and verify the effectiveness of the proposed approach. It is shown that the RFAGA is able to converge quickly in the identification process, and the simulation curves based on the identification results fit well with the experimental hysteresis curves. Furthermore, the proposed approach is shown to be a useful tool for evaluating the fatigue performance of MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010).

• 한국수산과학회지
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• 제55권5호
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• pp.742-750
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• 2022

This study aimed to monitor the distribution of Cobitidae in Korea by the identification of species using genetic analysis. Based on the genetic analysis, Cobitidae species in four of five domestic fish farms consisted of only Chinese muddy loach Misgurnus mizolepis, but muddy loach Misgurnus anguillicaudatus was also present it in one fish farm. In the case of imported Cobitidae species, in addition to Chinese muddy loach and muddy loach, the harmful species Paramisgurnus dabryanus, was also present. Chinese muddy loach accounted for 20%, 67%, and 60% of the S6, S7, and S8 samples, respectively. An analysis of the total length, body length, and weight showed that domestic Chinese muddy loach showed higher values than imported muddy loach, and imported Chinese muddy loach showed similar values to P. dabryanus. There were no significant differences in the country of origin of the three species. Thus, the mitochondrial cytochrome c oxidase subunit I gene sequence was analyzed and compared the verification of species identification. The three species of Cobitidae were genetically divided into three groups and determined to have genetic differences. These results indicate that it is necessary to reduce the heterogeneous mixing rate through discriminating species by genetic analysis.

• Horticulture, Environment, and Biotechnology : HEB
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• 제59권6호
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• pp.889-897
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• 2018

Currently, there is a lack of genetic markers capable of effectively detecting polymorphisms in Clematis. Therefore, we developed new markers to investigate inter- and intraspecific diversity in Clematis. Based on the complete chloroplast genome of Clematis terniflora, simple sequence repeats were explored and primer pairs were designed for all ten adequate repeat regions (cpSSRs), which were tested in 43 individuals of 11 Clematis species. In addition, the nuclear ITS region was sequenced in 11 Clematis species. Seven cpSSR loci were found to be polymorphic in the genus and serve as markers that can distinguish different species and be used in different genetic analyses, including cultivar identification to assist the breeding of new ornamental cultivars.

• 한국지능시스템학회논문지
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• 제20권6호
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• pp.820-824
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• 2010

유전 알고리즘은 비선형 전역 최적화 문제 해결에 효과적이라고 알려져 있다. 그러나 해답의 신뢰성을 높이려면 많은 양의 계산이 필요하여 온라인 방식에는 적합하지 않다. 본 논문에서는 집단 피드백 유전 알고리즘을 사용한 온라인 비선형 시스템 식별기 구성을 제안한다. 제안된 시스템 식별기의 유용성은 모의실험을 통해 보인다.