• Asian-Australasian Journal of Animal Sciences
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• v.25 no.2
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• pp.170-176
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• 2012

Genetic diversities, population genetic structures and demographic histories of the thread-sail filefish Stephanolepis cirrhifer were investigated by nucleotide sequencing of 336 base pairs of the mitochondrial DNA (mtDNA) control region in 111 individuals collected from six populations in Korean coastal waters. A total of 70 haplotypes were defined by 58 variable nucleotide sites. The neighbor-joining tree of the 70 haplotypes was shallow and did not provide evidence of geographical associations. Expansion of S. cirrhifer populations began approximate 51,000 to 102,000 years before present, correlating with the period of sea level rise since the late Pleistocene glacial maximum. High levels of haplotype diversities ($0.974{\pm}0.029$ to $1.000{\pm}0.076$) and nucleotide diversities (0.014 to 0.019), and low levels of genetic differentiation among populations inferred from pairwise population FST values (-0.007 to 0.107), support an expansion of the S. cirrhifer population. Hierarchical analysis of molecular variance (AMOVA) revealed weak but significant genetic structures among three groups ($F_{CT}$ = 0.028, p<0.05), and no genetic variation within groups (0.53%; $F_{SC}$ = 0.005, p = 0.23). These results may help establish appropriate fishery management strategies for stocks of S. cirrhifer and related species.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.619-624
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• 2011

This paper presents a controller design method for a robust control problem with multiple constraints using genetic algorithms and LMI design method. A robust $H_{\infty}$ constraint with loop shaping and pole placement is used to address disturbance attenuation with error limits and desired transient specifications, in spite of the plant uncertainties and disturbances. In addition, a loop gain constraint is considered so as not to enlarge the loop gain unnecessarily. The robust $H_{\infty}$ constraint and pole placement constraint can be expressed in terms of two matrix inequalities and the loop gain constraint can be considered as an objective function so that genetic algorithms can be applied. Accordingly, a robust controller can be obtained by integrating genetic algorithms with LMI approach. The proposed controller design method is applied to a track-following system of an optical disk drive and is evaluated through simulation results.

• The Plant Pathology Journal
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• v.32 no.2
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• pp.145-156
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• 2016

Fusarium graminearum Schwabe causes Fusarium head blight (FHB), a devastating disease that leads to extensive yield and quality loss of wheat and other cereal crops. Twelve isolates of F. graminearum were collected from naturally infected spikes of wheat from Assiut Egypt. These isolates were compared using SRAP. The results indicated distinct genetic groups exist within F. graminearum, and demonstrated that these groups have different biological properties, especially with respect to their pathogenicity on wheat. There were biologically significant differences between the groups; with group (B) isolates being more aggressive towards wheat than groups (A) and (C). Furthermore, Trichoderma harzianum (Rifai) and Bacillus subtilis (Ehrenberg) which isolated from wheat kernels were screened for antagonistic activity against F. graminearum. They significantly reduced the growth of F. graminearum colonies in culture. In order to gain insight into biological control effect in situ, highly antagonistic isolates of T. harzianum and B. subtilis were selected, based on their in vitro effectiveness, for greenhouse test. It was revealed that T. harzianum and B. subtilis significantly reduced FHB severity. The obtained results indicated that T. harzianum and B. subtilis are very effective biocontrol agents that offer potential benefit in FHB and should be harnessed for further biocontrol applications. The accurate analysis of genetic variation and studies of population structures have significant implications for understanding the genetic traits and disease control programs in wheat. This is the first known report of the distribution and genetic variation of F. graminearum on wheat spikes in Assiut Egypt.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.47.1-47
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• 2002

$\textbullet$ Introduction $\textbullet$ Genetic Algorithm $\textbullet$ Optimal Control $\textbullet$ Simulation $\textbullet$ Conclusion

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.645-647
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• 1998

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and a steady state genetic algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is a optimization algorithm, tunes the input-output membership parameters and fuzzy rules automatically. The computer simulation to control a inverted pendulum is presented to illustrate the approaches.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.2
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• pp.62-69
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• 2000

In this paper, we design a GA-fuzzy precompensated PID controller for the load frequency control of two-area interconnected power system. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic-based precompensation approach for PID controller. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PID controller. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PID control and a fuzzy precompensated PID control in dynamic responses about the load disturbances of power system and is convinced robustness reliableness in view of structure.

• 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 Information Technology Services
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• v.10 no.3
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• pp.167-177
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• 2011

Code obfuscation is a technique that protects the abstract data contained in a program from malicious reverse engineering and various obfuscation methods have been proposed for obfuscating intention. As the abstract data of control flow about programs is important to clearly understand whole program, many control flow obfuscation transformations have been introduced. Generally, inlining is a compiler optimization which improves the performance of programs by reducing the overhead of calling invocation. In code obfuscation, inlining is used to protect the abstract data of control flow. In this paper, we define new control flow complexity metric based on entropy theory and N-Scope metric, and then apply genetic algorithm to obtain optimal inlining results, based on the defined metric.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.119-121
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• 1997

This paper suggests a design method of the robust model following PD control system using genetic algorithm. This PD control system is designed by applying genetic algorithm with reference model to the optimal determination of proportional and derivative gains that are given by PD servo controller. These proportional and derivative gains are optimized simultaneously in the search domain guaranteeing the robust stability of closed-loop system satisfying different stability margins. The effectiveness of this PD control system is verified by computer simulation.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.214-217
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• 2002

The chromosomes of Genetic Algorithm(GA) are classified to be good or not to be by Support vector machines(SVM), and then the only good chromosomes are adopted to the evolution process. By this way, computational load becomes low, so the evolution speed of Genetic Algorithm modified by SVM can be much accelerated than the conventional GA.