• Journal of Software Engineering Society
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• v.26 no.4
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• pp.93-103
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• 2013

Complexity and maintenance cost of software increase as much as software has been evolved, therefore importance of software maintenance recently arise. There are many signs that are difficulties to maintain software, called bad smell, in a large-scale software. The bad smell should be removed to improve maintainability. Recently, many software refactoring methods have researched to terminate the bad smell. In this paper, we propose how to identify long parameter list, which causes bad smell, and how to solve the problem for increasing software maintainability. In our approach, we classify the parameters for creating new objects by measuring semantic similarity among them. This is evaluated by experienced software developers, and the result is statistically verified.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.268-276
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• 2005

This paper deals with optimum communication spanning tree(OCST) problem. Generally, OCST problem is known as NP-hard problem and recently, it is reveled as MAX SNP hard by Papadimitriou and Yannakakis. Nevertheless, many researchers have used polynomial approximation algorithm for solving this problem. This paper uses evolutionary algorithm. Especially, when an evolutionary algorithm is applied to tree network problem such as the OCST problem, representation and genetic operator should be considered simultaneously because they affect greatly the performance of algorithm. So, we introduce a new representation method to improve the weakness of previous representation which is proposed for solving the degree constrained minimum spanning tree problem. And we also propose a new decoding method to generate a reliable tree using the proposed representation. And then, for finding a suitable genetic operator which works well on the proposed representation, we tested three kinds of genetic operators using the information of network or the genetic information of parents. Consequently, we could confirm that the proposed method gives better results than the previous methods.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.247-257
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• 2005

Generally, the network topology design problem is characterized as a kind of NP-hard combinatorial optimization problem, which is difficult to solve with the classical method because it has exponentially increasing complexity with the augmented network size. In this paper, we propose the efficient approach with two phase that is comprised of evolutionary computation approach based on Prufer number(PN), which can efficiently represent the spanning tree, and a heuristic method considering 2-connectivity, to solve the highly reliable network topology design problem minimizing the construction cost subject to network reliability: firstly, to find the spanning tree, genetic algorithm that is the most widely known type of evolutionary computation approach, is used; secondly, a heuristic method is employed, in order to search the optimal network topology based on the spanning tree obtained in the first Phase, considering 2-connectivity. Lastly, the performance of our approach is provided from the results of numerical examples.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.501-511
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• 2008

Probe design is one of the essential tasks in successful DNA microarray experiments. The requirements for probes vary as the purpose or type of microarray experiments. In general, most previous works use the simple filtering approach with the fixed threshold value for each requirement. Here, we formulate the probe design as a multiobjective optimization problem with the two objectives and solve it using ${\epsilon}$-multiobjective evolutionary algorithm. The suggested approach was applied in designing probes for 19 types of Human Papillomavirus and 52 genes in Arabidopsis Calmodulin multigene family and successfully produced more target specific probes compared to well known probe design tools such as OligoArray and OligoWiz.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.694-705
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• 2010

The mutation operation is the main operation in the evolutionary programming which has been widely used for the optimization of real valued function. In general, the mutation operation utilizes both a probability distribution and its parameter to change values of variables, and the parameter itself is subject to its own mutation operation which requires other parameters. However, since the optimal values of the parameters entirely depend on a given problem, it is rather hard to find an optimal combination of values of parameters when there are many parameters in a problem. To solve this shortcoming at least partly, if not entirely, in this paper, we propose a new mutation operation in which the parameter for the variable mutation is theoretically estimated from the self-adaptive perspective. Since the proposed algorithm estimates the scale parameter of the Cauchy probability distribution for the mutation operation, it has an advantage in that it does not require another mutation operation for the scale parameter. The proposed algorithm was tested against the benchmarking problems. It turned out that, although the relative superiority of the proposed algorithm from the optimal value perspective depended on benchmarking problems, the proposed algorithm outperformed for all benchmarking problems from the perspective of the computational time.

• Journal of Technology Innovation
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• v.31 no.2
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• pp.29-58
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• 2023

The knowledge and technological competitiveness produced by universities determines a country's scientific technology and industrial competitiveness. The aim of this study is to find the evolution of Triple Helix Model (THM) by analyzing cases of technological catching-up and nurturing the human capital policies during Korea industrialization era, from 1960 to 2020. To fulfill the aims, this study utilized qualitative methodologies. In this study not only the process of acquiring technologies and core technologies but also the system for nurturing the human capital focused on scientific and engineers was analyzed. In addition characteristics of each ear of Triple Helix Model (THM) were examined. From the results, this study suggests Triple Helix Syncro-Scenario (THSS) which is the compelling innovation model, based on scenario, combined the personal experiences various critical thoughts and problems with enterprises and government for creating the incessment innovations.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.19-28
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• 2011

In today and near future, most of the commercially manufactured IT products will be evolved into software convergence product. Recently, the embedded software products is called as 'Software Convergence Products.' This phenomenon does not simply show the trendy fashion, but has the seriously implication that the functionalities of IT product is accomplished and evolved via software technology, not via mechanical nor electrical means. It will become true that the quality of the convergence product is dominantly governed by the software it uses. Meanwhile, we are facing with the threatening fact that software defects in the mass products will requires tremendous amounts of cost proportional to the quantity of the product. We can remind ourselves of the disasters that have been already happened, such as Automotive recalls, Smart-phone recalls, and others. In software engineering, there have been large amount of work done in software quality improvement for the past couple of decades. Software process improvement, and testings are the representative ones. But we are facing with limitations of those traditional approaches in current convergence industry; exponentially increasing software sizes and rapid changes in software technology. In this paper, we analyze the characteristics of the software convergence industry, the limitations of the traditional Software quality improvement approaches. We suggest a new approaches in software quality improvement in different angles of thought and philosophy.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.357-360
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• 2005

유전자 알고리즘(Genetic Algorithm, GA)은 자연적 진화과정에서 생존 경쟁 측면의 가장 적합한 메커니즘이다. GA를 소프트웨어로 수행하는데 큰 지연시간은 필수적이기 때문에 하드웨어 설계를 이용하여 알고리즘 실행 속도를 증가시키기 위한 많은 연구가 진행되어 왔다. 본 논문에서는 염색체의 임의의 유전인자를 기준으로 입력 받은 염색체에 대하여 GA 연산을 수행하는 유전 연산자를 설계한다. 설계된 디자인을 ARM 코어와 PLD로 구성된 Altera사의 Excalibur칩에 구현하여 동작을 검증하였다.

• Digital Contents
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• no.3 s.106
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• pp.58-63
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• 2002

레포지터리의 개념은 예전부터 존재했는데 투자 효과적 측면과 기술적 측면에 의해 본격적으로 도입되는 예는 적다. 그러나 작년부터 인터넷을 이용한 기업 간을 연결하는 시스템 및 데이터웨어 하우스에 있어서 메타데이터 관리가 중요시되고 있다. 기술적으로도 하드웨어 기반의 진화 및 소프트웨어 표준화에 의해 실제로 이용할 수 있는 기반도 정비되고 있다. 본고에서는 메타데이터를 관리하는 레포지터리의 기능을 설명하고 그에 대응한 최근의 표준화 동향에 대해서 OMG(Object Management Group)를 중심으로 해서 보고 하겠다.

• Information and Communications Magazine
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• v.31 no.6
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• pp.12-17
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• 2014

네트워크 기술과 응용 서비스 및 단말 장비의 발전으로 인터넷 사용 환경이 다양한 모습으로 진화하고, 이로 인하여 다시 네트워크 혁신이 요구되는 순환이 이루어지고 있다. 네트워크 혁신 기술의 주요 흐름의 하나인 소프트웨어 정의 네트워킹(SDN)은 네트워크 인프라 기술을 개방하여 새로운 응용 서비스를 유연하게 지원할 수 있도록 하며, 산업계에서의 역할별 역학관계를 새롭게 형성하고 있다. 본 고는 SDN을 중심으로 네트워크 혁신을 위한 사업자 관점의 요구 사항, 기술 표준화와 개발 동향 및 향후 발전 방향을 고찰한다.