• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Journal of Drive and Control
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• v.17 no.2
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• pp.1-8
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• 2020

Recently, industrial systems are becoming quicker and lighter to enable the reduction of energy consumption and increase productivity. So the latest systems are more flexible and rapid than the previous systems. But, with this improvement, another problem has emerged, such as the increase in residual vibration when a system is started or stopped. The input shaper is a command generation method that can remove residual vibration. It can provide a solution to the problem of residual vibration in industrial systems. However, it is difficult to generate the input shaper in high order systems, such as a typical industrial system because the input shaper is induced from the system's vibration characteristics. This study focused on the extra insensitivity shaper that can compensate for the system's modeling error such as input dynamics, and the high order's system affection. A genetic algorithm was deployed to adjust a vibration limitation for the extra insensitivity of the input shaper. A plant is a low damping system that includes one zero and a pole. The fitness functions are an error signal of the system's response with normalized frequency variations. Verification of the suggested system is satisfied by comparison between the zero vibration derivative input shaper's response and the suggested one.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.122-128
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• 2013

Genetic diversity and population genetic structure were estimated in nine natural populations of Exochorda serratifolia in South Korea using ISSR marker system. Average of polymorphic loci per primer was 5.8 (S.D.=2.32) and percentage of polymorphic loci per population was 78.7% with total 35 loci from 6 ISSR primers. In AMOVA, 27.8% of total genetic variation came from genetic difference among populations and 72.2% was resulted from difference among individual trees within populations. Genetic differentiations by Bayesian inference were 0.249 of ${\theta}^{11}$ and 0.227 of $G_{ST}$. Inbreeding coefficient for total populations was 0.412. There was significant correlation between genetic distance and geographic distance among populations. On the results of Bayesian cluster analysis, nine populations were assigned into three groups. The first group included 5 populations, and the second and the third had two populations per group, respectively. These three regions could explain 10.0% of total genetic variation from hierarchical AMOVA, and the levels of among-population and among-individual were explained 19.7% and 70.3%, respectively. The geographic distribution of populations following the three Bayesian clusters could be explained with mountain range as Baekdudaegan which is the main chain of mountains in Korea. The mountains as the physical barrier might hamper gene flow in the pearlbush. So when protected areas are designated for conservation of this species, we should consider those three regions into considerations and would better to choose at least one population per region.

• Journal of Plant Biotechnology
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• v.3 no.1
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• pp.13-17
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• 2001

A Chromium (VI)[Cr(VI)] reductase gene from heavy metal reducing bacteria Pseudomonas aeruginosa HP014 was used to transform tobacco plant cells. A chimeric construct containing the Cr(VI) reductase gene was transfered to tobacco leaf disks using an Agrobacteriun tumefaciens binary vector system. From the leaf disks, transformed plantlets were regenerated. Hybridization experiments demonstrated that the Cr(VI) reductase gene was inserted into and expressed in the regenerated plants. The Cr(VI) reduction activity showed that the transgenic plants may be a another possible tool to reduce the pollution of the toxic Cr(VI) in soil.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1473-1476
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• 1997

This paper presents a scheme for continuous-time fuzzy modelling of nonlinear systems, based on the adjustment technique and the genetic algorithm technque. The fuzzy model is characterized by fuzzy "If-then" rules whcih represent locally linear input-output relations whose consequence part is defined as subsystem of a nonlinear system. To compute the final output and deal with the initialization and unmeasurable signal problems in on-line estimatio of the fuzzy model, a discrete-time model is obtaned. Then the parameters of both the premis and consequence of the fuzzy model are adjusted on-line by a genetic algorithm. A simulation work is carried out to demonstrate the effectiveness of the proposed method.ed method.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.559-564
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• 2001

The design of an automobile mirror actuator system needs a systematic optimization due to several variables, constraints, geometric limitations, moving angle, and so on. Therefore, this article provides the procedure of a genetic algorithm(GA) based optimization with finite element analysis for design of a mirror actuator considering design constraints, geometric limitations, moving angle. Local optimum problem in optimization design with sensitivity analysis is overcome by using zero-order overall searching method which is new optimization design method using a genetic algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.7
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• pp.863-871
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• 2007

This paper proposes a methodology to estimate the system coefficients for the infinite impulse response(IIR) digital filters using real code GA. In the traditional real coded GA, it adapts the general genetic operations, whereas in this paper the proposed real coded GA applies improved genetic operations in order to search the optimal solution in given problems. Each of unknown IIR digital coefficients collected as forms of a chromosome. Two illustrative examples including the band pass and band stop IIR digital filters are demonstrated to verify the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.10-15
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• 2012

This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic capacity of sub tree. This problem is known to be NP-hard. To efficiently solve the problem, a tabu list coupled genetic algorithm approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation