• Genomics & Informatics
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• v.1 no.1
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• pp.7-19
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• 2003

The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.271-274
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• 1997

This paper presents the optimization technique for design of a CMAC network by using an evolution strategies(ES). The proposed technique is designed to find the optimal parameters of a CMAC network, which can minimize the learning error between the desired output and the CMAC network's as well as the number of memory used in the CMAC network. Computer simulations demonstrate the effectiveness of the proposed design method.

• Genomics & Informatics
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• v.2 no.4
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• pp.167-173
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• 2004

The main mechanism of evolution is that biological entities change, are selected, and reproduce. We propose a different concept in terms of the main agent or atom of evolution: in the biological world, not an individual object, but its interactive network is the fundamental unit of evolution. The interaction network is composed of interaction pairs of information objects that have order information. This indicates a paradigm shift from 3D biological objects to an abstract network of information entities as the primary agent of evolution. It forces us to change our views about how organisms evolve and therefore the methods we use to analyze evolution.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1585-1588
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• 1997

In this paper, divide total workspace of robot manipulator into several subspaces and construct PID controller ineach subspace. Using EvolutionSTrategy we optimize the gains of PID controller in each subspace. But the gains may have a large difference on the boundary of subspaces, which can cause bad oscillatory performance. So we use Aritificial Neural Network to have continuous gain curves htrough the entire subspaces. Simualtion results show that the proposed method is quite useful.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.239-244
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• 1998

The environment of the access network service market is characterized by uncertain demand and various competing alternative technologies. In Korea, despite the introduction of competition, dominant Public Network Operator(PNO) still leads the market. Therefore, the decision of PNO has a great impact on the access network evolution. In this paper, we propose an model which aims to reduce risks and both investment and operating costs, to cope with the uncertain demand and technology evolution. We expect this model to provide a tool analyze risks and evaluate various strategies on the network evolution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.62-69
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• 2012

This thesis proposes a new social network simulator, which can be used for the social network analysis (SNA). It is composed of three modules; initialization, network evolution, and output generation. For the network evolution module, we suggest a modified JGN (MJGN) based on JGN, the network evolution model developed by Jin, Girvan, and Newman. Arena, one of the most popular simulation tools, was used to model the agent based social network simulator. Lastly, some test results were presented to show the value of the proposed simulator when one performs SNA at the longitudinal point of view.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.223-226
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• 1997

Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.113-119
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• 2021

With the increasing pressure of international competition, urban agglomeration cooperation and innovation had become an important means of regional economic development. This study analyzed the spatial characteristics of the Urban Cooperative Innovation Network in Guangdong-Hong Kong-Macao Greater Bay Area, found out the dynamic evolution law of innovation, provided suggestions for policy management departments, and effectively planned the industrial layout. According to the data of the State Intellectual Property Office of China, this study researched invention patents from 2005 to 2019. This paper constructed the urban cooperative innovation network, and took 11 cities in the bay area as the research objects, and used social network analysis to study the spatial structure and dynamic evolution of the urban innovation network. Every indicator reflected the urban cooperative innovation, but they all showed a certain decline in 2008-2010. And it is inferred that the innovation network space of each city will be "obvious fist advantages, significant spillover effect and weakening role of Hong Kong and Macao". This paper divided urban cooperative innovation of Guangdong-Hong Kong-Macao Greater Bay Area into three stages. Summing up the characteristics of each stage is helpful to recognize the changes of urban cooperative innovation and to do a good job in industrial layout planning.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.628-641
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• 2012

This research investigates a dynamic mechanism underlying the co-evolution between network and space by applying hype-curve model, typical phenomenon which shows how new technologies and ideas initially adapted in the society. This study analysis the knowledge intensive industry of digital contents using social network analysis (SNA) in terms of structural, spatial, and temporal aspects, year of 2000, 2005, and 2010 focused on Seoul area. First of all, network and space establish 'inter-feedback' as a result of evolution and differentiation process. Second, it happen temporal 'delay' through the learning process stage of 'peak of inflated expectation' and 'trough of disillusionment.' As a result, Seoul develops with the technology commercialized-orient strategy affect government policy. This trend changes to technology-oriented development in Seoul area in the late of 2000 established 'self-organization' with geographical proximity organizations through learning process.