• Journal of Multimedia Information System
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• v.3 no.4
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• pp.149-154
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• 2016

Wireless mesh networks (WMNs) are used for metropolitan area network that requires high network throughput for handling many users. TDMA-based access is a common solution for this problem and several scheduling methods have been proposed. However, existing heuristic methods have room for improvement at network throughput although they are low complexity. In this paper, we propose a novel divided scheduling method based on structural characteristics in order to improve network throughput in WMNs. It separately schedules neighbor links of gateways and that of the other links by different scheduling algorithms. Simulation-based evaluations show that our proposal improves up to 14% of network throughput compared with conventional scheduling algorithm script.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.309-324
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• 2009

The purpose of this study, decision factors about network characteristics among Social Welfare service organizations and individuals are defined to obtain proper empirical bases for building systematic human service network in the local community bases. As results of analysis, Firstly The order of density is shown as follows the information exchange, the client refer, the resource exchange. Secondly The result about the analysis whether the level factor of the organizational members and organizations affect positively is summarized as follows.it is shown that the number of meeting, organizational type, the prior experience of networking affect positively.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.211-238
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• 2017

This study was conducted using social network analysis, variance analysis, and regression analysis to investigate the effects of cooperation between innovators on technical performance. Based on data of joint applicants filed with the Korean Intellectual Property Office from 2009 to 2012, we derive network structural variables and characteristic variables, and identify network characteristics that affect the overall network structural type, roles for each subject of innovation, and innovation performance. The findings are as follows: ⅰ) The network of this study is a distributed, small-world network within which relatively small groups of innovators are distributed. ⅱ) Universities were found to play the most important role in cooperation, but diversity of cooperating partners exhibited similar effects. ⅲ) It was shown that access to quick and accurate knowledge from familiar partners exhibits a more positive influence on generating innovative performance than unfamiliar knowledge from a variety of cooperating partners in non-familiar fields.

• International Commerce and Information Review
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• v.17 no.3
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• pp.29-57
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• 2015

The purpose of this study is to find the factors of Social Network Games of continuous game flow and addiction. This study analyzes the significant factors of the addiction of SNGs from three categories. It also seeks to find out what relation between continuous game flow and addiction in SNGs. The model consists of three categories (SNG characteristic, User characteristic, and Environmental characteristic). The research model was conducted through the structural equation modeling(SEM) approach, and tested using 374 questionnaires. The results indicated that SNG characteristic(accessibility, enjoyment, feedback), User characteristic(self-control), Environmental characteristic(social interaction, subjective norm) have a positive effect on continuous game flow. The findings also that continuous game flow plays a moderation role that affects addiction. Finally, we discussed the research results and offered relevant suggestions for schools, firms, and future studies

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.688-701
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• 2020

This study analyzes the characteristics and change factors of technological cooperation networks in the automotive industry. Using Social Network Analysis (SNA) of 112,009 autonomous driving-related patents filed from 2000 to 2017 by major automotive firms in the world, we investigate the structure of the technological cooperation network. Network characteristics such as density are analyzed through structural characteristic analysis among the network analysis indicators. The structural characteristics of the technology cooperation network are confirmed through analysis of status characteristic indicators, such as the degree of centrality, betweenness centrality, and closeness centrality. Results show that car makers such as Toyota and Hyundai Motors, as well as parts suppliers such as Bosch and Continental, have high-performance technology developments related to autonomous driving. The structural characteristics of the network show that companies participating in cooperative networks for autonomous driving technology development have increased in number and are diversified, and all of the status characteristics indicators have decreased. This can be interpreted as an increasing number of horizontal and complementary forms of technological cooperation between firms. In addition, it was confirmed that the number of participants in the field of autonomous driving technology has increased, and the networks have become more complex.

• Investigative Magnetic Resonance Imaging
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• v.15 no.2
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• pp.110-122
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• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.176-187
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• 1998

The Dismantlement and Construction of the buildings have the characteristic of floating change with repetition within a Time Schedule which has fixed resources, that is, Manpower, Materials and Equipments, and involve many disaster factors in performing each processes. But as these factors are variable, a work supervisor should be able to understand data analysed distinctive factors of each activities systematically, organizationally, and scientifically, and diagrammatic information media so that he can control any complex and situation beyond forecast. Thus this study is intended to be a barometer of weighing if the existing process schedule has taken security into consideration by making the characteristics of organization and action of Work package per process in a building field with more factors like these into a network system and is intended to minimize construction disasters by systematizing this into a Transformational Network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2605-2614
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• 2002

This paper describes an analysis method based on the experimental parameter for the 0.9kW starter motor of vehicle with permanent magnet and pole core, and analyzed the influence by the effect of pole core of starting motor by using the 2D-FEM analysis technique to reflect the structural characteristic of 3D. A starter motor consists mainly of armature, yoke and permanent magnet with the pole core. Additionally, there is different the axial length of each part. Therefore, the effective of analysis method is needed to improve the characteristic of it. This paper approached the 2D-FEM analysis estimated by the 3D-EMCN(Three Dimensional Equivalent Magnetic Circuit Network) instead of the 3D-FEM analysis to minimize parameters. As a result, this paper is proposed the validity of 2D-FEM analysis and obtained reasonable results for improving a performance.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.83-98
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• 2021

Research collaboration is an important strategy to improve research output, and virtual academic communities (VACs) have become an important platform to collaborate on. This paper reveals the influencing factors of researchers' collaboration intention in VACs from two attributes: individual, and inter-members. On the basis of the Social Cognitive Theory, Social Exchange Theory, social network theory, and Five-Factor Model, this paper constructed a model demonstrating the influencing factors of VACs researchers' collaboration intention. A self-administered questionnaire was employed on members of four VACs in China to collect data; subsequently, 558 usable responses were analyzed using structural equation modeling. The result showed that openness, conscientiousness, reciprocity, trust, and the social network characteristic had a significant influence on the collaboration intention of researchers in VACs, while self-efficacy, agreeableness, extroversion, neuroticism, and experience had no significant effects on the collaboration intention of researchers in VACs. This model plays a positive role in promoting the research collaboration intention of Chinese VACs researchers and in guiding the construction of VAC platforms.