• Journal of Digital Convergence
• /
• v.1 no.1
• /
• pp.147-164
• /
• 2003

The use of electronic mail and messenger software tools has been increased rapidly over a decade. Compared to the variety of research related to the relationship between personal psychological traits and communication modes, effects of human psychology on online activities have only recently become a focus of interest. This research analyzed the relationship between personal psychological type and online connectivity. We employed network analysis methodology and collected and analyzed data from 146 subjects. Significant differences in network measures were found among groups with different psychological style. Findings of the research can provide several implications for managerial activities regarding social connectivity.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.10B
• /
• pp.1807-1814
• /
• 1999

This paper considers connectivity-related reliability measures for a class of fault-tolerant shuffle exchange networks to characterize the degrading features over time in the presence of faulty switching elements. The mean number of connected input/output pairs, the mean number of survivable input are considered as connectivity measures. The measures for the unique-path shuffle exchange network(SEN) and its two fault-tolerant variants, extra-stage SEN(SEN+) and INDRA network are derived analytically, and then are compared with numerical experiments.

• Journal of Korean Society of Transportation
• /
• v.28 no.6
• /
• pp.167-177
• /
• 2010

This study proposes a methodology for assessing the improvement of network connectivity of transport hubs. Extending a previously developed model that measures the connectivity of a node in transportation networks, we define two quantities called the supplied connectivity and the experienced connectivity. Using these quantities, we provide a systematic procedure for analyzing the network connectivity of a transport hub and also suggest criteria for determining whether a given project is effective in improving the network connectivity of the transport hub. The application of the methodology to a test site produces reasonable results, and as such it is expected that the methodology can be used for various transport hubs in the national road network. Once enough data from the application of the methodology are accumulated, a further study on the level of service in terms of network connectivity needs to be followed.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.85-98
• /
• 2010

As transit networks are becoming more multimodal, the concept of connectivity of transit networks becomes important. This study aims to develop a quantitative model for measuring the connectivity of multimodal transit networks. To that end, we select, as evaluation measures of a transit line, its length, capacity, and speed. We then define the connecting power of a transit line as the product of those measures. The degree centrality of a node, which is a widely used centrality measure in social network analysis, is employed with appropriate modifications suited for transit networks. Using the degree centrality of a transit stop and the connecting powers of transit lines serving the transit stop, we develop an index quantifying the level of connectivity of the transit stop. From the connectivity indexes of transit stops, we derive the connectivity index of a transit line as well as an area of a multimodal transit network. In addition, we present a method to evaluate the connectivity of a transfer center using the connectivity indexes of transit stops and passenger acceptance rate functions. A case study shows that the connectivity evaluation model developed in this study takes well into consideration characteristics of multimodal transit networks, adequately measures the connectivity of transit stops, lines, and areas, and furthermore can be used in determining the level of service of transfer centers.

• Annals of Clinical Neurophysiology
• /
• v.22 no.2
• /
• pp.82-91
• /
• 2020

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer's disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

• Molecules and Cells
• /
• v.42 no.8
• /
• pp.579-588
• /
• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Annals of Clinical Neurophysiology
• /
• v.23 no.2
• /
• pp.69-81
• /
• 2021

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.1
• /
• pp.149-158
• /
• 2019

Due to the rapid development of IT (information technology) and internet, products become smart and able to collect, process and produce information and can think of themselves to provide better service to consumers. However, research on the characteristics of smart product is still sparse. In this paper, we report the systemic development of a scale to measure the perceived product smartness associated with smart product. To develop product smartness scale, this study follows systemic scale development processes of item generation, item reduction, scale validation, reliability and validity test consequently. And, after acquiring a large amount of qualitative interview data asking the definition of smart product, we add a unique process to reduce the initial items using both a text mining method using 'r' s/w and traditional reliability and validity tests including factor analysis. Based on an initial qualitative inquiry and subsequent quantitative survey, an eight-factor scale of product smartness is developed. The eight factors are multi-functionality, human-like touch, ability to cooperate, autonomy, situatedness, network connectivity, integrity, and learning capability consequently. Results from Korean samples support the proposed measures of product smartness in terms of reliability, validity, and dimensionality. Implications and directions for further study are discussed. The developed scale offers important theoretical and pragmatic implications for researchers and practitioners.

• Convergence Security Journal
• /
• v.9 no.4
• /
• pp.7-12
• /
• 2009

It is very difficult to completely block the DDoS attack, which paralyzes services by depleting resources or occupying the network bandwidth by transmitting a vast amount of traffic to the specific website or server from normal users' PCs that have been already infected by an outside attacker. In order to defense or endure the DDoS attack, we usually use various solutions such as IDS (Intrusion Detection System), IPS (Intrusion Prevention System), ITS (Intrusion Tolerance System), FW (Firewall), and the dedicated security equipment against DDoS attack. However, diverse types of security appliances cause the cost problem, besides, the full function of the equipments are not performed well owing to the unproper setting without considering connectivity among systems. In this paper, we present the effective connectivity of security equipments and countermeasure methodology against DDoS attack. In practice, it is approved by experimentation that this designed methdology is better than existing network structure in the efficiency of block and endurance. Therefore, we would like to propose the effective security control measures responding and enduring against discriminated DDoS attacks through this research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.660-663
• /
• 2003

The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using Fuzzy Cognitive Maps(FCM) that can detect intrusion by the Denial of Service(DoS) attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The Sp flooding Preventer using Fuzzy cognitive maps(SPuF) model captures and analyzes the packet information to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. The result of simulating the "KDD ′99 Competition Data Set" in the SPuF model shows that the Probe detection rates were over 97 percentages.