• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• 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 KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.284-288
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• 2009

With explosively growing PPI databases, the computational approach for a prediction and configuration of PPI network has been a big stream in the bioinformatics area. Recent researches gradually consider physicochemical properties of proteins and support high resolution results with integration of experimental results. With regard to current research trend, it is very close future to complete a PPI network configuration of each organism. However, direct applying the PPI network to real field is complicated problem because PPI network is only a set of co-expressive proteins or gene products, and its network link means simple physical binding rather than in-depth knowledge of biological process. In this paper, we suggest a protein functional flow model which is a directed network based on a protein functions' relation of signaling transduction pathway. The vertex of the suggested model is a molecular function annotated by gene ontology, and the relations among the vertex are considered as edges. Thus, it is easy to trace a specific function's transition, and it can be a constraint to extract a meaningful sub-path from whole PPI network. To evaluate the model, 11 functional flow models of Homo sapiens were built from KEGG, and Cronbach's alpha values were measured (alpha=0.67). Among 1023 functional flows, 765 functional flows showed 0.6 or higher alpha values.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.159-169
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• 2003

Five two-phase closed loop thermosyphons (TLTs) specially designed and constructed for the present study are one small scale loop, two medium scale loops (MSLI and MSLII) and two large scale loops (LSLI and LSLII). Two simulation models based on thermal resistance network, lumped and sectorial, are presented. In the Lumped model, the evaporator section is dealt as one lumped boiling section. Whereas, in the Sectorial model, all possible phenomena which would occur in the evaporator section due to the two-phase boiling process are considered in detail. Flow regimes, the flow transitions between flow regimes and other two-phase parameters involved in two-phase flows are carefully analyzed. In the present study, the results of two different simulation models are compared with experimental results. The comparisons showed that the simulation results by the Lumped model and by the Sectorial model did not show any partiality for the model used for the simulation. The simulation results according to the correlations show the various results in the large different range.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.140-144
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• 2003

A sensor network consists of many low-cost, low-power, and multi-functional sensor nodes. One of most important issues in of sensor networks is to increase network lifetime, and there have been researches on the problem. In this paper, we propose a routing mechanism to prolong network lifetime, in which each node adjusts its transmission power to send data to its neighbors. We model the energy efficient routing with power control and present an algorithm to obtain the optimal flow solution for maximum network lifetime. Then, we derive an upper bound on the network lifetime for specific network topologies.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.465-475
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• 2003

Groundwater inflow into the caverns constructed in fractured rock mass was simulated by numerical modeling, NAPSAC (DFN, discrete fracture network model) and NAMMU (CPM, continuous porous media model), a finite-element software package for groundwater flow in 3D fractured media developed by AEA Technology, UK. The input parameters for modeling were determined on surface fracture survey, core logging and single hole hydraulic test data. In order to predict the groundwater inflow more accurately, the anisotropic hydraulic conductivity was considered. The anisotropic hydraulic conductivities were calculated from the fracture network properties. With a minor adjustment during model calibration, the numerical modeling is able to reproduce reasonably groundwater inflows into cavern and the travel length and times to the ground surface along the flow paths in the normal, dry and rainy seasons.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.347-360
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• 1998

Numerical simulations for fluid flow and solute transport in a fracture rock masses are performed by using a transient flow model, which is based on the three-dimensional stochastic and discrete fracture network model (DFN model) and is coupled hydraulic model with mechanical model. In the numerical simulations of the solute transport, we used to the particle following algorithm which is similar to an advective biased random walk. The purpose of this study is to predict the response of the tracer test between two deep bore holes (GPK1 and GPK2) implanted at Soultz sous Foret in France, in the context of the geothermal researches.l The data sets used are obtained from in situcirculating experiments during 1995. As the result of the transport simulation, the mean transit time for the non reactive particles is about 5 days between two bore holes.

• Journal of Korean Society of Transportation
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• v.19 no.3
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• pp.133-144
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• 2001

The purpose of this study is to develop micro simulation model for large-scale network with driver's behavior model. This study is performed for uninterrupted flow road section. And this model is developed to simulate traffic flow of the real network with unique geometric structure. The vehicle transmission and drivers' behavior model based on the exiting Cellular Automata approach.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.732-736
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• 2008

The traditional method to calculate the gravity feed is to assume that only one tank in fuel system supplies the needed fuel to the engine, and then calculated for the single branch. Actually, all fuel tanks compete for supplying oil. Our method takes into consideration all fuel tanks and therefore, we believe, our method is intrinsically superior to traditional methods and is closer to understanding the real seriousness of the oil supply situation. Firstly, the thesis gives the mathematical model for fuel flow pipe, pump, check valve and the simulation model for fuel tank. On the basis of flow network theory and time difference method, we established a new calculation method for gravity feed oil of aeroplane fuel system, secondly. This model can solve the multiple-branch and transient process simulation of gravity feed oil. Finally, we give a numerical example for a certain type of aircraft, achieved the variations of oil level and flow mass per second of each oil tanks. In addition, we also obtained the variations of the oil pressure of the engine inlet, and predicted the maximum time that the aeroplane could fly safely under gravity feed. These variations show that our proposed method of calculations is satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1307-1323
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• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.