• The Microorganisms and Industry
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• v.12 no.1
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• pp.9-14
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• 1986

We have adopted a genetic approach to identifying those residues and local sequences in a polypeptide chain which play an important role on the folding pathway. Our approach has been to isolate and characterize mutants which specifically alter the folding and subunit association pathway of a polypeptide chain, without altering the native protein. Such mutants distinguish residues involved in the kinetic control of conformation from residues involved in the stability and activity of the native protein. This approach is complementary to the efforts to characterize mutations which alter the stability of the mature protein(6,7,8). It is likely that many residues will have roles in both aspects of the functioning of the polypeptide chain. We thought it likely, however, that at least with large proteins, these aspects might be segregated in different local sequences.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.313-318
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• 1996

In this paper, a parametric study of the unbalance response and the stability is carried out to show the influence of seal parameters on the response of rotor. The seal parameters optimized are the seal clearance and the seal length. The minimum quantity of a Q factor in the critical speed and the maximum quantity of a logarithmic decreement in the operating speed, avoiding the reign of resonance, are the objective function. This paper describes a new approach to find a seal parameter of rotor system. The optimization method is used genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics. The results show the capability of this method and indicate that an optimal design of seals can improve the unbalance and the stability of rotor.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.115-121
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• 2002

This paper presents an evolutionary design of digital IIR filters using the genetic algorithm (GA) with modified genetic operators and real-valued encoding. Conventional digital IIR filter design methods involve algebraic transformations of the transfer function of an analog low-pass filter (LPF) that satisfies prescribed filter specifications. Other types of frequency-selective digital fillers as high-pass (HPF), band-pass (BPF), and band-stop (BSF) filters are obtained by appropriate transformations of a prototype low-pass filter. In the GA-based digital IIR filter design scheme, filter coefficients are represented as a set of real-valued genes in a chromosome. Each chromosome represents the structure and weights of an individual filter. GA directly finds the coefficients of the desired filter transfer function through genetic search fur given filter specifications of minimum filter order. Crossover and mutation operators are selected to ensure the stability of resulting IIR filters. Other types of filters can be found independently from the filter specifications, not from algebraic transformations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.241-246
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• 2004

This paper proposed trajectory tracking control of mobile robot. Trajectory tracking control scheme are real coding genetic-algorithm and back-propergation algorithm. Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studios have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using Real coding Genetic Algorithm(RCGA) and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verify numerical simulations and the results show better performance than constant gain controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.615-620
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• 2001

This paper addresses the design of sliding model fuzzy-model-based controller using genetic algorithms. In general, the construction of fuzzy logic controllers has difficulties for the lack of systematic design procedure. To release this difficulties, the sliding model fuzzy-model-based controllers was presented by authors. In this proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Although, the stability and the performance is guaranteed by the proposed method, some design parameters have to be chosen by the designer manually. This problem can be solved by using genetic algorithms. The proposed method tunes the parameters of the controller, by which the reasonable accuracy and the control effort is achieved. The validity and the efficiency of the proposed method are verified through simulations.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.99-106
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• 2003

In this paper, a new optimized design methodology is proposed. It integrates the discrete element method (DEM) and real-coded genetic algorithm for the design of landfill lining system subjected to equipment loadings. In applying the design method to a tapered lining system, the effect of the taperness, which means the change of shape for cover soil, is examined. The optimization problem to maximize the capacity of a waste-containment facility is solved using real coded genetic algorithm. Numerical example analysis is carried out for a typical landfill slope structure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.301-305
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• 1996

$\mu$ theory can handle the parametric uncertainty and produces more non-conservative controller than H$_{\infty}$ control theory. However an existing solution of the theory, D-K iteration, creates a controller of huge order and cannot handle the real or mixed real-complex perturbation sets. In this paper, we use genetic algorithms to solve these problems of the D-K iteration method. The Youla parameterization is used to obtain all stabilizing controllers and the genetic algorithms determines the values of the state feedback gain, the observer gain, and Q parameter to minimize $\mu$, the structured singular value, of given system. From an example, we show that this method produces lower order controller which controls a real parameter-perturbed plant than D-K iteration method.

• Journal of Plant Biotechnology
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• v.36 no.4
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• pp.344-351
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• 2009

Current status of soybean transformation method in Korera was reviewed with recent publications. Most frequently used method for genetic transformation was Agrobacterium-mediated transformation on cotyledonary node which is most popular method used in foreign country. In addition to this, various methods such as sonicationmediated transformation, in planta transformation, and transformation on meristem tissue of germinating seed, have been tried in Korea, even though their efficiencies on repeatability and stability were relatively low. Based on the promising results developed recently by reviewer, several important considerations for successful soybean transformations were suggested. They are 1) proper genotype screening, 2) targeting transformation on exact point, 3) multiple shoot formation, 4) efficient selection pressure, 5) successful shoot elongation, 6) efficient root formation. These are the basic requirements for stable and highly efficient soybean transformation of Korean soybean.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.