• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.40-47
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• 1996

This paper describes a new approach to solve balancing of a rigid rotor. In this paper, the balancing of the rigid rotor using genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics is proposed. Under the assumption that the initial vibration values used to calculate correction masses contain errors, the influence coefficient method, the least squares method and a genetic algorithm are compared. The results show that the vibration amplitude obtained with the least squares method and the genetic algorithm is smaller than that obtained with the influence coefficient method.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.140-140
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• 2000

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. In this paper, we use random process and simulated annealing instead of mutation operator in order to get well tuned fuzzy rules. The key of this approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1423-1443
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• 2011

Background compensation plays an important role in detecting and isolating object motion in visual tracking. Here, we propose a Genetic Hough Transform, which combines the Hough Transform and Genetic Algorithm, as a method for eliminating background motion. Our method can handle cases in which the background may contain only a few, if any, feature points. These points can be used to estimate the motion between two successive frames. In addition to dealing with featureless backgrounds, our method can successfully handle motion blur. Experimental comparisons of the results obtained using the proposed method with other methods show that the proposed approach yields a satisfactory estimate of background motion.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.293-298
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• 1997

The H$_{\infty}$robust controller fur the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of non-convex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. robust design.

• Journal of Genetic Medicine
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• v.12 no.1
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• pp.6-11
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• 2015

Intellectual disability (ID) is the most common disability among people under the age of 20 years. In the absence of obvious non-genetic causes of ID, the majority of cases of severe ID are thought to have a genetic cause. The advent of technologies such as array comparative genomic hybridization, single nucleotide polymorphism genotyping arrays, and massively parallel sequencing has shown that de novo copy number variations and single nucleotide variations affecting coding regions are major causes of severe ID. This article reviews the genetic causes of ID along with diagnostic approaches for this disability.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.2
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• pp.101-114
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• 2000

Multicasting is the simultaneous transmission of data to multiple destinations. In multicast ATM networks the effect of failures on transmission links or nodes can be catastrophic so that the issue of survivability is of great importance. However little attention has been paid to the problem of multicast restoration. This paper presents an efficient heuristic technique for routing backup virtual paths in ulticast networks with link failure. Genetic algorithm is employed here as a heuristic. In the application of genetic algorithm to the problem, a new genetic encoding and decoding method and genetic operators are proposed in this paper. The other several heuristics are also presented in order to assess the performance of the proposed algorithm. Experimental results demonstrate that our algorithm is a promising approach to solving the problem.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.40-50
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• 2008

The problem of assigning customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. For this combinatorial optimization problem, standard optimization methods take a large computation time and so genetic algorithms (GA) and ant colony optimization (ACO) can be used to obtain the best and/or optimal assignment of customers to satellite channels. In this paper, we present a comparative study of GA and ACO to this problem. Various issues related to genetic algorithms approach to this problem, such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. We also discuss an ACO for this problem. In ACO methodology, three strategies, ACO with only ranking, ACO with only max-min ant system (MMAS), and ACO with both ranking and MMAS, are considered. A comparison of these two approaches (i,e., GA and ACO) with the standard optimization method is presented to show the advantages of these approaches in terms of computation time.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.749-754
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• 2014

In this paper, we suggest tree-structure-aware GP (Genetic Programming) operators that heed tree distributions in structure space and their possible structural difficulties. The main idea of the proposed GP operators is to place the generated offspring of crossover and/or mutation in a specified region of tree structure space insofar as possible by biasing the tree structures of the altered subtrees, taking into account the observation that most solutions are found in that region. To demonstrate the effectiveness of the proposed approach, experiments on the binomial-3 regression, multiplexor and even parity problems are performed. The results show that the results using the proposed tree-structure-aware operators are superior to the results of standard GP for all three test problems in both success rate and number of evaluations.

• Childhood Kidney Diseases
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• v.27 no.1
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• pp.11-18
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• 2023

Remarkable advances in genetic diagnosis expanded our knowledge about inherited tubulopathies and other genetic kidney diseases. This review suggests a simple categorization of inherited tubular disease, clarifies the concept of autosomal dominant tubulointerstitial kidney disease (ADTKD), and introduces novel therapies developed for tubulopathies. Facing patients with suspicious tubular disorders, clinicians should first evaluate the status of volume and acid-base. This step helps the clinicians to localize the affected segment and to confirm genetic diagnosis. ADTKD is a recently characterized disease entity involving tubules. The known causative genes are UMOD, MUC1, REN, and HNF1β. Still, only half of ADTKD patients show mutations for these four identified genes. Whole exome sequencing is a suitable diagnostic tool for tubulopathies, especially for ADTKD. Genetic approaches to treat tubulopathies have progressed recently. Despite the practical obstacles, novel therapies targeting inherited tubulopathies are currently in development.