• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.67-79
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• 1997

In this paper we develop the integrated software that can compare algorithms of the minimum cost flow problem using PC. The chosen algorithms are the network simplex method, dual network simplex method, and out-of-kilter method, which methods correspond to primal, dual, and primal-dual approach respectively. We also present the improved methods obtaining the initial solution to increase the efficiency of algorithms, and experiment results shown the difference between the entering(dropping) selection rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5307-5327
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• 2016

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we propose a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN. First, we develop two different types of charging cycles: initialization cycles and renewable cycles and give relevant algorithms to construct these two cycles for each sensor node. We then formulate the optimization problem into an optimal construction algorithm and prove its correctness through theoretical analysis. Finally, we conduct extensive simulations to demonstrate the effectiveness of our proposed algorithms.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.63-65
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• 2006

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.363-384
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• 2011

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.66-69
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• 1998

Comparative genomic hybridization (CGH) is an important molecular cytogenetics technique that maps abnormal copy number of specific DNA sequence of the chromosome. CGH is based on quantitative digital image analysis of ratio images from fluorescently labeled chromosomes. In this paper, we would like to introduce how recently developed image analysis algorithms are used for CGH techniques. To average the ratio profile of each chromosome, binarization, skeletonization, and stretching of chromosome images have been studied. Developed algorithms have been implemented in the karyotyping system ChIPS commercially developed at Biomedlab Co. Ltd.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.843-845
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• 2003

Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is the route determination service, where various kinds of shortest path problems need to be solved in location-based service. Our research aim is to propose a route technique in real-time locationbased service (LBS) environments according to user’s route preferences such as shortest, fastest, easiest and so on. Turn costs modeling and computation are important procedures in route planning. There are major two kinds of cost parameters in route planning. One is static cost parameter which can be pre-computed such as distance and number of traffic-lane. The other is dynamic cost parameter which can be computed in run-time such as number of turns and risk of congestion. In this paper, we propose a new cost modeling method for turn costs which are traditionally attached to edges in a graph. Our proposed route determination technique also has an advantage that can provide service interoperability by implementing XML web service for the OpenLS route determination service specification. In addition to, describing the details of our shortest path algorithms, we present a location-based service system by using proposed routing algorithms.

• Journal of Software Engineering Society
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• v.25 no.3
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• pp.59-62
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• 2012

We propose a novel metric for quantitatively comparing different clustered results generated from software clustering algorithms. A quantitative evaluation of software clustering helps understanding of architectural changes of software. The concept of split, which has been used for analysis of genetic characters in bio-informatics, is applied in the analysis of software architecture.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.17-21
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• 2001

Theories for optimal design and optimization algorithms have long been well developed. In industries, however, they are not well practiced. To make them work for industry, a new philosophy is necessary and an integration of various software systems required. A review of the history in the aspect of optimal design software is made and a newly developed code DS-Structure is introduced.

• The Journal of Korean Association of Computer Education
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• v.18 no.5
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• pp.45-54
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• 2015

There has been rising demand within the South Korean government and among various industries in recent years on the need for strengthening the competitiveness of the software industry, which they highlight as being a core element for national competitiveness. This strategy would require enhancing the job competency of software developers. This study's results show that computational thinking (CT), which is one of the more creative solutions to the problem, has a positive effect on the enhancement of software developers' job competency. Furthermore, the study finds that the software development tasks of simulation, algorithms and procedures, and parallelization (in that order) serve as influential elements for software developers, which differs from previous studies' findings. The South Korean government thus should emphasize education in these areas in order to enhance the nation's job competence. Because simulation, especially, has the greatest influence among these areas, there is a particular need to strengthen that area.