• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.39-66
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• 2023

New media moved the place of social exchange to the Internet, allowing large groups to communicate in one place beyond the limits of time and space. Recent studies have also reported cases in which human social abilities do not keep up with the expansion of group scale through social media. In this context, current study investigated how human perception of social information is affected by the expansion of the group scale in the context of majority bias. Using Internet-based task, the psychological processes that group ratio and group size are perceived and affect majority-biased social information use were investigated, and whether group scale moderates those processes was examined. The group ratio has a positive effect on the majority bias, and the relationship was partially mediated by ratio perception. Group scale did not moderate the relationship between group ratio and ratio perception. On the other hand, the correlation between group size and majority-biased social information use was not significant. Group scale moderates group size perception. The group size and size perception showed positive correlation under the smaller group scale condition. However under the extended group scale condition, the perceived group size became significantly lower and lost its correlation with group size. These results provide evidence that the psychological mechanism related to group size perception was not properly responding to the expansion of the group scale. Furthermore, the possibility of a specific psychological mechanism for processing group size information and the form of information input specifically accepted by majority bias were discussed from perspective of evolutionary psychology.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.355-361
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• 2008

Stereo matching is one of the most active research areas in computer vision. In this paper, we propose a stereo matching scheme using genetic algorithm for object depth recognition. The proposed approach considers the matching environment as an optimization problem and finds the optimal solution by using an evolutionary strategy. Accordingly, genetic operators are adapted for the circumstances of stereo matching. An individual is a disparity set. Horizontal pixel line of image is considered as a chromosome. A cost function is composed of certain constraints which are commonly used in stereo matching. Since the cost function consists of intensity, similarity and disparity smoothness, the matching process is considered at the same time in each generation. The LoG(Laplacian of Gaussian) edge is extracted and used in the determination of the chromosome. We validate our approach with experimental results on stereo images.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.24-36
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• 2008

Architecture-based competition has become a very important issue in many industries. As companies seek lower cost, fast development, and more customizability at the same time, modular architecture of products and processes seem to be an inevitable choice. Existing literature, however, has only focused on the basic contents of architecture-based competition. Different competitive environments and technological competencies of incumbent companies influence the evolutionary dynamics of dominant architecture of industries. In this paper we suggest a new theoretical framework to deal with the complex co-adaptation process of architecture-based competition. We first explore the emerging modular architecture in Chinese commercial vehicle industry, and then compare it with the architecture strategies of Korean companies. Based on the explorative case study, we propose new hypotheses relating the market demand, technological competencies of major players and dominant architecture in an indus-try.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.

• Journal of The Korean Astronomical Society
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• v.46 no.3
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• pp.103-123
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• 2013

Star clusters are superb astrophysical laboratories containing cospatial and coeval samples of stars with similar chemical composition. We initiate the Sejong Open cluster Survey (SOS) - a project dedicated to providing homogeneous photometry of a large number of open clusters in the SAAO Johnson-Cousins' UBV I system. To achieve our main goal, we pay much attention to the observation of standard stars in order to reproduce the SAAO standard system. Many of our targets are relatively small sparse clusters that escaped previous observations. As clusters are considered building blocks of the Galactic disk, their physical properties such as the initial mass function, the pattern of mass segregation, etc. give valuable information on the formation and evolution of the Galactic disk. The spatial distribution of young open clusters will be used to revise the local spiral arm structure of the Galaxy. In addition, the homogeneous data can also be used to test stellar evolutionary theory, especially concerning rare massive stars. In this paper we present the target selection criteria, the observational strategy for accurate photometry, and the adopted calibrations for data analysis such as color-color relations, zero-age main sequence relations, Sp - MV relations, Sp - $T_{eff}$ relations, Sp - color relations, and $T_{eff}$ - BC relations. Finally we provide some data analysis such as the determination of the reddening law, the membership selection criteria, and distance determination.

• Ocean and Polar Research
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• v.27 no.2
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• pp.205-214
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• 2005

From ancient Antarctic glacier ice, we extracted total genomic DNA that was suitable for prokaryotic 16S rDNA gene cloning and sequencing, and bacterial artificial chromosome (BAC) library and end-sequencing. The ice samples were from the Dry Valley region. Age dating by $^{40}Ar/^{39}Ar$ analysis on the volcanic ashes deposited in situ indicated the ice samples are minimum 100,000-300,000 yr (sample DLE) and 8 million years (sample EME) old. Further assay proved the ice survived freeze-thaw cycles or other re-working processes. EME, which was from a small lobe of the basal Taylor glacier, is the oldest known ice on Earth. Microorganisms, preserved frozen in glacier ice and isolated from the rest of the world over a geological time scale, can provide valuable data or insight for the diversity, distribution, survival strategy, and evolutionary relationships to the extant relatives. From the 16S gene cloning study, we detected no PCR amplicons with Archaea-specific primers, however we found many phylotypes belonging to Bacteria divisions, such as Actinobacteria, Acidobacteria, Proteobacteria $({\alpha},\;{\beta},\;and\;{\gamma})$, Firmicutes, and Cytophaga-Flavobacterium-Bacteroid$. BAC cloning and sequencing revealed protein codings highly identical to phenylacetic acid degradation protein paaA, chromosome segregation ATPases, or cold shock protein B of present day bacteria. Throughput sequencing of the BAC clones is underway. Viable and culturable cells were recovered from the DLE sample, and characterized by their 16S rDNA sequences. Further investigation on the survivorship and functional genes from the past should help unveil the evolution of life on Earth, or elsewhere, if any.

• Convergence Security Journal
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• v.13 no.2
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• pp.85-93
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• 2013

Today, the structure of terrorist organizations in the form of a variety of network complexity are evolving. However, terrorist organization not combining randomly generated network but preferential attachment a network. So, it's research should be preceded a better understanding about the characteristics and type of terror network for a effective counter-terrorism policy of law enforcement. In addition, the appropriate response strategy have to technique establish in an era of smart media. In particular, homegrown terrorist attacks on unspecified people without boundaries of countries and regions unlike the traditional terrorism. Also, homegrown terrorism are violence and criminal activity by new various of religion, politics, philosophy. Besides the extreme members of homegrown terror networks went grow up through the evolutionary process in the age of smart media. Law enforcement agencies must identify the terrorist network at the national level. Therefore, terror networks evolving in the online space, forming a radical homegrown terror organizations have access to the network. Intelligence community track terrorist networks and to block the negative aspects of the smart media outlets should be considered.

• International Journal of Fluid Machinery and Systems
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• v.8 no.4
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• pp.283-293
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• 2015

The effect of the inlet swirling flow in a hydraulic turbine draft tube is a very complex phenomenon, which has been extensively investigated both theoretically and experimentally. In fact, the finding of the optimal flow distribution at the draft tube inlet in order to get the best performance has remained a challenge. Thus, attempting to answer this question, it was assumed that through an automatic optimization process a Genetic Algorithm would be able to manage a parameterized inlet velocity profile in order to achieve the best flow field for a particular draft tube. As a result of the optimization process, it was possible to obtain different draft-tube flow structures generated by the automatic manipulation of parameterized inlet velocity profiles. Thus, this work develops a qualitative and quantitative analysis of these new draft tube flow field structures provoked by the redesigned inlet velocity profiles. The comparisons among the different flow fields obtained clearly illustrate the importance of the flow uniformity at the end of the conduit. Another important aspect has been the elimination of the re-circulating flow area which used to promote an adverse pressure gradient in the cone, deteriorating the pressure recovery effect. Thanks to the evolutionary optimization strategy, it has been possible to demonstrate that the optimized inlet velocity profile can suppress or mitigate, at least numerically, the undesirable draft tube flow characteristics. Finally, since there is only a single swirl number for which the objective function has been minimized, the energy loss factor might be slightly affected by the flow rate if the same relation of the axial-tangential velocity components is maintained, which makes it possible to scale the inlet velocity field to different operating points.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.