• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.326-328
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• 1998

This paper presents an optimal fuzzy power system stabilizer to damp out low frequency oscillation. The fuzzy logic controllers has been applied to a power system stabilizing controllers. But the design of a fuzzy logic power system stabilizer relies on empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This paper presents the optimal design method of the fuzzy logic stabilizer using the genetic algorithm, which is the optimization method based on the mechanics of natural selection and natural genetics. The proposed method tunes the parameters of the fuzzy logic stabilizer in order to minimize the consuming time during the design process. In this paper, the proposed method tunes the shape of membership function of the fuzzy variables. The proposed system is applied to the one-machine infinite-bus model of a power system. Through the case study, the efficiency of the fuzzy stabilizing controller tuned by genetic algorithm is verified.

• Journal of the military operations research society of Korea
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• v.26 no.1
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• pp.56-69
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• 2000

A frequency assignment problem on time division duplex system is considered. Republic of Korea Army (ROKA) has been establishing an infrastructure of tactical communication (SPIDER) system for next generation and it will be a core network structure of system. VHF system is the backbone network of SPIDER, that performs transmission of data such as voice, text and images. So, it is a significant problem finding the frequency assignment with no interference under very restricted resource environment. With a given arbitrary configuration of communications network, we find a feasible solution that guarantees communication without interference between sites and relay stations. We formulate a frequency assignment problem as an Integer Programming model, which has NP-hard complexity. To find the assignment results within a reasonable time, we take a genetic algorithm approach which represents the solution structure with available frequency order, and develop a genetic operation strategies. Computational result shows that the network configuration of SPIDER can be solved efficiently within a very short time.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.231-238
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• 2014

In the buildings, the systems of structures are influenced by the gravity load changes due to room alteration or construction stage. This paper proposes a system identification method establishing mass as well as stiffness to parameters in model updating process considering mass change in the buildings. In this proposed method, modified genetic algorithm, which is optimization technique, is applied to search those parameters while minimizing the difference of dynamic characteristics between measurement and FE model. To search more global solution, the proposed modified genetic algorithm searches in the wider search space. It is verified that the proposed method identifies the system of structure appropriately through the analytical study on a steel beam structure in the building. The comparison for performance of modified genetic algorithm and existing simple genetic algorithm is carried out. Furthermore, the existing model updating method neglecting mass change is performed to compare with the proposed method.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.577-586
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• 2001

The objective of this study is the development of sizing and system shape discrete optime design algorithm which is based on the genetic algorithms (GAs). The algorithm can perform both size and shape optimum designs of space trusses. The developed algorithm was implemented in a computer program. The algorithm is known to be very efficient for the discrete optimization The genetic process selects the next design points based on the survivability of the current design points The evolutionary process evaluates the survivability of the design points selected from the genetic process in the genetic process of the simple genetic algorithms there are three basic operators : reproduction cross-over and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.119-130
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• 2014

This paper proposes GAPS, a practical genetic algorithm based pathfinding system for conveniently analyzing various networks. To this end, the GAPS is developed through integration of the intuitive graphic user interface for network modeling, the database management system for managing the data generated in modeling and exploring procedures, and a simple genetic algorithm for analyzing a wide range of networks. Especially, previous genetic algorithms are not appropriate for analyzing the networks with many dead-ends where there are few feasible paths between the given two nodes, however, GAPS is based on the genetic algorithm with the fitness function appropriate for evaluating both feasible and infeasible paths, which enables GAPS to analyze a wide range of networks while maintaining the diversity of the population. The experiment results reveal that GAPS can be used to analyze both networks with many dead-ends and networks with few dead-ends conveniently, and GAPS has several advantages over the previous genetic algorithms for pathfinding problems.

• Proceedings of the Botanical Society of Korea Conference
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• 1998.07a
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• pp.10-18
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• 1998

For improvement of plants in disease resistance, it is most important to elucidate the mechanism to perceive and respond to the signal molecules of invaders. A model system with pea and its pathogen, Mycosphaerella pinodes, showed that the fungal elicitor induced defense responses in all plant species tested but that the suppressor of the fungus blocked or delayed the expression of defense responses and induced accessibility only in the host plant. In the world, many researchers believe that the pathogens` signals are recognized only on the receptors in the plasma membranes. Though we found that the ATPase and polyphosphoinositide metabolism in isolated plasma membranes responded to these fungal signals, we failed to detect specific actions of the suppressor in vitro on these plasma membrane functions. Recently, we found that ATPase (NTPases) and superoxide generating system in isolated cell wall were regulated by these fungal signals even in vitro, especially, by the suppressor in a strictly species-specific manner and also that the cell wall alone prepared an original defense system. The effects of both fungal signals on the isolated cell wall functions in vitro coincide perfectly with those on defense responses in vivo. In this treatise, we discuss the key role of the cell wall, which is plant-specific and the most exterior organelle, in determining host-parasite specificity and molecular target for improvement of plants.

• Genomics & Informatics
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• v.16 no.4
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• pp.31.1-31.7
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• 2018

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.480-482
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• 2000

The KEPCO is developing the practical power distribution operating system. The system adopt Genetic Algorithm and will be used loss reduction, load balancing, service planning for large capacity load and various kinds of simulations in the distribution power system. This paper presents the some obstacles and solutions on practical simulation system development, and some problems that need more study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.