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Journal articles on the topic 'Parameters of network'

Author: Grafiati

Published: 28 June 2021

Last updated: 3 February 2022

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1

PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Romanov, Oleksandr, Ivan Saychenko, Anton Marinov, and Serhii Skolets. "RESEARCH OF SDN NETWORK PERFORMANCE PARAMETERS USING MININET NETWORK EMULATOR." Information and Telecommunication Sciences, no. 1 (June 29, 2021): 24–32. http://dx.doi.org/10.20535/2411-2976.12021.24-32.

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Background. The implementation of the new modern services on existing networks requires replacement or modernization of the old equipment. This negatively affects the efficiency of providing users with new types of services and their cost. Therefore, the issues/tasks of implementing SDN technology in the construction of networks are in the center of attention of representatives of science-research organizations and telecom operators. SDN networks have the advantages of flexible scaling of the system without replacing existing server and network equipment, eliminating operator dependence on specific vendor solutions, and rapid implementation of the new network technologies and services. To determine the numerical values of indicators of the functioning of SDN networks and check their compliance with the requirements, it is proposed to use in working process simulation modelling. For these goals, the Open Networking Foundation consortium developed the Mininet network emulator, the elements of that are published publicly. The formation of the simulation model and the procedure for determining the performance indicators of the SDN network that uses Mininet has a number of features that are considered in the work. Objective. The purpose of the article is to build a simulation model based on the Mininet network emulator and to determine the performance indicators of SDN networks of various structures/topologies. Methods. Creation of a SDN network segment for testing process of its functioning in the overload mode of various communication routes. Results. The Mininet network emulator allows simulating SDN networks of a rather complex structure, change the performance of network branches and the amount of load in communication routes, to create a network-wide overload mode. During the simulation, it is possible to determine a number of indicators of network performance, such as the RTT (Round Trip Time) parameter for each route of communication, the bandwidth of branches and routes of communication, the amount of delay on network elements, loading of OpenFlow Switch ports/interfaces, network elements with the highest delay, the number of served and lost packets. Conclusions. Use of the Mininet network emulator is a fairly convenient tool for determining the performance indicators of SDN networks. However, there are also some problems. First of all, setting up the system's operability requires writing programs to ensure the interaction among standard elements from the Mininet library. Secondone, is when congestion occurs in the communication routes, the Floodlight Controller does not automatically balance traffic along the work-around route. This task has to be solved manually by making changes to the OpenFlow Switch routing tables. Keywords: Software Defined Networking; SDN; Mininet; OpenFlow; Floodlight Controller; OpenFlowSwitch; NFV; virtualization.

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3

Zhou, Yang. "Research on Network Control Based on QoS of the Network." Advanced Materials Research 989-994 (July 2014): 4265–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4265.

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With the technology improvement of computer communication and multimedia coding, real time communication such as audio and video is introduced to networks and become a dominant way of communication.In the control of network, the optimization problem of the network controller based on network Quality of service (QoS) is a very important problem in the research of network control. Considering the influence of network quality—of service (QoS) on the control performance,a system model combining the network parameters and the control parameters is established for networked control systems (NCSs). Based on this, the condition dependent on the network parameters and control parameters is presented for the existence of guaranteed cost controllers.LMI).Within the scope of QoS perturbation,the designed controller can not only make the system as hypnotically stable but also guarantee that the system performance index is not greater than the upper bound.

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LIU, WEI-YI, and KUN YUE. "BAYESIAN NETWORK WITH INTERVAL PROBABILITY PARAMETERS." International Journal on Artificial Intelligence Tools 20, no. 05 (October 2011): 911–39. http://dx.doi.org/10.1142/s0218213011000449.

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Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditional interval probabilities and the probabilistic description, as well as the multiplication rules. Furthermore, we propose the method for learning the Bayesian network structure from interval data and the algorithm for corresponding approximate inferences. Experimental results show that our methods are feasible, and we conclude that the Bayesian network with interval probability parameters is the expansion of the general Bayesian network.

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Wang, Jia Jia, Yong Xiang Zhang, Wei Gong Zhang, and Hua Zhang Zhou. "Measuring Parameters of SpaceWire Network." Advanced Materials Research 159 (December 2010): 522–26. http://dx.doi.org/10.4028/www.scientific.net/amr.159.522.

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SpaceWire is used for on-board satellite networks. It is proposed by the ESA as the basis for future data-handing architectures. It takes an indispensable role for the shortage of bandwidth of bus among data processing. Based on the research of SpaceWire Network, some parameters, such as delay, bit error ratio, bandwidth and throughput, a new scheme of the testing about the parameters of SpaceWire was proposed. Then its implementation technology was analyzed and explained.

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6

Besfat, Henok M., Zelalem Hailu Gebeyehu, and Sudhir K. Routray. "Estimation of Parameters of 5G Network Dimensioning." International Journal of Electronics, Communications, and Measurement Engineering 10, no. 2 (July 2021): 15–32. http://dx.doi.org/10.4018/ijecme.2021070102.

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Cellular network traffic increases rapidly, and new services are introduced every year. For proper planning and design of such networks, exact requirements must be known with good accuracy. Dimensioning is an important part of network planning and design. Dimensioning is essential to determine the network requirements. In the coming years, fifth-generation (5G) will be deployed widely. 5G infrastructure is hybrid of wireless and optical components. For 5G network dimensioning, there is a need of a hybrid model. In this paper, the authors develop mathematical expressions for 5G network dimensioning. They use ITU proposed typical 5G network provisions to estimate bandwidth, network capacity, coverage, and capital expenditures. They also establish the correlation between the optical and the wireless parts. The expressions developed in this work can be used for the fast estimation of network coverage. So, this model can play important roles for 5G network planning and design.

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7

Chechin, G. V. "Select main parameters of Internet traffic exchange network." Issues of radio electronics 49, no. 5 (July 5, 2020): 10–16. http://dx.doi.org/10.21778/2218-5453-2020-5-10-16.

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The article considers the urgent problem of mathematical modeling of the Internet traffic exchange network. A mathematical model of an Internet traffic exchange network with an arbitrary number IX (Internet Exchange Point, IX, IXP) in the form of a Queuing network is presented. The model allows you to analyze alternative options for building a traffic exchange network and determine the optimal parameters of its structure: minimize the average delay and select the bandwidth of communication channels, the performance of traffic exchange nodes with restrictions on reliability and cost, and select the topology of the traffic exchange network taking into account the main parameters. Calculation examples are provided for Internet traffic exchange networks with a uniformly growing and substantially uneven loading of channels. We conclude that it is advisable to build networks with several Internet traffic exchange nodes.

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8

Buranova, M., and R. Latypov. "MPLS Network Parameters Analysis when Changing the Topology." Proceedings of Telecommunication Universities 5, no. 3 (2019): 6–12. http://dx.doi.org/10.31854/1813-324x-2019-5-3-6-12.

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One of the main tasks in network design is to evaluate the probability-time characteristics of the functioning of information and communication networks. Considering that the traffic of modern networks possesses the properties of self-similarity, we observe the difficulties while using the traditional mathematical apparatus of public serve theory. This paper presents us the results of jitter simulation estimating the delay, the probability of traffic loss when traffic processing in MPLS network. It was taken the real multimedia traffic and an on-off flow with a distribution of Pareto periods on and off as the researched flows. As the result, we discovered that when the network load increases or network size increases, jitter usually decreases, while the delay and loss probability increase.

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9

Ma, Yifang, and Zhiming Zheng. "Extracting principal parameters of complex networks." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550103. http://dx.doi.org/10.1142/s012918311550103x.

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The evolution of networks or dynamic systems is controlled by many parameters in high-dimensional space, and it is crucial to extract the reduced and dominant ones in low-dimensional space. Here we consider the network ensemble, introduce a matrix resolvent scale function and apply it to a spectral approach to get the similarity relations between each pair of networks. The concept of Diffusion Maps is used to get the principal parameters, and we point out that the reduced dimensional principal parameters are captured by the low order eigenvectors of the diffusion matrix of the network ensemble. We validate our results by using two classical network ensembles and one dynamical network sequence via a cooperative Achlioptas growth process where an abrupt transition of the structures has been captured by our method. Our method provides a potential access to the pursuit of invisible control parameters of complex systems.

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10

NITTA, TOHRU. "THE UNIQUENESS THEOREM FOR COMPLEX-VALUED NEURAL NETWORKS WITH THRESHOLD PARAMETERS AND THE REDUNDANCY OF THE PARAMETERS." International Journal of Neural Systems 18, no. 02 (April 2008): 123–34. http://dx.doi.org/10.1142/s0129065708001439.

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This paper will prove the uniqueness theorem for 3-layered complex-valued neural networks where the threshold parameters of the hidden neurons can take non-zeros. That is, if a 3-layered complex-valued neural network is irreducible, the 3-layered complex-valued neural network that approximates a given complex-valued function is uniquely determined up to a finite group on the transformations of the learnable parameters of the complex-valued neural network.

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11

Freitag, J., N. L. S. da Fonseca, and J. F. de Rezende. "Tuning of 802.11e network parameters." IEEE Communications Letters 10, no. 8 (August 2006): 611–13. http://dx.doi.org/10.1109/lcomm.2006.1665127.

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12

., Sampada D. Samudra. "QOS PARAMETERS FOR OBS NETWORK." International Journal of Research in Engineering and Technology 03, no. 11 (November 25, 2014): 407–11. http://dx.doi.org/10.15623/ijret.2014.0311068.

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13

Klinke, David J. "Signal Transduction Networks in Cancer: Quantitative Parameters Influence Network Topology." Cancer Research 70, no. 5 (February 28, 2010): 1773–82. http://dx.doi.org/10.1158/0008-5472.can-09-3234.

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14

Vishnu D Asal, Vishnu D. Asal, and Prof R. I. Patel Prof.R.I.Patel. "A Review on Prediction of EDM Parameters using Artificial Neural Network." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 145–49. http://dx.doi.org/10.15373/22778179/mar2013/46.

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15

Kenchannavar, Anita H., and Dr S. F. Rodd. "Fundamental Analysis of Financial Parameters and Ranking using Artificial Neural Network." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 134–39. http://dx.doi.org/10.9756/bijsesc.8260.

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16

HUSAINI, NOOR AIDA, ROZAIDA GHAZALI, NAZRI MOHD NAWI, and LOKMAN HAKIM ISMAIL. "THE EFFECT OF NETWORK PARAMETERS ON PI-SIGMA NEURAL NETWORK FOR TEMPERATURE FORECASTING." International Journal of Modern Physics: Conference Series 09 (January 2012): 440–47. http://dx.doi.org/10.1142/s2010194512005521.

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In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johor. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the network parameters which might affect the Neural Network's performance. Therefore, this study tends to explore the effect of network parameters by using Pi Sigma Neural Network (PSNN) with backpropagation algorithm. The network's performance is evaluated using the historical dataset of temperature in Batu Pahat for one step-ahead and benchmarked against Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have significantly affected the performance of PSNN for temperature forecasting. Towards the end of this paper, we concluded the best forecasting model to predict the temperature based on the comparison of our study.

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17

Li, Feng. "Measuring lexicographic product network efficiency with small time delay." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5301–9. http://dx.doi.org/10.3233/jifs-189015.

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The diameter and distance parameters of a network play very significant roles in analyzing the efficiency of a communication network, these parameters provide some efficient ways to measure information time delay in communication networks. We use the lexicographic product method to construct a larger network model, which is called the lexicographic product network by some specified small graphs. Network models based on the lexicographic product method contain these small graphs as sub-networks, and many desirable properties of these sub-networks are preserved. By using algebra graph theory, we investigated the diameter parameters of the lexicographic product network, and established an enumeration formula which only depends on the parameters of sub-networks. By analyzing the diameter formula and comparing it with other network models, it is proved that the lexicographic product network has a smaller time delay.

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18

Blewitt, Geoffrey. "Geodetic network optimization for geophysical parameters." Geophysical Research Letters 27, no. 22 (November 15, 2000): 3615–18. http://dx.doi.org/10.1029/1999gl011296.

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19

Shahat, Adel El. "Neural network storage unit parameters modelling." International Journal of Industrial Electronics and Drives 1, no. 4 (2014): 249. http://dx.doi.org/10.1504/ijied.2014.066215.

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20

Boulos, Paul F., and Don J. Wood. "Explicit Calculation of Pipe‐Network Parameters." Journal of Hydraulic Engineering 116, no. 11 (November 1990): 1329–44. http://dx.doi.org/10.1061/(asce)0733-9429(1990)116:11(1329).

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21

Scardoni, Giovanni, Michele Petterlini, and Carlo Laudanna. "Analyzing biological network parameters with CentiScaPe." Bioinformatics 25, no. 21 (September 2, 2009): 2857–59. http://dx.doi.org/10.1093/bioinformatics/btp517.

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22

Gupta, Rajesh, and Pramod R. Bhave. "Fuzzy parameters in pipe network analysis." Civil Engineering and Environmental Systems 24, no. 1 (March 2007): 33–54. http://dx.doi.org/10.1080/10286600601024822.

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23

Rahman, F., A. Kumar, G. Nagendra, and G. S. Gupta. "Network Approach for Physiological Parameters Measurement." IEEE Transactions on Instrumentation and Measurement 54, no. 1 (February 2005): 337–46. http://dx.doi.org/10.1109/tim.2004.834595.

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24

Tiwari, K. C. "Neural network parameters affecting image classification." Defence Science Journal 51, no. 3 (January 1, 2001): 263–78. http://dx.doi.org/10.14429/dsj.51.2237.

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25

Nghe, Philippe, Wim Hordijk, Stuart A. Kauffman, Sara I. Walker, Francis J. Schmidt, Harry Kemble, Jessica A. M. Yeates, and Niles Lehman. "Prebiotic network evolution: six key parameters." Molecular BioSystems 11, no. 12 (2015): 3206–17. http://dx.doi.org/10.1039/c5mb00593k.

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26

Garcia, R. B., M. W. N. Lobão, and C. T. Andrade. "Estimation of Agarose Network Characteristic Parameters." Polymer Reaction Engineering 2, no. 1-2 (January 1994): 1–15. http://dx.doi.org/10.1080/10543414.1994.10744447.

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27

Quadras, Jasintha, and Sajiya Merlin Mahizl Albert. "Domination Parameters in Coronene Torus Network." Mathematics in Computer Science 9, no. 2 (May 7, 2015): 169–75. http://dx.doi.org/10.1007/s11786-015-0222-4.

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28

Martovytskyi, Vitalii, Igor Ruban, Оleksandr Sievierinov, and Natalia Bolohova. "Selection of network infrastructure monitoring parameters to classify network status." Advanced Information Systems 2, no. 4 (December 26, 2018): 5–10. http://dx.doi.org/10.20998/2522-9052.2018.4.01.

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29

Al-namer, Zain-aalabdain. "Rationing the main parameters of the Quality of Network services." T-Comm 14, no. 11 (2020): 72–76. http://dx.doi.org/10.36724/2072-8735-2020-14-11-72-76.

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The European Telecommunications Standards Institute (ETSI) recommends that IP-telephony networks be divided into four classes according to the quality of service (quality of network services) QoS, the main indicator of which is packet delay (Y.1541). ITU-T Recommendation G.114 for public switched telephone networks provides delays close to ETSI gradations that correspond to different types of communication. The quality of the service from the user's point of view can be expressed by a set of indicators. These metrics are described in terms that are understandable to both the user and the service, and are independent of the network structure. Quality of service indicators are focused primarily on the effect perceived by the user, must be guaranteed to the user by the service and be objectively measured at the point of access to the service (ITU-T Recommendation I.350). ITU-T Recommendation E.862 provides possible approaches to accounting for the economic losses of the operator (in planning, design, operation and telecommunication network) and the user associated with technical failures. Operators of networks, working in market conditions, are interested in assessing possible losses due to failures and in comparing them with the costs of increasing the reliability of their funds.

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30

Li, Bing, Jie Zhuo, and Zheng Jia He. "Estimation of Crack Parameters through WFEM and Neural Network." Key Engineering Materials 413-414 (June 2009): 31–37. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.31.

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This paper presents the application of the wavelet finite element methods (WFEM) and neural network to crack parameters estimation and discusses the accuracy and efficiency of this method. The crack is presented by a rotational massless spring, and the natural frequencies for various crack parameters (location and depth) are obtained through WFEM. The neural network is then applied to establish the mapping relationship between the natural frequencies and the crack parameters, which uses feed-forward multiplayer neural networks trained by back-propagation, error-driven supervised training. With this trained neural network, the crack location and depth is estimated through using the measured natural frequencies as the input. The results of a cantilever beam experiment indicate that the estimating error of crack location is less than 3%, and the error of crack depth is less than 2%.

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31

Gattaz, Cristiane Chaves, Paulo Estevão Cruvinel, and Marcos Roberto Piscopo. "Semantic Parameters to Manage an Innovation Network Using Managing as Designing Approach: The Virtual Innovation Society Network Case." International Journal of Innovation 2, no. 1 (June 1, 2014): 45–64. http://dx.doi.org/10.5585/iji.v2i1.8.

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The most recent operations and management frameworks in innovation have not been complete to explicit required knowledge to manage the cooperation of its networked open innovation value chain in the knowledge economy and open enterprise. Strategic actors from the Virtual Innovation Society network were interviewed to identify critical semantic parameters that address this issue. As a result, this study suggests the characterization of inter-dependent added-values and its performance metrics, under the “managing as designing” approach, as input for managing the externalities, the integration of the articulation between business operations, strategy and information technology, and waste of innovation. In this context, the identification of the main managerial indicators for future command and control of existing innovation network operations under the “managing as designing” approach becomes a new challenge for future research. Keywords: Managing as Designing; Innovation Management; Network Managament; Operations Management; Virtual Networks.

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Kolesnikov, D. S., and D. A. Kuznetsov. "OPTIMAL UNIFORM QUANTIZATION OF PARAMETERS OF CONVOLUTIONAL NEURAL NETWORKS." Issues of radio electronics, no. 8 (August 20, 2018): 99–103. http://dx.doi.org/10.21778/2218-5453-2018-8-99-103.

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State of the art convolutional neural networks provide high accuracy in solving a wide range of problems. Usually it is achieved by a significant increasing their computational complexity and the representation of the network parameters in single-precision floating point numbers. However, due to the limited resources, the application of networks in embedded systems and mobile applications in real time is problematic. One of the methods to solve this problem is to reduce the bit depth of data and use integer arithmetic. For this purpose, the network parameters are quantized. Performing quantization, it is necessary to ensure a minimum loss of recognition accuracy. The article proposes to use an optimal uniform quantizer with an adaptive step. The quantizer step depends on the distribution function of the quantized parameters. It reduces the effect of the quantization error on the recognition accuracy. There are also described approaches to improving the quality of quantization. The proposed quantization method is estimated on the CIFAR-10 database. It is shown that the optimal uniform quantizer for CIFAR-10 database with 8-bit representation of network parameters allows to achieve the accuracy of the initial trained network.

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Hu, Jian Hua, and Yuan Hua Shuang. "A Reverse Approach in Optimizing Pass Parameters." Advanced Materials Research 113-116 (June 2010): 1707–11. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.1707.

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A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

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Jafari, Alireza, and Tayfun Babadagli. "A Sensitivity Analysis for Effective Parameters on 2D Fracture-Network Permeability." SPE Reservoir Evaluation & Engineering 12, no. 03 (May 31, 2009): 455–69. http://dx.doi.org/10.2118/113618-pa.

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Summary Fracture-network mapping and estimation of its permeability constitute two major steps in static-model preparation of naturally fractured reservoirs. Although several different analytical methods were proposed in the past for calculating fracture-network permeability (FNP), different approaches are still needed for practical use. We propose a new and practical approach to estimate FNP using statistical and fractal characteristics of fracture networks. We also provide a detailed sensitivity analysis to determine the relative importance of fracture-network parameters on the FNP in comparison to single-fracture conductivity using an experimental-design approach. The FNP is controlled by many different fracture-network parameters such as fracture length, density, orientation, aperture, and single-fracture connectivity. Five different 2D fracture data sets were generated for random and systematic orientations. In each data set, 20 different combinations of fracture density and length for different orientations were tested. For each combination, 10 different realizations were generated. The length was considered as constant and variable. This yielded a total of 1,000 trials. The FNPs were computed through a commercial discrete-fracture-network (DFN) modeling simulator for all cases. Then, we correlated different statistical and fractal characteristics of the networks to the measured FNPs using multivariable-regression analysis. Twelve fractal (sandbox, box counting, and scanline fractal dimensions) and statistical (average length, density, orientation, and connectivity index) parameters were tested against the measured FNP for synthetically generated fracture networks for a wide range of fracture properties. All cases were above the percolation threshold to obtain a percolating network, and the matrix effect was neglected. The correlation obtained through this analysis using four data sets was tested on the fifth one with known permeability for verification. High-quality match was obtained. Finally, we adopted an experimental-design approach to identify the most-critical parameters on the FNP for different fracture-network types. The results are presented as Pareto charts. It is believed that the new method and results presented in this paper will be useful for practitioners in static-model development of naturally fractured reservoirs and will shed light on further studies on modeling and understanding the transmissibility characteristics of fracture networks. It should be emphasized that this study was conducted on 2D fracture networks and could be extended to 3D models. This, however, requires further algorithm development to use 2D fractal characteristics for 3D systems and/or development of fractal measurement techniques for a 3D system. This study will provide a guideline for this type of research.

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35

Smith, H. Allison, and J. Geoffrey Chase. "Identification of Structural System Parameters Using the Cascade-Correlation Neural Network." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (December 1, 1994): 790–92. http://dx.doi.org/10.1115/1.2899280.

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The use of neural networks for structural system identification is receiving an increasing amount of attention through the research focused on structural control and intelligent systems. These systems require continuous monitoring and controlling of structural response; thus, on-line identification techniques are needed to provide real-time information about structural parameters. The Cascade-Correlation (Cascor) neural network is applied here to the structural system identification problem. The Cascor network utilizes a dynamic network architecture and a variable error threshold mechanism which facilitates training and can increase the network’s ability to generalize.

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Morse, Brian, Masoud Hessami, and Céline Bourel. "Mapping environmental conditions in the St. Lawrence River onto ice parameters using artificial neural networks to predict ice jams." Canadian Journal of Civil Engineering 30, no. 4 (August 1, 2003): 758–65. http://dx.doi.org/10.1139/l03-025.

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This paper evaluates the potential of using artificial neural networks to model ice parameters related to ice jams in the St. Lawrence River navigation channel through Lake St. Pierre. The artificial neural networks mapped environmental conditions onto ice parameters through multilayer feed-forward networks. The ice parameters include velocity, thickness, concentration, and unit discharge. The input to the network is based on two meteorological parameters: wind velocity and air temperature. The LevenbergMarquardt algorithm with Bayesian regularization is used to train the feed-forward network. The artificial neural networks adequately modelled the ice parameters. The predicted ice velocity, thickness, and unit discharge were very satisfactory, but ice concentration was not. Methods to improve forecasting (particularly of ice concentration) are suggested.Key words: ice parameters, ice jam, artificial neural network, ADCP, IPS.

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Du, Yan Ping, Fang Fang Chen, and Zhen Hua Zhang. "Analysis of Urban Rail Transit Based on Complex Network." Applied Mechanics and Materials 90-93 (September 2011): 770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.770.

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The development of urban rail transit plays the more and more important role in the public transit. It’s significant to analyze the urban rail transit network characteristics and its stability. The basic statistics parameters of Beijing metro network are computed in the paper based on the theory of complex networks, and its network characteristic is analyzed through the comparison to characteristic parameters of four typical networks.

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Singh, S., and A. K. Ghosh. "Estimation of lateral-directional parameters using neural networks based modified delta method." Aeronautical Journal 111, no. 1124 (October 2007): 659–67. http://dx.doi.org/10.1017/s0001924000004838.

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Abstract The aim of the study described herein was to develop and verify an efficient neural network based method for extracting aircraft stability and control derivatives from real flight data using feed-forward neural networks. The proposed method (Modified Delta method) draws its inspiration from feed forward neural network based the Delta method for estimating stability and control derivatives. The neural network is trained using differential variation of aircraft motion/control variables and coefficients as the network inputs and outputs respectively. For the purpose of parameter estimation, the trained neural network is presented with a suitably modified input file and the corresponding predicted output file of aerodynamic coefficients is obtained. An appropriate interpretation and manipulation of such input-output files yields the estimates of the parameter. The method is validated first on the simulated flight data using various combinations and types of real-flight control inputs and then on real flight data. A new technique is also proposed for validating the estimated parameters using feed-forward neural networks.

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Turaci, Tufan, and Hüseyin Aksan. "The Vulnerability of Some Networks including Cycles via Domination Parameters." Journal of Optimization 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4705493.

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LetG=(V(G),E(G))be an undirected simple connected graph. A network is usually represented by an undirected simple graph where vertices represent processors and edges represent links between processors. Finding the vulnerability values of communication networks modeled by graphs is important for network designers. The vulnerability value of a communication network shows the resistance of the network after the disruption of some centers or connection lines until a communication breakdown. The domination number and its variations are the most important vulnerability parameters for network vulnerability. Some variations of domination numbers are the 2-domination number, the bondage number, the reinforcement number, the average lower domination number, the average lower 2-domination number, and so forth. In this paper, we study the vulnerability of cycles and related graphs, namely, fans,k-pyramids, andn-gon books, via domination parameters. Then, exact solutions of the domination parameters are obtained for the above-mentioned graphs.

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Li, Xia. "The Parameters Measurement of the Network Performance." Applied Mechanics and Materials 380-384 (August 2013): 850–53. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.850.

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By using active method of injecting test flow data into the network the parameters of throughput, bandwidth, delay jitter, packet loss can be gained, UDP network performance of the single network segment can be tested. According to the measurement results, the network should be properly distributed for the access connection number and allocation of bandwidth for each client to improve the network performance.

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Purohit, Prathamesh, Ruturaj Kadikar, M. Susila, and B. Amutha. "Study of optimization parameters for service chaining in cloud environment." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 190. http://dx.doi.org/10.14419/ijet.v7i2.4.13037.

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and it is growing towards integration of 5G technology in near future. Therefore, to improve the quality of experience for viewing the content over the internet requires dynamic allocation and adaptation of network resources in an optimized manner. Traditional IP networks are vertically integrated hence flexibility in network resources management is very less. Software-defined networking (SDN) as an emerging technology, which comes with the promise of the solution to dynamically govern various network resources by breaking this chain or hierarchy of vertical integration. Network function virtualization along with service chain optimization provides the solution to enhance the Quality of Experience (QoE) and Quality of Service (QoS). In this paper, we are proposing an approach to improve the QoE by ameliorating the service chain and data center parameters.

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Kim, Nuri, Donghoon Lee, and Songhwai Oh. "Searching Effective Network Parameters to Construct Convolutional Neural Networks for Object Detection." Journal of KIISE 44, no. 7 (July 31, 2017): 668–73. http://dx.doi.org/10.5626/jok.2017.44.7.668.

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Ahuja, Kiran, Brahmjit Singh, and Rajesh Khanna. "Network selection algorithm based on link quality parameters for heterogeneous wireless networks." Optik 125, no. 14 (July 2014): 3657–62. http://dx.doi.org/10.1016/j.ijleo.2014.01.082.

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Kavithamani, A., V. Manikandan, and N. Devarajan. "Soft Fault Classification of Analog Circuits Using Network Parameters and Neural Networks." Journal of Electronic Testing 29, no. 2 (April 2013): 237–40. http://dx.doi.org/10.1007/s10836-013-5370-3.

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Sunitha R. and Chandrika J. "Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks." International Journal of Digital Crime and Forensics 13, no. 5 (September 2021): 130–44. http://dx.doi.org/10.4018/ijdcf.20210901.oa8.

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The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.

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Anish Soni and Rajneesh Randhawa. "Simulating and Analysing the Impact of Routing Protocols on Different Parameters of WSNs." Journal of Technology Management for Growing Economies 6, no. 1 (April 27, 2015): 27–37. http://dx.doi.org/10.15415/jtmge.2015.61003.

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Energy efficiency of Wireless Sensor Networks has become an essential requirement and is the main issue for researchers. Various routing, data dissemination and energy efficient protocols have been designed for Wireless Sensor Networks where energy issue has been given more stress. Sensors in wireless sensor networks work on battery and have limited energy. Hence, network has limited lifetime. Routing protocol plays a major role in deciding for how much time a network will survive. All routing algorithms tend to increase the lifetime of WSN while maintaining factors like successful and real-time delivery of a message. This paper aims towards studying different categories of routing protocols and finally four hierarchical routing protocols LEACH, EHRP, SEP and FAIR have been simulated. The performance of each routing protocol has been measured on some performance metrics like network lifetime, packets transferred to BS, number of dead nodes etc and finally concluded that how a routing protocol can impact the network lifetime.

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Stepins, Deniss, Gundars Asmanis, and Aivis Asmanis. "Measuring Capacitor Parameters Using Vector Network Analyzers." Electronics ETF 18, no. 1 (June 23, 2014): 29. http://dx.doi.org/10.7251/els1418029s.

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Toroshanko, O. S., and A. G. Zakharzhevskyi. "Control system parameters of wireless communication network." Telecommunication and information technologies 66, no. 1 (2020): 33–44. http://dx.doi.org/10.31673/2412-4338.2020.013344.

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Nijenhuis, K. Te. "Calculation of network parameters in thermoreversible gels." Polymer Gels and Networks 4, no. 5-6 (January 1996): 415–33. http://dx.doi.org/10.1016/s0966-7822(97)89915-7.

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Prinz, Astrid A., Dirk Bucher, and Eve Marder. "Similar network activity from disparate circuit parameters." Nature Neuroscience 7, no. 12 (November 21, 2004): 1345–52. http://dx.doi.org/10.1038/nn1352.

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