• Journal of Korea Technology Innovation Society
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• v.6 no.2
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• pp.213-227
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• 2003

초고속 인터넷 서비스는 사용자수의 증가와 더불어 고객의 다양한 욕구 즉 인터넷 방송, 주문형비디오(VOD)서비스, 원격교육, 고화질 TV 등 대용량의 멀티미디어 서비스에 대한 욕구가 폭발적으로 증가하고 있다. 이러한 욕구를 충족하기 위해서는 현재의 초고속 인터넷서비스로서는 속도에 대한 한계에 부딪치게 되어 통신사업자들은 새로운 기술 또는 여러 가지 기술적 대안들을 추구하고 있다. 2002년부터 시작하여 2003년 이후에는 멀티미디어 수요의 증가에 따라 ADSL을 대체하는 기술로 VDSL이 등장하여 매년 꾸준한 신규가입자 수요가 발생하고 있으나, 통신사업자들은 각각의 망 특성, 시장위치, 전략적 필요성 둥에 의해 상용화를 적극 검토,추진하고 있으나 각각 전개하는 방식은 조금씩 다르다. 따라서 본 연구에서는 통신사업자들의 가입자망 진화 전략에 대해 살펴 본 다음 Lot3n-Volterra Competition (LVC) 모델을 이용 ADSL 과 VDSL 두 기술간의 상호 경쟁 및 대체를 통해 어떻게 진화 되어가는지를 살펴보았다. 대표적인 통신사업자인 KT는 막강한 자금력을 바탕으로 시장 확대 및 경쟁사와의 차별화를 위해 VDSL 서비스 조기도입을 서두르고 있고, 하나로는 자금의 열세로 인한 ADSL 투자비를 회수 할때까지 VDSL 서비스를 연기하고 있는 실정이다. ADSL과 VDSL 두 기술의 관계는 Lotka-Volterra Competition (LVC) 모델을 이용한 시뮬레이션 결과를 통해 빠른 속도와 비슷한 가격대의 VDSL이 침략자(predator)로 기존 시장 지배자인 ADSL을 사냥감(prey)으로 빠른 속도로 대체해 나가는 것을 알 수 있었다.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.22-22
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• 2017

소셜벤처는 기존의 일반적인 벤처기업과 달리 기술, 경영 혁신 등의 방법으로 경영 활동을 수행하는 기업 형태에 사회적 가치를 추구해야 하는 기업 형태를 결합한 조직이다. 소셜벤처에게는 창업 초기의 전략 수립과 이에 따른 비즈니스 모델 설정이 일반 벤처보다 더 어려울 수밖에 없는데 이는 소셜벤처들이 경제적 가치 추구와 함께 사회적 가치를 동시에 추구해야 하는 소셜벤처만의 이중적인 사업 목적을 가지고 있기 때문이다. 이런 이중적인 사업 목적을 달성하기 위한 새로운 또는 효과적인 사업 전략의 필요성이 대두되고 있으며, 관련 그룹들을 플랫폼에 모아 네트워크 효과를 창출하고 새로운 기업생태계를 구축하는 플랫폼 전략(Platform Strategy)이 주목된다. 본 논문에서는 국내 소셜벤처중에 점프!라는 기업의 사례를 주목하게 되는데 이 회사는 2011년 5월에 창업한 이래 현재까지 지난 7년동안 사업의 내용과 규모를 확장하면서 지속적인 성장을 이루어 냈기 때문이다. 그런데 이러한 성장의 배경에는 타 소셜벤처가 시도하지 않았던 플랫폼 전략을 시도하여, 다양한 참여자들을 유인하고, 이들이 새로운 혁신과 성장을 리드하였다는 것이 큰 작용을 했다는 사실이 존재한다. 본 논문을 통해 소개되는 소셜벤처 점프!의 창업과 성장 사례를 통해서 소셜벤처에게 창업 초기부터 플랫폼 전략을 적용하는 것이 지속 가능한 성장에 유의미한 효과를 발생시키는지를 알아보고 기업생태계를 넘어 사회전체적인 생태계를 통한 사회적 대공진화를 논의해 보고자 한다.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.24-34
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• 2008

The study aims to anticipate evolution direction of mobile content business in the future, and derive successful strategies at the same time with multiple and composite analysis of evolution process and environmental change of mobile business. In conclusion, Firstly, it should strengthen customer approach performance, secondly, approaches with sensitively, and needs opened business model. Fourthly, it needs planning and production of contents which reflects the need of customers, and fifthly, it requires development of mobile platform leading contents group and multiplication of CP business model. In the aspect of industrial policy, sustainable and future-oriented policy should be arranged rather than short-term and simple police, for the continual development of mobile content industry. To establish healthy and active mobile contents industry, the government and regulatory institutes are more preferable to devote to the role of circumstance builder rather than the leader of mobile content market. In other words, they are recommended to focus on establishing basic regulation and order for smooth operation of the market and its management.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.253-288
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• 2010

Production innovation capabilities are critical to the survival and growth of firms. This paper investigates industrial dynamics and dynamic capabilities of firms by looking at how an industry evolution process influences firms' product innovation strategy and how dynamic capabilities affect firms' product innovation process. Korea Wireless Internet industry shows a full cycle of industry evolution process including introduction phase, growth phase, maturity phase, and decline phase using by dynamic technological and market changes. 7 listed companies in Korea Wireless Internet industry were selected. We have conducted multiple case studies based upon in depth interviews. Empirical results show that different phases of industry evolution influence firms' strategy of product innovation. Dynamic capabilities are also appears to be very important to the survival and growth of a firm.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.509-526
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• 2008

This paper aims to examine the evolutionary dynamics of the Korea's footwear industry by adopting evolutionary perspectives. To explain the evolutionary dynamics of an industry, evolutionary perspectives have paid a particular attention to exploring a variety of factors for influencing the evolution of the industry, such as the selection and imitation of the firm, the mechanism of firm's entry and exit, technological characteristics and innovation processes. The majority of existing research tend to explain that the decline of the Korea's footwear industry since 1990 was mostly due to the rapid rising of wage and the structural changes in labor-intensive industries. On the contrary, this paper attempts to explain the decline of the Korea's footwear industry, in terms of the path of selection and imitation, the dominant technological paradigm, regulatory frameworks and the meso trajectory of industry evolution. This paper concludes that the decline of the Korea's footwear industry since 1990 was appeared as a result of the evolutionary selection processes of the firms in order to adapt to changes in the environment of competition and the regime of market selection in the global footwear industry.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.257-265
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• 2000

There is much selective confliction in nature where selfish and rational individuals exists. Iterated Prisoner's Dilemma (IPD) game deals with this problem, and has been used to study on the evolution of cooperation in social, economic and biological systems. So far, there has been much work about the relationship of the number of players and cooperation, strategy learning as a machine learning and the effect of payoff functions to cooperation. In this paper, We attempt to investigate the cooperative coalition size according to payoff functions, and observe the relationship of localization and the evolution of cooperation in NIPD (N-player IPD) game. Experimental results indicate that cooperative coalition size increases as the gradient of the payoff function for cooperation becomes steeper than that of defector's payoff function, or as the minimum coalition size gets smaller, Moreover, the smaller the neighborhood of interaction is, the higher the cooperative coalition emerges through the evolution of population.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method which perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.13-30
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• 2008

The objective of this study is to explore a knowledge sharing typology for Internet portals based on knowledge-based view of firm. Furthermore, we provide insights into how the evolution of Internet portals takes place by describing user behavior of knowledge sharing. For doing this, we first present a typology of knowledge sharing based on the two dimensions such as knowledge donation and knowledge collection. Then we conduct case study of the Korean major portals to demonstrate a proposed typology. The main finding of the analysis is that three distinctive types of knowledge sharing patterns within portals are distinguished: collaboration, accumulation, and publishing. We conclude that user behavior of knowledge sharing is characterized as guiding factors in evolution process.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.