Relevant bibliographies by topics / Network model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network model'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 February 2023

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Journal articles on the topic "Network model"

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HRABCAK, David, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (December 4, 2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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Koide, Satoshi, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn. "Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network." International Journal of Machine Learning and Computing 4, no. 4 (2014): 319–27. http://dx.doi.org/10.7763/ijmlc.2014.v4.431.

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Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, and Kenji Mase. "Greedy Network Growth Model of Social Network Service." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (July 20, 2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also enabled us to reproduce 30 networks built up by existing network models. We thus show that the proposed model represents the expressiveness of many existing network models.
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Murugan, S., and Dr M. Jeyakarthic. "Optimal Deep Neural Network based Classification Model for Intrusion Detection in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1374–87. http://dx.doi.org/10.5373/jardcs/v11sp10/20192983.

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Hrabcak, David, Lubomir Dobos, Jan Papaj, and Lubos Ovsenik. "Multilayered Network Model for Mobile Network Infrastructure Disruption." Sensors 20, no. 19 (September 25, 2020): 5491. http://dx.doi.org/10.3390/s20195491.

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In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.
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Pardo, Raúl, and Gerardo Schneider. "Model Checking Social Network Models." Electronic Proceedings in Theoretical Computer Science 256 (September 6, 2017): 238–52. http://dx.doi.org/10.4204/eptcs.256.17.

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Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (July 1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This method produces statistically significant gains in accuracy over both a standard neural network approach and a linear model. Furthermore, using the approximate linear model to initialize the weights of the network produces statistically less variation in model fidelity. By structuring the neural network according to the approximate linear model, the model can be readily interpreted.
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Xu, Shuai, and Bai Da Zhang. "Complex Network Model and its Application." Advanced Materials Research 791-793 (September 2013): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.

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Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, however, due to the complexity of these networks, conventional simplified model and cannot be good solution to the above problems. The complex network and network complexity has become a hot issue in the scientific and engineering concern. This article describes a few common complex network models and its application brief.
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Ouassit, Youssef. "CT Liver Segmentation: A Capsules Network Model." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1016–24. http://dx.doi.org/10.5373/jardcs/v12sp4/20201574.

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Djellali, Choukri, and Mehdi adda. "An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 35–41. http://dx.doi.org/10.5383/juspn.15.01.005.

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In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.
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Dissertations / Theses on the topic "Network model"

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Xiaodan, Xie. "Network Interdiction Model on Interdependent Incomplete Network." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702.

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Mao, Ai-sheng. "A Theoretical Network Model and the Incremental Hypercube-Based Networks." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277860/.

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The study of multicomputer interconnection networks is an important area of research in parallel processing. We introduce vertex-symmetric Hamming-group graphs as a model to design a wide variety of network topologies including the hypercube network.
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Kuehn, Daniel, and Sofia Ljunggren. "Refining a Network Model Concerning Network Security Risk Analysis." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154355.

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P²CySeMoL is a framework in which security risks are calculated and presented with a value referring to the probability that an attack will succeed in a system, mainly SCADA systems. This thesis covers the creation of a more granular network module for the P²CySeMoL security riskanalysis framework to better be able to represent a network in concrete modules and to enable security riskanalysis on a network infrastructure at a greater detail. This paper outlines the creation of a network module with the base in the OSI model. It is replicated in a way that the network module is an extension to the P²CySeMoL metamodel, without interfering and restructuring it. It also covers a smaller survey to verify and get feedback about the created module from security and network experts and analysis of the survey. The conclusion is made that the network module is a good base to build upon and reflects to good degree a model needed to do security risk analysis on a network infrastructure and suggestions about further validation and research to improve the module are outlined. This thesis was produced in cooperation with Spotify AB and parts of the team behind P²CySeMoL at the ICS department at KTH.
P²CySeMoL är ett ramverk där säkerhetsrisker beräknas och redovisas i form av ett värde som hän-visar till sannolikheten att en attack lyckas i ett system, huvudsakligen SCADA system. Den här avhandlingen behandlar skapandet av en mer detaljerad nätverksmodul för säkerhetsriskramverket P²CySeMoL för att bättre representera ett nätverks konkreta moduler och för att möjliggöra analys av säkerhetsrisker rörande en nätverksinfrastruktur på ett mer detaljerat sätt. Den här rapporten beskriver skapandet av en nätverksmodul med en bas i OSI-modellen. Den är replikerad på ett sätt att den är en extension av P²CySeMoL metamodell, utan att omstrukturera den. Det omfattar även en mindre undersökning för att kontrollera och samla återkoppling på den skapade modulen från säkerhet- och nätverksexperter samt en analys av undersökningen. Slutsatsen fastställer att nätverksmodulen är en bra bas att bygga vidare på och den återspeglar till hög grad en modell som behövs för att göra säkerhetsriskanalyser på en infrastruktur, förslag om ytterligare validering och forskning för att förbättra modulnätet beskrivs. Det här arbetet har producerats i samarbete med Spotify och delar av teamet bakom P²CySeMoL vidICS avdelningen på KTH.
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Gammelgård, Magnus. "The network performance assessment model." Licentiate thesis, KTH, Electrical Systems, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1746.

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The electricity distribution in Sweden is experiencing aconsiderable change in conditions by the radical change inregulation policy. The Swedish Energy Agency(Energimyndigheten) is introducing a new regulatory model, theNetwork Performance Assessment Model, as the main tool forsupervising the natural monopolies of local electricitydistribution. The new model has interesting and far-reachingconsequences for the involved parties.

This thesis investigates the implications of the newregulation and the regulatory tool, in particularly related toIT-investments evaluations in the distribution utilities. Intoday’s utilities, IT-systems are often a vital part ofoperations, ranging from e.g. real-time monitoring andcontrolling of the network to various administrative tasks.Considerable amounts of money are spent on investments toenhance and maintain these IT-systems. The changed regulationand the new regulatory model put a focus, more than ever, onefficient use of utilities’resources, implying a need forsuitable methods to evaluate ITinvestments.

In the thesis, the new regulation is described, inparticular the new regulatory model. The model is presented andcentral implications are identified, e.g. in terms of newrequirements put on the utilities and general businessconsequences. As stated, the focus is on implications relatedto IT-systems and investments in these systems.

Furthermore, a wide range of IT-investment evaluationmethods are presented and categorized in the thesis, focusingon IT-investment appraisal techniques. The categories rangesfrom methods only considering cash flows of investments to moreelaborate methods, e.g. for considering behavioral sciencesaspects. The thesis outlines and presents categories of methodsas well as examples of individual methods.

In the final part of the thesis, suitable IT-investmentevaluation methods, given the implications of the newregulation, are discussed. The implications include both directbusiness related aspects as well as more technical issuesrelated to the IT-investments. It is also concluded that asuitable method need to incorporate both monetary consequencesof the investment as well as a limited number of non-monetary,related to the regulatory model.

Key words:Regulation of electricity distribution,IT-investment evaluation methods, Implications of monopolyregulation


QC 20100608
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Arulselvan, Ashwin. "Network model for disaster management." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024855.

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Lundy, G. M. "Systems of communicating machines : a model for communication protocols." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/8210.

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Yao, Ye. "Ad Hoc Networks Measurement Model and Methods Based on Network Tomography." Phd thesis, Université de Technologie de Belfort-Montbeliard, 2011. http://tel.archives-ouvertes.fr/tel-00636282.

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The measurability of Mobile ad hoc network (MANET) is the precondition of itsmanagement, performance optimization and network resources re-allocations. However, MANET is an infrastructure-free, multi-hop, andself-organized temporary network, comprised of a group of mobile nodes with wirelesscommunication devices. Not only does its topology structure vary with time going by, butalso the communication protocol used in its network layer or data link layer is diverse andnon-standard.In order to solve the problem of interior links performance (such as packet loss rate anddelay) measurement in MANET, this thesis has adopted an external measurement basedon network tomography (NT). To the best of our knowledge, NT technique is adaptable for Ad Hoc networkmeasurement.This thesis has deeply studied MANET measurement technique based on NT. The maincontributions are:(1) An analysis technique on MANET topology dynamic characteristic based onmobility model was proposed. At first, an Ad Hoc network mobility model formalizationis described. Then a MANET topology snapshots capturing method was proposed to findand verify that MANET topology varies in steady and non-steady state in turnperiodically. At the same time, it was proved that it was practicable in theory to introduceNT technique into Ad Hoc network measurement. The fitness hypothesis verification wasadopted to obtain the rule of Ad Hoc network topology dynamic characteristic parameters,and the Markov stochastic process was adopted to analyze MANET topology dynamiccharacteristic. The simulation results show that the method above not only is valid andgenerable to be used for all mobility models in NS-2 Tool, but also could obtain thetopology state keeping experimental formula and topology state varying probabilityformula.IV(2) An analysis technique for MANET topology dynamic characteristic based onmeasurement sample was proposed. When the scenario file of mobile models could notbe obtained beforehand, End-to-End measurement was used in MANET to obtain thepath delay time. Then topology steady period of MANET is inferred by judging whetherpath delay dithering is close to zero. At the same time, the MANET topology wasidentified by using hierarchical clustering method based on measurement sample of pathperformance during topology steady period in order to support the link performanceinference. The simulation result verified that the method above could not only detect themeasurement window time of MANET effectively, but also identify the MANETtopology architecture during measurement window time correctly.(3) A MANET link performance inference algorithm based on linear analysis modelwas proposed. The relation of inequality between link and path performance, such as lossrate of MANET, was deduced according to a linear model. The phenomena thatcommunication characteristic of packets, such as delay and loss rate, is more similarwhen the sub-paths has longer shared links was proved in the document. When the rankof the routing matrix is equal to that of its augmentation matrix, the linear model wasused to describe the Ad Hoc network link performance inference method. The simulationresults show that the algorithm not only is effective, but also has short computing time.(4) A Link performance inference algorithm based on multi-objectives optimizationwas proposed. When the rank of the routing matrix is not equal to that of its augmentationmatrix, the link performance inference was changed into multi-objectives optimizationand genetic algorithm is used to infer link performance. The probability distribution oflink performance in certain time t was obtained by performing more measurements andstatistically analyzing the hypo-solutions. Through the simulation, it can be safelyconcluded that the internal link performance, such as, link loss ratio and link delay, can beinferred correctly when the rank of the routing matrix is not equal to that of itsaugmentation matrix.
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Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.

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Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network (specifically, GRN) growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E. coli and yeast, we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the central idea is to grow the network one motif (specifically, we consider one downlink motif) at a time. The accuracy of our proposed algorithm was evaluated extensively and show much better performance than existing network growing models both in terms of degree and motif distributions. We also propose a complex network growing game that can identify important strategies behind motif interactions by exploiting human (i.e., gamer) intelligence. Our proposed gaming software can also help in educational purposes specifically designed for complex network studies.
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Draai, Kevin. "A model for assessing and reporting network performance measurement in SANReN." Thesis, Nelson Mandela Metropolitan University, 2017. http://hdl.handle.net/10948/16131.

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The performance measurement of a service provider network is an important activity. It is required for the smooth operation of the network as well as for reporting and planning. SANReN is a service provider tasked with serving the research and education network of South Africa. It currently has no structure or process for determining network performance metrics to measure the performance of its network. The objective of this study is to determine, through a process or structure, which metrics are best suited to the SANReN environment. This study is conducted in 3 phases in order to discover and verify the solution to this problem. The phases are "Contextualisation", "Design",and "Verification". The "Contextualisation" phase includes the literature review. This provides the context for the problem area but also serves as a search function for the solution. This study adopts the design science research paradigm which requires the creation of an artefact. The "Design" phase involves the creation of the conceptual network performance measurement model. This is the artefact and a generalised model for determining the network performance metrics for an NREN. To prove the utility of the model it is implemented in the SANReN environment. This is done in the "Verification" phase. The network performance measurement model proposes a process to determine network performance metrics. This process includes getting NREN requirements and goals, defining the NRENs network design goals through these requirements, define network performance metrics from these goals, evaluating the NRENs monitoring capability, and measuring what is possible. This model provides a starting point for NRENs to determine network performance metrics tailored to its environment. This is done in the SANReN environment as a proof of concept. The utility of the model is shown through the implementation in the SANReN environment thus it can be said that it is generic.The tools that monitor the performance of the SANReN network are used to retrieve network performance data from. Through understanding the requirements, determining network design goals and performance metrics, and determining the gap the retrieving of results took place. These results are analysed and finally aggregated to provide information that feeds into SANReN reporting and planning processes. A template is provided to do the aggregation of metric results. This template provides the structure to enable metrics results aggregation but leaves the categories or labels for the reporting and planning sections blank. These categories are specific to each NREN. At this point SANReN has the aggregated information to use for planning and reporting. The model is verified and thus the study’s main research objective is satisfied.
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Jones, Thomas Carroll Jr. "JigCell Model Connector: Building Large Molecular Network Models from Components." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78277.

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The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist.
Master of Science
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Books on the topic "Network model"

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Network, Canadian Model Forest. Canadian Model Forest Network achievements. [Ottawa]: Natural Resources Canada, 2006.

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Wasserman, Theodore, and Lori Drucker Wasserman. Apraxia: The Neural Network Model. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24105-5.

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Black, Uyless. OSI: A model for computer communications standards. Englewood Cliffs, N.J: Prentice-Hall, 1991.

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OSI: A model for computer communications standards. Englewood Cliffs, N.J: Prentice-Hall, 1991.

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Wasserman, Theodore, and Lori Drucker Wasserman. Therapy and the Neural Network Model. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26921-0.

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Estrada, James. Connections: A report on network model options. Hartford, Conn: Connecticut State Library, 1993.

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Christakis, Nicholas A. An empirical model for strategic network formation. Cambridge, MA: National Bureau of Economic Research, 2010.

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A neural network model of lexical organization. London: Continuum Intl Pub Group, 2009.

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Fortescue, Michael D. A neural network model of lexical organization. London: Continuum, 2011.

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Wasserman, Theodore, and Lori Wasserman. Motivation, Effort, and the Neural Network Model. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58724-6.

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Book chapters on the topic "Network model"

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Stańczak, Sławomir, Marcin Wiczanowski, and Holger Boche. "Network Model." In Fundamentals of Resource Allocation in Wireless Networks, 85–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-79386-1_4.

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Nicoletti, Bernardo. "SCOR Model." In Supply Network 5.0, 19–41. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22032-6_2.

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Bijalwan, Anchit. "Network Forensics Process Model." In Network Forensics, 53–75. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003045908-4.

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Shekhar, Shashi, and Hui Xiong. "Network Data Model." In Encyclopedia of GIS, 787. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_876.

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Feng, Jun, and Toyohide Watanabe. "Road Network Model." In Index and Query Methods in Road Networks, 41–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10789-9_3.

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Hainaut, Jean-Luc. "Network Data Model." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_246-2.

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Chang, Gerard J., Lirong Cui, and Frank K. Hwang. "The Network Model." In Network Theory and Applications, 111–26. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0273-5_7.

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Rawolle, Shaun, Muriel Wells, Louise Paatsch, Russell Tytler, and Coral Campbell. "The Network Model." In Improving Schools, 25–41. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-931-8_2.

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Shekhar, Shashi, and Hui Xiong. "Roadway Network Model." In Encyclopedia of GIS, 979. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1141.

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Shekhar, Shashi, and Hui Xiong. "Spatial Network Model." In Encyclopedia of GIS, 1102. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1287.

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Conference papers on the topic "Network model"

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Burgueno, Loli, Jordi Cabot, and Sebastien Gerard. "An LSTM-Based Neural Network Architecture for Model Transformations." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00013.

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Martins, João, José M. Fonseca, Rafael Costa, José C. Campos, Alcino Cunha, Nuno Macedo, and José N. Oliveira. "Verification of railway network models with EVEREST." In MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3550355.3552439.

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Salem, Ahmed, Michael Backes, and Yang Zhang. "Get a Model! Model Hijacking Attack Against Machine Learning Models." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2022. http://dx.doi.org/10.14722/ndss.2022.23064.

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Johrendt, Jennifer L., and Peter R. Frise. "Neural Network Bushing Model Development Using Simulation." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28103.

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Neural networks are computationally efficient mathematical models that can be used to model quantitative and qualitative data. A neural network can be created through training with known input and output load-deflection data such that it learns to generalize the material characteristics without over-predicting the training data and losing its ability to anticipate behavior outside the training set. The challenge in creating a neural network model of a rubber bushing in a virtual model of a prototype assembly, for instance, is the lack of a physical prototype assembly. This paper describes a method by which data can be measured from a virtual prototype and used to define an appropriate data acquisition for the physical bushing. Training data can then be acquired using these guidelines and used for neural network model development. Subsequently, the enhanced model can then be used in the virtual simulation environment to increase the accuracy of the simulation results.
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Ferrandiz, Thomas, Fabrice Frances, and Christian Fraboul. "A Network Calculus Model for SpaceWire Networks." In 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). IEEE, 2011. http://dx.doi.org/10.1109/rtcsa.2011.42.

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Dahai Han, Kai Zhang, Xinyuan Lai, and Min Zhao. "Novel network simulation model of sensor networks with complex network charateristics." In IET International Conference on Communication Technology and Application (ICCTA 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0713.

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Shi, Zhanqun, Yibo Fan, Fengshou Gu, Abdul-Hannan Ali, and Andrew Ball. "Neural Network Modelling Applied for Model-Based Fault Detection." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58197.

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This paper aims to combine neural network modelling with model-based fault detection. An accurate and robust model is critical in model-based fault detection. However, the development of such a model is the most difficult task especially when a non-linear system is involved. The problem comes not only from the lack of concerned information about model parameters, but also from the inevitable linearization. In order to solve this problem, neural networks are introduced in this paper. Instead of using conventional neural network modelling, the neural network is only used to approximate the non-linear part of the system, leaving the linear part to be represented by a mathematical model. This new scheme of integration between neural network and mathematical model (NNMM) allows the compensation of the error from conventional modelling methods. Simultaneously, it keeps the residual signatures physically interpretable.
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Pikatza, Naiara, Izaskun Álvarez-Meaza, Rosa María Río-Belver, and Ernesto Cilleruelo. "Strategic Open Innovation model: mapping Iberdrola network." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11610.

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Companies are increasingly obliged to collaborate with each other if they wantto be innovative, and this growing transfer of knowledge takes place in acontext of Open Innovation. To study these scientific-technologicalcollaboration networks within an Open Innovation context, the case study ofIberdrola, a Spanish business group dedicated to the production, distributionand marketing of energy, has been chosen. Two methods have been used; thebibliometric method to analyze the Iberdrola scientific network, and patentdata analysis, to analyze the technological network. This has been achievedby using the Scopus and PatSeer databases, and refining the data withVantagePoint software. It was found that in both cases collaboration takesplace with the university, other companies, and research centers. Iberdrolahas an extensive scientific and technological collaboration networkthroughout the world. Both scientific and technological collaboration, despiteit not being common practice in companies, nevertheless, it can be concludedthat Iberdrola is committed to such collaboration in following with theguidelines of its organizational model based on Open Innovation.
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Schneider, Ben. "Automatic Network Configuration for Real-Time, Distributed Industrial Automation Systems." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00096.

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Karaduman, Burak, Sadaf Mustafiz, and Moharram Challenger. "FTG+PM for the Model-Driven Development of Wireless Sensor Network based IoT Systems." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00052.

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Reports on the topic "Network model"

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Yoo, Wucherl, and Alex Sim. Network Bandwidth Utilization Forecast Model on High Bandwidth Network. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1136782.

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Bierman, A., and M. Bjorklund. Network Configuration Access Control Model. RFC Editor, March 2018. http://dx.doi.org/10.17487/rfc8341.

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Berger, L., C. Hopps, A. Lindem, D. Bogdanovic, and X. Liu. YANG Data Model for Network Instances. RFC Editor, March 2019. http://dx.doi.org/10.17487/rfc8529.

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Berger, L., C. Hopps, A. Lindem, D. Bogdanovic, and X. Liu. YANG Model for Logical Network Elements. RFC Editor, March 2019. http://dx.doi.org/10.17487/rfc8530.

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Moreaux, Alexandre, Samantha Gamboa, David Griffith, and Richard Rouil. UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks. National Institute of Standards and Technology, April 2021. http://dx.doi.org/10.6028/nist.tn.2149.

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Makedonska, Nataliia, Elchin Jararov, and Lianjie Huang. Flow Simulation Using Discrete Fracture Network Model. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1469490.

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Zisiadis, D., S. Kopsidas, M. Tsavli, and G. Cessieux, eds. The Network Trouble Ticket Data Model (NTTDM). RFC Editor, February 2011. http://dx.doi.org/10.17487/rfc6137.

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Bierman, A., and M. Bjorklund. Network Configuration Protocol (NETCONF) Access Control Model. RFC Editor, March 2012. http://dx.doi.org/10.17487/rfc6536.

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Konrad, A. M., M. Perez, J. Rivera, Y. Rodriguez, M. J. Durst, D. W. Merrill, and H. H. Holmes. Investigation of network-based information system model. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/409897.

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Christakis, Nicholas, James Fowler, Guido Imbens, and Karthik Kalyanaraman. An Empirical Model for Strategic Network Formation. Cambridge, MA: National Bureau of Economic Research, May 2010. http://dx.doi.org/10.3386/w16039.

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