• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.107-114
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• 2023

The study provides the identification of vulnerabilities in the security issues by Wireless Network. To achieve it the research focus on packet flow analysis, end to end data communication, and the security challenges (Cybercrime, insider threat, attackers, hactivist, malware and Ransomware). To solve this I have used the systematic literature review mechanisms and demonstrative tool namely Wireshark network analyzer. The practical demonstration identifies the packet flow, packet length time, data flow statistics, end- to- end packet flow, reached and lost packets in the network and input/output packet statics graphs. Then, I have developed the proposed model that used to secure the Wireless network solution and prevention vulnerabilities of the network security challenges. And applying the model that used to investigate the security challenges and vulnerabilities of cloud computing services is used to fulfill the network security goals in Wireless network. Finally the research provides the model that investigate the security challenges and vulnerabilities of cloud computing services in wireless networks

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.397-397
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• 2015

The frequency of urban floods is recently increased as a consequence of climate change and haphazard development in urban area. To mitigate and prevent the flood damage, we generally utilized a numerical model to investigate the causes and risk of urban flood. Contrary to general flood inundation model simulating only the surface flow, the model needs to consider flow of the sewer network system like SWMM and ILLUDAS. However, this kind of model can not consider the interaction between the surface flow and drainage network. Therefore, we tried to evaluate the impact of bidirectional interaction between sewer and surface flow in urban flooding analysis based on simulations using the quasi-interacted model and the interacted model. As a general quasi-interacted model, SWMM5 and FLUMEN are utilized to analyze the flow of drainage network and simulate the inundation area, respectively. Then, FLO-2D is introduced to consider the interaction between the surface flow and sewer system. The two method applied to the biggest flood event occurred in July 2011 in Sadang area, South Korea. Based on the comparison with observation data, we confirmed that the model considering the interaction the sewer network and surface flow, showed a good agreement than the quasi-interacted model.

• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• Nuclear Engineering and Technology
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• v.39 no.1
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• pp.43-50
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• 2007

A partial flow blockage in an assembly of a liquid metal reactor could result in a cooling deficiency of the core. To develop a partial blockage detection system, we have studied the changes of the temperature fluctuation characteristics in the upper plenum according to changes of the t10w blockage conditions in an assembly. We analyzed the temperature fluctuation in the upper plenum with the Large Eddy Simulation (LES) turbulence model in the CFX code and evaluated its statistical parameters. Based on the results of the statistical analyses, we developed a neural network model for detecting a partial flow blockage in an assembly. The neural network model can retrieve the size and the location of a flow blockage in an assembly from a change of the root mean square, the standard deviation, and the skewness in the temperature fluctuation data. The neural network model was found to be a possible alternative by which to identify a flow blockage in an assembly of a liquid metal reactor through learning and validating various flow blockage conditions.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.145-152
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• 2004

The flow stress value was calculated by comparing predicted and measured roll force. Using basic on-line roll force model and logged mill data the flow stress equation of high strength steel for automobile was derived. The flow stress equation consists of the flow stress equation of carbon steel and flow stress factor calculated by neural network with input parameters not only carbon contents, strip temperature, strain, and strain rate, but also compositions such as Mn, p, Ti, Nb, and Mo. Using the flow stress equation and basic roll force model, precision roll force model of high strength steel for automobile was derived. Using test set of logged mill data the flow stress equation was verified.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.309-319
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• 2014

Virtual Network service is a key characteristics of future Internet which is debate internationally. There are two kinds of network virtualization technologies considered lately: network functions virtualization and virtual network approaches. Several national wide research networks including US's GENI project have experimented technologies for future Internet and in particular network virtualization is one of key issues. Representative Korean research network, KREONET, is working on deploying virtual network framework as a preliminary for future Ineternet using the virtualization model of SDN/OpenFlow which is typical network model of future Internet. This paper proposes a stepwise model to bring virtual network services in KREONET. Firstly, we requirements of KREONET users' virtual network service and network resource management and network deploying virtual network. Finally, we verify the adequacy of our virtual network model for KREONET.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.247-272
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• 2012

Social Network Service(SNS) is a kind of advanced Internet service that acts as personal media. This study was intended to find out the structural relationships among factors affecting the usage of social network services by extending the Technology Acceptance Model(TAM). For this purpose, the variable 'Flow' was first integrated into the TAM in order to understand the internal motivations of users. And then the external factors of SNS usage were derived from the perspective of users, contents, and media side of SNS, and finally the dependent variable was set with the intention of sustainable use. Then these factors were carefully integrated into a structural model. We expect the findings of this study will be very helpful for the internet marketing professionals, SNS developers, and the others.

• Transactions of Materials Processing
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• v.27 no.4
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• 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.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.