• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.197-224
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• 2020

Mobile games have become one of the most important driving forces of the game industry. We focus on the continuous intention to use Chinese mobile games from the perspective of experiential marketing and network externalities. We integrate user experience, network externalities and flow theory into expectation confirmation model and explore the influencing factors of continuous usage intention of Chinese mobile game and propose a research model. Game experience, service experience, perceived enjoyment, social interaction, challenge, perceived number of users and perceived number of peers were employed as independent variables, while flow, perceived value and satisfaction as mediating variables and continuous intention as the dependent variable. After surveying 426 samples, the model is tested with structural equation model. The results reveal that perceived enjoyment significantly positively influences perceived value, flow, satisfaction, and continuous intention. The greater the enjoyment of the game, the greater the satisfaction of the game and the greater the willingness to use it continuously. Game experience has a significant direct effect on continuous intention, which indicates that a better game experience can retain more users. Service experience and perceive number of peers positively influence satisfaction. Another finding is that social interaction and perceived number of users positively influence perceived value and flow, which indicate that social attributes are critical roles for retaining users. Game challenge also positively influences flow. The proper level of challenge is more likely to cause users to enter the state of flow. Flow indirectly influences continuous usage intention through the satisfaction of the game, which indicates that satisfaction is driven by flow experience and further retaining users. Empirical results implied that mobile game companies need to focus on improving user experience, expectation satisfaction and extending network externalities to improve the continuous intention of using mobile game.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1454-1459
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• 2004

The major parameters governing the fluid dynamical and thermo-dynamical behavior in the large pipeline network system are friction loss and the pipeline length. But in local pipeline networks and relatively short distance pipeline system, secondary loss and the considerations of the moving states of the fluid machine are also important. One of the major element in local pressure control system is pressure regulator. It causes the variations of the physical properties in that pipeline system. When it is under working, the accurate analysis of the flow properties is so difficult. In this study, some numerical approaches to investigate the critical-flow-characteristics of the pressure regulator have been done according to the variations of the opening ratio or cross-sectional area and the detail examinations and considerations of the pressure regulator as a pipeline network elements have been carried. Finally the flow-flied distributions and critical-flow-characteristics have been presented in detail and the critical flow phenomena and the relation to the opening ratio or cross-sectional-area ratio have been studied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.4
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• pp.103-112
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• 1989

A numerical model for Steady state groundwater flow has been established to understand the groundwater flow phenomena near a radioactive waste repository. The integrated finite difference method based on a network composed of nodes and members was applied to investigate groundwater flow in homogeneous, heterogeneous and layered media. Its numerical solution was in good agreement with analytic solution. Physical phenomena associated in the groundwater flow depending on both hydraulic characteristics and effects of fractured zone were also investigated. A method by which feasible groundwater flow paths can be identified was developed. This method used the composite network for the geologic media near a repository and the direction of computed groudwater velocity. Groundwater velocity and travel time were predicted for the possible pathway form a repository to a biosphere.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.608-619
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• 2018

As distributed generation (DG) is connected to grid, there is new node-type occurring in distribution network. An efficient algorithm is proposed in this paper to calculate power flow for weakly meshed distribution network with DGs in different load models. The algorithm respectively establishes mathematical models focusing on the wind power, photovoltaic cell, fuel cell, and gas turbine, wherein the different DGs are respectively equivalent to PQ, PI, PQ (V) and PV node-type. When dealing with PV node, the algorithm adopts reactive power compensation device to correct power, and the reactive power allocation principle is proposed to determine reactive power initial value to improve convergence of the algorithm. In addition, when dealing with the weakly meshed network, the proposed algorithm, which builds path matrix based on loop-analysis and establishes incident matrix of node voltage and injection current, possesses good convergence and strong ability to process the loops. The simulation results in IEEE33 and PG&G69 node distribution networks show that with increase of the number of loops, the algorithm's iteration times will decrease, and its convergence performance is stronger. Clearly, it can be effectively used to solve the problem of power flow calculation for weakly meshed distribution network containing different DGs.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4787-4807
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• 2016

Although the dense interconnection datacenter networks (DCNs) (e.g. FatTree) provide multiple paths and high bisection bandwidth for each server pair, the single-path TCP (SPT) and ECMP which are widely used currently neither achieve high bandwidth utilization nor have good load balancing. Due to only one available transmission path, SPT cannot make full use of all available bandwidth, while ECMP's random hashing results in many collisions. In this paper, we present OFPT, an OpenFlow based Parallel Transport framework, which integrates precise routing and scheduling for better load balancing and higher network throughput. By adopting OpenFlow based centralized control mechanism, OFPT computes the optimal path and bandwidth provision for each flow according to the global network view. To guarantee high throughput, OFPT dynamically schedules flows with Seamless Flow Migration Mechanism (SFMM), which can avoid packet loss in flow rerouting. Finally, we test OFPT on Mininet and implement it in a real testbed. The experimental results show that the average network throughput in OFPT is up to 97.5% of bisection bandwidth, which is higher than ECMP by 36%. Besides, OFPT decreases the average flow completion time (AFCT) and achieves better scalability.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.51-51
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• 1989

• Annals of Clinical Neurophysiology
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• v.25 no.2
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• pp.93-102
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• 2023

Background: We aimed to identify any differences in the structural covariance network based on structural volume and those in the functional network based on cerebral blood flow between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura. Methods: We prospectively enrolled 27 patients with migraine without aura, all of whom had unilateral migraine pain. We defined the ipsilateral hemisphere as the side of migraine pain. We measured structural volumes on three-dimensional T1-weighted images and cerebral blood flow using arterial spin labeling magnetic resonance imaging. We then analyzed the structural covariance network based on structural volume and the functional network based on cerebral blood flow using graph theory. Results: There were no significant differences in structural volume or cerebral blood flow between the ipsilateral and contralateral hemispheres. However, there were significant differences between the hemispheres in the structural covariance network and the functional network. In the structural covariance network, the betweenness centrality of the thalamus was lower in the ipsilateral hemisphere than in the contralateral hemisphere. In the functional network, the betweenness centrality of the anterior cingulate and paracingulate gyrus was lower in the ipsilateral hemisphere than in the contralateral hemisphere, while that of the opercular part of the inferior frontal gyrus was higher in the former hemisphere. Conclusions: The present findings indicate that there are significant differences in the structural covariance network and the functional network between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.80-85
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• 2021

This study is to analyze the structure of the networks of tourism marketing on Twitter, identifying the most prominent users, the flow of information about tourism marketing, and the interaction between the users posting tweets. This study employs NodeXL pro as a visualization software package for social network analysis. The number of vertices or nodes is 171, and the number of the unique edges or links is 128, but there are 101 edges with duplicates, so the total links are 229, which means that there are fewer Twitter accounts in the social network on tourism marketing, but they have a few close relationships by sharing information. The research can map the social network of communicators of tourism marketing using Twitter data. The network has a complicated pattern, including one independent network and some connected networks. Some mediators connect each network and can control the information flow of tourism marketing. More communicators are getting the information than the ones providing it, which means that there is likely to be the dependence of information among communicators that can cause an obstacle and distortion of the information flow system, especially in the independent network.

• Smart Media Journal
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• v.4 no.4
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• pp.24-32
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• 2015

Recently, SDN is actively used as datacenter networks and gradually increase its applied areas. Along with this change of networking environment, research of deploying network security systems on SDN becomes highlighted. Especially, systems for detecting network flooding attacks by monitoring every packets through ports of OpenFlow switches have been proposed. However, because of the centralized management of a SDN controller which manage multiple switches, it may be substantial overhead that the attack detection system continuously monitors all the flows. In this paper, a sampling based network flooding attack detection and prevention system is proposed to reduce the overhead of monitoring packets and to achieve reasonable functionality of attack detection and prevention. The proposed system periodically takes sample packets of network flows with the given sampling conditions, analyzes the sampled packets to detect network flooding attacks, and block the attack flows actively by managing the flow entries in OpenFlow switches. As network traffic sampler, sFlow agent is used, and snort, an opensource IDS, is used to detect network flooding attack from the sampled packets. For active prevention of the detected attacks, an OpenDaylight application is developed and applied. The proposed system is evaluated on the local testbed composed with multiple OVSes (Open Virtual Switch), and the performance and overhead of the proposed system under various sampling condition is analyzed.