• Development and Reproduction
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• v.24 no.4
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• pp.317-325
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• 2020

The Far Eastern catfish (Silurus asotus) is an important commercial freshwater fish in Korea. Investigation of the genetic diversity of wild and cultured domestic catfish groups is essential for the restoration of fishery resources and for increasing local revenue. However, there are relatively few genetic diversity studies on wild and cultured catfish in Korea. In the present study, we analyzed the genetic diversity and association of wild and cultured catfish using five microsatellite markers. We determined that the number of alleles per locus (N_A) ranged from 9 to 25, wherein the Jeonbuk catfish demonstrated the highest mean number of alleles per locus and the cultured catfish exhibited the lowest. The average expected heterozygosity (He) of the wild catfish samples was 0.907, and that of the cultured catfish showed was 0.875. The genetic distances (GD value) among populations of all catfish ranged from 0.138 to 0.242. Jeonnam and Jeonbuk wild catfish were located closest to each other, and the cultured group was separated from the other groups. In conclusion, the present study confirmed that the genetic diversity of wild and cultured catfish was maintained at a high level. In the case of the wild group, it is effective in maintaining diversity due to the continuous fry release by the local fish research institute. However, the genetic diversity of cultured catfish declined. Low diversity is associated with slow growth and weakened immunity, and therefore continuous monitoring is necessary.

• IE interfaces
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• v.25 no.3
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.837-839
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• 2003

Low spatial resolution remote sensing (RS) data (LSRD) are promising in agricultural monitoring activities due to their high temporal resolution, but under such a spatial resolution, mixing in a pixel is a common problem. In this study, a numerical experiment was conducted to explore a mixed pixel problem in agriculture using a combined RSsimulation model SWAP (Soil-Water-Atmosphere -Plant) and a Genetic Algorithm (GA) approach. Results of the experiments showed that it is highly possible to address the mixed pixel problem with LSRD.

• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.445-461
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• 2018

The aim of this research is to evaluate the effects of the number of spans, height of spans, number of floors, height of floors, column to beam moment of inertia ratio, and plastic joints distance of beams from columns on the ductility of moment frames. For the facility in controlling the ductility of the frames, this paper offers a simple relation instead of complex equations of different codes. For this purpose, 500 analyzed and designed frames were randomly selected, and their ductility was calculated by the use of nonlinear static analysis. The results cleared that the column-to-beam moment of inertia ratio had the highest effect on ductility, and if this relation was more than 2.8, there would be no need for using the complex relations of codes for controlling the ductility of frames. Finally, the ductility of the most frames of this research could be estimated by using the combination of genetic algorithm and artificial neural networks properly.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Fisheries and Aquatic Sciences
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• v.11 no.2
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• pp.82-87
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• 2008

Marine fisheries are important natural resources and must be maintained, especially fish species that are important sources of food. Despite the increase in stocking programs to maintain fisheries with artificially raised fish, the genetic impact stocking has on the wild fry population has not been addressed. Genetic variation in rock bream, Oplegnathus fasciatus, within and between wild-caught parents and the $F_1$ generation produced by them in 1 day was assayed using nine highly variable micro satellite markers. The nine micro satellite loci used in this study displayed diverse polymorphisms, and in total, 98 different alleles were observed over all loci. Differences in genetic variability of the $F_1$ offspring compared to their wild-caught parents (brood stock) were observed in terms of allele frequency, gene diversity, and heterozygosity. Although the $F_1$ generation of rock bream was missing 16% of the micro satellite alleles, no significant reduction was found in mean heterozygosity of the $F_1$ population compared to the brood stock. Eight of nine loci showed significant Hardy-Weinberg equilibrium (HWE) deviations in the $F_1$ population, while the brood stock deviated from HWE at three micro satellite loci (KOF85, KOF360 and KOF374). These deviations showed mostly a deficit of heterozygotes. Our results provide evidence for genetic differences in the $F_1$ hatchery offspring compared to their wild-caught parents and reinforce the need for a series of consecutive egg collections to avoid the loss of genetic variability. This also further underscores the importance of monitoring genetic variability of hatchery populations for the conservation of natural rock bream resources.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.6
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• pp.414-420
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• 2015

Concrete undergoes significant phase changes from liquid to solid states as hydration progresses. These phase changes are known as the setting process. A liquid state concrete is electrically conductive because of the presence of water and ions. However, since the conductive elements in the liquid state of concrete are consumed to produce non-conductive hydration products, the electrical conductivity of hydrating concrete decreases during the setting process. Therefore, the electrical properties of hydrating concrete can be used to monitor the setting process of concrete. In this study, a parameter identification method to estimate electrical parameters such as ohmic resistance of concrete is proposed. The effectiveness of the proposed method for monitoring the setting process of concrete is experimentally validated.

• Earthquakes and Structures
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• v.25 no.1
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• pp.69-78
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• 2023

Life-cycle performance analysis of a reinforced concrete box section bridge was generated. Moreover, Monte Carlo simulation with important sampling (IS) was used to simulate the bridge material and load uncertainties. The bridge deterioration model was generated with the basic probabilistic principles and updated according to the measurement data. A genetic algorithm (GA) with the response surface model (RSM) was used to determine the deterioration rate. The importance of health monitoring systems to sustain the bridge to give services economically and reliably and the advantages of fiber-optic sensors for SHM applications were discussed in detail. This study showed that the most effective loss of strength in reinforced concrete box section bridges is corrosion of the reinforcements. Due to reinforcement corrosion, the use of the bridge, which was examined, could not meet the desired strength performance in 25 years, and the need for reinforcement. In addition, it has been determined that long-term health monitoring systems are an essential approach for bridges to provide safe and economical service. Moreover the use of fiber optic sensors has many advantages because of the ability of the sensors to be resistant to environmental conditions and to make sensitive measurements.