• Journal of Power System Engineering
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• v.2 no.1
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• pp.52-58
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-objective problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed algorithm is identified by using multi-peak function which have many local optimums and identification of the flexural rigidity for wire rope model.

• Smart Structures and Systems
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• v.23 no.2
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• pp.123-139
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• 2019

Impact forces induced by external object collisions can cause serious damages to civil engineering structures. While accurate and prompt identification of such impact forces is a critical task in structural health monitoring, it is not readily feasible for civil structures because the force measurement is extremely challenging and the force location is unpredictable for full-scale field structures. This study proposes a novel approach for identification of impact force including its location and time history using a small number of multi-metric observations. The method combines an augmented Kalman filter (AKF) and Genetic algorithm for accurate identification of impact force. The location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations and then time history of the impact force is accurately constructed by optimizing the error co-variances of AKF using Genetic algorithm. The efficacy of proposed approach is numerically demonstrated using a truss and a plate model considering the presence of modelling error and measurement noises.

• Journal of Ginseng Research
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• v.34 no.1
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• pp.41-46
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• 2010

Expensive herbs such as ginseng are always a possible target for fraudulent labeling. New mountain ginseng strains have occasionally been found deep within mountain areas and commercially traded at exorbitant prices. However, until now, no scientific basis has existed to distinguish such ginseng from commonly cultivated ginseng species other than by virtue of being found within deep mountain areas. Polymerase chain reaction (PCR) analysis of the internal transcribed spacer has been shown to be an appropriate method for the identification of the most popular species (Panax ginseng) in the Panax ginseng genus. A single nucleotide polymorphism (SNP) has been identified between three newly found mountain ginseng (KGD4, KGD5, and KW1) and already established Panax species. Specific PCR primers were designed from this SNP site within the sequence data and used to detect the mountain ginseng strains via multiplex PCR. The established multiplex-PCR method for the simultaneous detection of newly found mountain ginseng strains, Korean ginseng, and foreign ginseng in a single reaction was determined to be effective. This study is the first report of scientific discrimination of "mountain ginsengs" and describes an effective method of identification for fraud prevention and for uncovering the possible presence of other, cheaper ginseng species on the market.

• Journal of Society of Preventive Korean Medicine
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• v.27 no.2
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• pp.35-48
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• 2023

Objective : We studied prognostic biomarkers discovery for lung cancer based on the pattern identification for the personalized Korean medicine. Methods : Using 30 tissue samples, we performed a whole exome sequencing to examine the genetic differences among three groups. Results : The exome sequencing identified among 23,490 SNPs germline variants, 12 variants showed significant frequency differences between Xu and Stasis groups (P<0.0005). As similar, 18 and 10 variants were identified in analysis for Xu vs. Gentleness group and Stasis vs. Gentleness group, respectively (P<0.001). Our exome sequencing also found 8,792 lung cancer specific variants and among the groups identified 6, 34, and 12 variants which showed significant allele frequency differences in the comparison groups; Xu vs. Stasis, Xu vs. Gentleness group, and Stasis vs. Gentleness group. As a result of PCA analysis, in germline data set, Xu group was divided from other groups. Analysis using somatic variants also showed similar result. And in gene ontology analysis using pattern identification variants, we found genes like as FUT3, MYCBPAP, and ST5 were related to tumorigenicity, and tumor metastasis in comparison between Xu and Stasis. Other significant SNPs for two were responsible for eye morphogenesis and olfactory receptor activity. Classification of somatic pattern identification variants showed close relationship in multicellular organism reproduction, anion-anion antiporter activity, and GTPase regulator activity. Conclusions : Taken together, our study identified 40 variants in 29 genes in association with germline difference of pattern identification groups and 52 variants in 47 genes in somatic cancer tissues.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.4
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• pp.463-467
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• 2006

Genetic profiles of Iranian Holstein young bulls at the national artificial insemination station were determined on the basis of individual genotypes at 13 ISAG's recommended microsatellites, the most useful markers of choice for parentage identification. In the present study a total of 119 individuals were genotyped at 13 microsatellite loci and for possible parent-offspring combinations. A high level of genetic variation was evident within the investigated individuals as assessed from various genetic diversity measures. The mean number of observed alleles per microsatellite marker was 9.15 and the number of effective alleles as usual was less than the observed values (4.03). The average observed and expected heterozygosity values were 0.612 and 0.898, respectively. The mean polymorphic information content (PIC) value (0.694) further reflected a high level of genetic variability. The average exclusion of probability (PE) of the 13 markers was 0.520, ranging from 0.389 to 0.788. The combined exclusion of probability was 0.999, when 13 microsatellite loci were used for analysis in the individual identification system. Inbreeding was calculated as the difference between observed and expected heterozygosity. Observed homozygosity was less than expected which reflects inbreeding of -3.7% indicating that there are genetic differences between bull-sires and bull-dams used to produce young bulls. The results obtained from this study demonstrate that the microsatellite DNA markers used in the present DNA typing are useful and sufficient for individual identification and parentage verification without accurate pedigree information.

• Journal of Animal Reproduction and Biotechnology
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• v.36 no.3
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• pp.154-161
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• 2021

A number of Korean Chicken breeds were registered in Domestic Animal Diversity Information System (DAD-IS, http://dad.fao.org/) of the Food and Agriculture Organization (FAO). Evaluation of genetic diversity and relationship of local breeds is an important factor towards the identification of unique and valuable genetic resources. Therefore, this study aimed to analysis the genetic diversity and relationship of 22 Korean Chicken breeds using 12 microsatellite (MS) markers. The mean number of alleles for each variety was 5.52, ranging from a 3.75 (Leghorn F; NF) to a 7.0 (Ross). The most diverse breed was the Hanhyup3 (HCC), which had the highest expected heterozygosity (H_Exp) (0.754) and polymorphic information content (PIC) (0.711). The NF was the least diverse population, having the lowest H_Exp (0.467) and PIC (0.413). As a result of the principal coordinates analysis (PCoA) and factorial correspondence analysis (FCA) confirmed that Hy-line Brown (HL) and Lohmann Brown (LO) are very close to each other and that Leghorn and Rhode Island Red (RIR) are clearly distinguished from other groups. Thus, the reliability and power of identification using 12 types of MS markers were improved, and the genetic diversity and probability of individual discrimination were confirmed through statistical analysis. This study is expected to be used as basic data for the identification of Korean chicken breeds, and our results indicated that these multiplex PCR marker sets will have considerable applications in population genetic structure analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1164-1171
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• 2012

Recently, machine olfactory systems as an artificial substitute of the human olfactory system are being studied actively because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. Even though dimensionality reduction is the very important part, in pattern analysis, We handled effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, we constructed the hierarchical rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1292-1298
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• 2017

In this paper, we represent a nonlinear identification of electronic brake pedal behavior in the brake-by-wire (BBW) system based on hybrid group method of data handling (GMDH) and genetic algorithm (GA). A GMDH is a kind of multi-layer network with a structure that is determined through training and which can express nonlinear dynamics as a mathematical model. The GA is used in the GMDH, enabling each neuron to search for its optimal set of connections with the preceding layer. The results obtained with this hybrid approach were compared with different nonlinear system identification methods. The experimental results showed that the hybrid approach performs better than the other methods in terms of root mean square error (RMSE) and correlation coefficients. The hybrid GMDH/GA approach was effective for modeling and predicting the brake pedal system under random braking conditions.

• Smart Structures and Systems
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• v.15 no.1
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.