• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.12
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• pp.1163-1170
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• 2008

A mission scheduling optimization algorithm according to the purpose of satellite operations is developed using genetic algorithm. Satellite mission scheduling is making a timetable of missions which are slated to be performed. It is essential to make an optimized timetable considering related conditions and parameters for effective mission performance. Thus, as important criterions and parameters related to scheduling vary with the purpose of satellite operation, those factors should be fully considered and reflected when the satellite mission scheduling algorithm is developed. The developed algorithm in this study is implemented and verified through a comprehensive simulation study. As a result, it is shown that the algorithm can be applied into various type of the satellite mission operations.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1244-1251
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• 2015

A herd of Berkshire pigs was established in 2003 and subjected to selection without introduction of any genetic resources until 2007. The complete pedigree, including 410 boars and 916 sows, as well as the records from 5,845 pigs and 822 litters were used to investigate the results obtained from the selections. The index of selection for breeding values included days to 90 kg (D90kg), backfat thickness (BF) and number of piglets born alive (NBA). The average inbreeding coefficients of pigs were found to be 0.023, 0.008, 0.013, 0.025, 0.026, and 0.005 from 2003 to 2007, respectively. The genetic gains per year were 12.1 g, -0.04 mm, -3.13 days, and 0.181 head for average daily gain (ADG), BF, D90kg, and NBA, respectively. Breeding values of ADG, BF and D90kg were not significantly correlated with inbreeding coefficients of individuals, except for NBA (-0.21). The response per additional 1% of inbreeding was 0.0278 head reduction in NBA. The annual increase of inbreeding was 0.23% and the annual decrease in NBA due to inbreeding was 0.0064 head. This magnitude could be disregarded when compared with the annual gain in NBA (0.181 head). These results suggest that inbreeding and inbreeding depression on ordinary farms can be controlled with a proper breeding scheme and that breeding programs are economical and safe relative to the risks associated with importation of pigs.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.878-886
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• 2009

This paper proposes an optimal algorithm for generating a train speed profile giving optimal energy efficiency based on GA (Genetic Algorithm) and shows its effectiveness with simulations. After simplifying the train operation mode to a maximum traction, a coasting and a maximum breaking, adjusting the coasting point to minimize the train consuming energy is the basic scheme. Satisfying the two constraints, running distance and running time between two stations, a coasting point is determined by GA with a fitness function consisting of a target running time. Simulation results have shown that multiple coasting points could exist satisfying both of the two constraints. After figuring out consumed energies according to the multiple coasting points, an optimal train speed profile with a coasting point giving the smallest consumed energy has been selected. Simulation blocks for the train performance simulation and GA have been designed with the Simulink.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.338-343
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• 2018

Recently, a computer numerical control (CNC) machine is used widely for mold making in various industries. In the operation of a CNC machine, the production quality and safety of workers are becoming increasingly important as the product process increases. A variety of tool life prediction studies has been conducted to standardize the quality of production and improve reproducibility. When the tool life is predicted using the conventional tool life equation, there is a large error between the experimental result and result by the conventional tool life equation. In this paper, an algorithm that can predict the precise tool life was implemented using a genetic algorithm.

• Information Systems Review
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• v.22 no.2
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• pp.201-217
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• 2020

Information security is an essential element not only to ensure the operation of the company and trust with customers but also to mitigate uncertain damage by preventing information data breach. Therefore, It is important to select appropriate information security countermeasures and determine the appropriate level of investment. This study presents a decision support model for the appropriate investment amount for each countermeasure as well as an optimal portfolio of information countermeasures within a limited budget. We analyze statistics on the types of information security breach by industry and derive an optimal portfolio of information security countermeasures by using genetic algorithms. The results of this study suggest guidelines for investing in information security countermeasures in various industries and help to support objective information security investment decisions.

• Reproductive and Developmental Biology
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• v.39 no.3
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• pp.83-88
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• 2015

The present study was devised to determine the effects of vitamin E and selenium (Selevit) on body weight, organ weight, hematological values and biochemical parameters in the orchidectomized (Orch) rats. Intact group (n=15) received no treatment and operation. Orch+Selevit received operation and Selevit. The body weights of each group increased, but that of the Orch＋Selevit group were significantly lower than those of all the other groups. There were significantly different decreased (p<0.001) of body weights between Orch＋Selevit group and all the other groups. Also, organ weights such as heart, liver, spleen, kidney, lung and skeletal muscle were measured. The heart and liver weights in the Orch＋Selevit group were significantly different decreased (p<0.001) in comparison with those in the Intact and Sham groups. The kidney weights in the Orch＋Selevit group were significantly different decreased (p<0.01, p<0.001) in comparison with those in all the other groups. The number of white blood cell (WBC) was significantly higher (p<0.05) in the Orch＋Selevit group than in all the other groups. The hematological values of 12 parameters were not significantly different in any of the groups. The concentrations of serum total protein, albumin and alkaline phosphatase only increased significantly (p<0.05, p<0.01) in the Orch＋Selevit group as compared to that in the Orch group. We conclude that Selevit was significantly decreased the body weight in the Orch rats. Our findings suggest that Selevit may influence the process of lipid packaging and absorption in the Orch rats.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.658-665
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• 2019

Among the renewable energy resources, wind power is growing rapidly in terms of technological development and market share. Recently, onshore wind farm have been affected by limitations of terrestrial space and environmental problems. Consequently, installation sites have been moved to the sea, and the development of floating offshore wind farms that are installed at deep waters with more abundant wind conditions is actively underway. In the context of maritime traffic, the optimal site of offshore wind farms is required to minimize the interference between ships and wind turbines and to reduce the probability of accidents. In this study, genetic algorithm based AIS(Automatic Indentification System) data composed of genes and chromosomes has been used. The optimal site of floating offshore wind farm was selected by using 80 genes and by evaluating the fitness of genetic algorithm. Further, the final site was selected by aggregating the seasonal optimal site. During analysis, 11 optimal site were found, and it was verified that the final site selected usng the genetic algorithm was viable from the perspective of maritime traffic.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.413-424
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• 2009

Wireless sensor network is based on an event driven system. Sensor nodes collect the events in surrounding environment and the sensing data are relayed into a sink node. In particular, when events are detected, the data sensing periods are likely to be shorter to get the more correct information. However, this operation causes the traffic congestion on the sensor nodes located in a routing path. Since the traffic congestion generates the data queue overflows in sensor nodes, the important information about events could be missed. In addition, since the battery energy of sensor nodes exhausts quickly for treating the traffic congestion, the entire lifetime of wireless sensor networks would be abbreviated. In this paper, a new congestion control method is proposed on the basis of genetic algorithm. To apply genetic algorithm, the data traffic rate of each sensor node is utilized as a chromosome structure. The fitness function of genetic algorithm is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets, the proposed method selects the optimal data forwarding sensor nodes for relieving the traffic congestion. In experiments, when compared with other methods to handle the traffic congestion, the proposed method shows the efficient data transmissions due to much less queue overflows and supports the fair data transmission between all sensor nodes as possible. This result not only enhances the reliability of data transmission but also distributes the energy consumptions across the network. It contributes directly to the extension of total lifetime of wireless sensor networks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1511-1519
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• 2013

This paper proposes the allocation method for capacitor-reactor banks in a distribution system with dispersed generators to reduce the installation costs, the maintenance costs and minimize the loss of electrical energy. The expected lifetime and maintenance period of devices with moving parts depends on the total number of operations, which affects the replacement and maintenance period for aging equipment under a limited budget. In this paper, the expected device lifetimes and the maintenance period are included in the formulation, and the optimal operation status of the devices is determined using a genetic algorithm. The optimal numbers and locations for capacitor-reactor banks are determined based on the optimal operation status. Simulation results in a 69-bus distribution system with the dispersed generator show that the proposed technique performs better than conventional methods.