• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1645-1650
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• 2005

FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) process, which are used as the most useful process in industry, are difficult about process identification because of the long dead-time problem and the model mismatch problem. Thus, the accuracy of process identification is the most important problem in FOPDT and SOPDT process control. In this paper, we proposed the real-coded genetic algorithm for identification of FOPDT and SOPDT processes. The proposed method using real-coding genetic algorithm shows better performance characteristic comparing with the existing an area-based identification method and a directed identification method that use step-test responses. The proposed strategy obtained useful result through a number of simulation examples.

• BMB Reports
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• v.37 no.1
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• pp.11-27
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• 2004

The introduction of molecular markers in genetic analysis has revolutionized medicine. These molecular markers are genetic variations associated with a predisposition to common diseases and individual variations in drug responses. Identification and genotyping a vast number of genetic polymorphisms in large populations are increasingly important for disease gene identification, pharmacogenetics and population-based studies. Among variations being analyzed, single nucleotide polymorphisms seem to be most useful in large-scale genetic analysis. This review discusses approaches for genetic analysis, use of different markers, and emerging technologies for large-scale genetic analysis where millions of genotyping need to be performed.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Genomics & Informatics
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• v.8 no.3
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.

• Journal of fish pathology
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• v.22 no.3
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• pp.391-400
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• 2009

Genetic identification of 17 fish-derived Aeromonas strains was attempted using 5 housekeeping genes. 16S rRNA, gyrB, rpoD, dnaJ and recA genes from the 17 strains were amplified, and total of 85 amplicons were sequenced. DNA sequences of the strains and type strains of the 17 Aeromonas homology groups were used for genetic identification and phylogenetic analyses. None of the strains was identified as a single species using the 16S rRNA gene, showing the same identities (average = 99.7%) with several Aeromonas species. According to gyrB, rpoD, dnaJ, and recA, 9 strains and RFAS-1 used in this study were identified as A. hydrophila and A. salmonicida, respectively. However, the other strains were closely related to 2 or more Aeromonas species (i.e., A. salmonicida, A. veronii, A. jandaei, A. media and A. troda) depending on the genetic marker used. In this study, gyrB, rpoD, dnaJ and recA gene sequences proved to be advantageous over 16S rRNA for the identification of field Aeromonas isolates obtained from fish. However, there are discrepancies between analyses of different phylogenetic markers, indicating there are still difficulties in genetic identification of the genus Aeromonas using the housekeeping genes used in this study. Advantages and disadvantages of each housekeeping gene should be taken into account when the gene is used for identification of Aeromonas species.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.225-231
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• 2006

Objectives : This study was performed to determine if unknown species of antler samples could be identified by genetic distance methods. Methods : The DNAs of 4 antler samples were extracted, amplified by PCR, and sequenced. The DNAs of antlers were identified by genetic distance. Genetic distance method was made using MEGA software (Molecular Evolutionary Genetics Analysis, 3.1). Results : By genetic distance methods, all 4 antler samples were closest to Cervus elaphus nelsoni among Cervus species. Conclusion : These results suggest that genetic distance methods might be used as a tool for the identification of Cervus species.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.