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Journal articles on the topic 'Optimizing the network'

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1

O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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2

Paluzo-Hidalgo, Eduardo, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, and Jónathan Heras. "Optimizing the Simplicial-Map Neural Network Architecture." Journal of Imaging 7, no. 9 (2021): 173. http://dx.doi.org/10.3390/jimaging7090173.

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Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage.
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Mackin, Erika, and Stacy Patterson. "Optimizing the Coherence of a Network of Networks." IEEE Transactions on Control of Network Systems 7, no. 3 (2020): 1465–75. http://dx.doi.org/10.1109/tcns.2020.2979884.

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4

Yan, Houyi, Lvlin Hou, Yunxiang Ling, and Guohua Wu. "Optimizing complex networks controllability by local structure information." International Journal of Modern Physics C 27, no. 10 (2016): 1650115. http://dx.doi.org/10.1142/s0129183116501151.

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Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure
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Monk, Jonathan, Juan Nogales, and Bernhard O. Palsson. "Optimizing genome-scale network reconstructions." Nature Biotechnology 32, no. 5 (2014): 447–52. http://dx.doi.org/10.1038/nbt.2870.

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Zhang, Xiaoxia, Zhongming Zheng, Qinghua Shen, Jing Liu, Xuemin Sherman Shen, and Liang-Liang Xie. "Optimizing Network Sustainability and Efficiency in Green Cellular Networks." IEEE Transactions on Wireless Communications 13, no. 2 (2014): 1129–39. http://dx.doi.org/10.1109/twc.2013.122713.131133.

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7

Kobayashi, Kunizaku, Toyoshi Torioka, and Nobuo Yoshida. "A wavelet neural network with network optimizing function." Systems and Computers in Japan 26, no. 9 (1995): 61–71. http://dx.doi.org/10.1002/scj.4690260906.

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8

Farshad, Nasimi Khameneh, and Syuhaimi Ab Rahman Mohammad. "Optimizing In-Vehicle Multiplexed Network Using WDM over POF." Applied Mechanics and Materials 663 (October 2014): 675–81. http://dx.doi.org/10.4028/www.scientific.net/amm.663.675.

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The past decade was huge challenge for vehicle industries to upgrade their on-board networks using several software technologies (multiplexed networks) but the hardware still is untouched. Now in-vehicle network is a complex of tens electronic controllers and hundreds sensors and actuators. Even telecommunication and multimedia became inevitable vehicle’s features with increasing day to day demands. Today copper wires are the constant option as transmission medium in current vehicles networks and they cannot provide proper bandwidth for such high speed applications. Although the MOST (Media Or
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9

Virmani, Dr Deepali. "Maximizing Network Lifetime Operator for Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2013): 267–72. http://dx.doi.org/10.24297/ijct.v4i2a2.3172.

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Optimizing and enhancing network lifetime with minimum energy consumption is the major challenge in field of wireless sensor networks. Existing techniques for optimizing network lifetime are based on exploiting node redundancy, adaptive radio transmission power and topology control. Topology control protocols have a significant impact on network lifetime, available energy and connectivity. In this paper we categorize sensor nodes as strong and weak nodes based on their residual energy as well as operational lifetime and propose a Maximizing Network lifetime Operator (MLTO) that defines cluster
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10

Et. al., Alyaa Abdulmunem M. Al-Najjar,. "Optimizing MANETs Network Lifetime Using a Proactive Clustering Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 3280–92. http://dx.doi.org/10.17762/turcomat.v12i2.2387.

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In wireless sensor networks that consist of a number of power constrained sensor nodes, the foremost challenges are the limited energy and system lifetime. Therefore, designing efficient routing protocols, which prolong the network lifetime, is one of the most critical issues. This paper evaluated several clustering algorithms, namely: Highest Degree Clustering Algorithm (HDCA), and Lowest Identifier Clustering Algorithm (LIDCA) under three metrics: throughput, Packets Delivered Ratio Factor (PDR) and network lifetime. One of the most important challenges facing Mobile Ad hoc Networks is savin
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11

Cai, Lin. "Research of Optimizing Computer Network Based on Dynamism Theory." Applied Mechanics and Materials 556-562 (May 2014): 5356–58. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5356.

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With the fast development of computer network services, the needs of every service is developing. To guarantee a effective computer network service we should improve the key quality of service (QoS) of each computer network service. Related QoS evaluating indexes mainly include throughput, delay time and packet loss of network. A optimizing method offers a theory support to a better computer network service quality and dynamic optimization is one of the most popular theoretical tools to study resource allocation and task scheduling problems in computer systems and computer networks. This text
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12

Rouhieh, Behzad, and Ciprian Alecsandru. "Optimizing route choice in multimodal transportation networks." Canadian Journal of Civil Engineering 41, no. 9 (2014): 800–810. http://dx.doi.org/10.1139/cjce-2013-0331.

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Advanced traveler information systems provide travelers with pre-trip and en route travel information necessary to improve the trip decision making process based on various criteria (e.g., avoiding the negative impacts of traffic congestion, selecting specific travel modes, etc.). This study investigates an adaptive routing methodology for multimodal transportation networks. To integrate transit networks, the model takes into account both the predefined timetables of public transportation services and the variability of travel times. A graph theory based methodology is proposed to capture trav
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13

Mohammadi, Ramin, and Ali Ghaffari. "Optimizing Reliability through Network Coding in Wireless Multimedia Sensor Networks." Indian Journal of Science and Technology 8, no. 9 (2015): 834. http://dx.doi.org/10.17485/ijst/2015/v8i9/56039.

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14

Wu, Jiajing, Chi K. Tse, and Francis C. M. Lau. "Optimizing Performance of Communication Networks: An Application of Network Science." IEEE Transactions on Circuits and Systems II: Express Briefs 62, no. 1 (2015): 95–99. http://dx.doi.org/10.1109/tcsii.2014.2362740.

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15

Boava, Adao, and Yuzo Iano. "Optimizing DSL network through new architectures level 2 communication networks." IEEE Latin America Transactions 14, no. 1 (2016): 380–85. http://dx.doi.org/10.1109/tla.2016.7430104.

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16

Ponnachan, Gargy. "Optimizing Sensor Network in Sustainable City." International Journal of Computing, Communications and Networking 9, no. 2 (2020): 11–15. http://dx.doi.org/10.30534/ijccn/2020/03922019.

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17

Benardos, P. G., and G. C. Vosniakos. "Optimizing feedforward artificial neural network architecture." Engineering Applications of Artificial Intelligence 20, no. 3 (2007): 365–82. http://dx.doi.org/10.1016/j.engappai.2006.06.005.

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18

Lvovich, I. Ya, A. P. Preobrazhenskiy, and O. N. Choporov. "Modelling and optimizing engineering network systems." IOP Conference Series: Materials Science and Engineering 687 (December 10, 2019): 044026. http://dx.doi.org/10.1088/1757-899x/687/4/044026.

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19

Malaguti, Enrico, Giacomo Nannicini, and Dimitri Thomopulos. "Optimizing allocation in a warehouse network." Electronic Notes in Discrete Mathematics 64 (February 2018): 195–204. http://dx.doi.org/10.1016/j.endm.2018.01.021.

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20

Kamiyama, Noriaki, Ryoichi Kawahara, Tatsuya Mori, Shigeaki Harada, and Haruhisa Hasegawa. "Parallel video streaming optimizing network throughput." Computer Communications 34, no. 10 (2011): 1182–94. http://dx.doi.org/10.1016/j.comcom.2010.12.003.

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21

Wang, Xiao, and Linying Xiang. "Optimizing Network Controllability with Minimum Cost." Complexity 2021 (January 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/6657307.

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In this paper, the issue of optimally modifying the structure of a directed network to guarantee its structural controllability is investigated. Given a directed network, in order to obtain a structurally controllable system, a framework for finding the minimum number of directed edges that need to be added to the network is proposed. After we get these edge-addition configurations, we further calculate the network cost of each optimization scheme and choose the one with the minimum cost. Our main contribution is twofold: first, we provide an algorithm able to find all optimal network modifica
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22

Samizadeh, Reza. "An integrated model for optimizing distribution network with considering assembly line balancing." International Academic Journal of Science and Engineering 06, no. 01 (2019): 178–87. http://dx.doi.org/10.9756/iajse/v6i1/1910017.

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23

Mochalin, S. M., and J. A. Koleber. "URBAN PASSENGER TRANSPORT ROAD NETWORKS: PROSPECTS OF METHODS’ OPTIMIZATION." Russian Automobile and Highway Industry Journal 16, no. 3 (2019): 241–55. http://dx.doi.org/10.26518/2071-7296-2019-3-241-255.

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Introduction.City passenger transport plays an important role in life of the population of the city and in ensuring the efficient, uninterrupted operation of the entire urban system as a whole. However, currently in the field of urban passenger transport in many cities of Russia a number of significant problems have accumulated. In particular, these are problems of the development of the urban route network, the performance indicators of which determine the level of quality of transport services for the population and the economic effect of the operation of urban passenger transport. In this c
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24

Bachiller, Pilar, and Julia González. "Optimizing and Learning Algorithm for Feed-forward Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 1 (2001): 51–57. http://dx.doi.org/10.20965/jaciii.2001.p0051.

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Feed-forward neural networks have emerged as a good solution for many problems, such as classification, recognition and identification, and signal processing. However, the importance of selecting an adequate hidden structure for this neural model should not be underestimated. When the hidden structure of the network is too large and complex for the model being developed, the network may tend to memorize input and output sets rather than learning relationships between them. Such a network may train well but test poorly when inputs outside the training set are presented. In addition, training ti
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25

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 pa
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26

Qi, Zhongang, Saeed Khorram, and Li Fuxin. "Visualizing Deep Networks by Optimizing with Integrated Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11890–98. http://dx.doi.org/10.1609/aaai.v34i07.6863.

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Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is
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27

Coughlin, Michael W., Sarah Antier, David Corre, et al. "Optimizing multitelescope observations of gravitational-wave counterparts." Monthly Notices of the Royal Astronomical Society 489, no. 4 (2019): 5775–83. http://dx.doi.org/10.1093/mnras/stz2485.

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ABSTRACT The ever-increasing sensitivity of the network of gravitational-wave detectors has resulted in the accelerated rate of detections from compact binary coalescence systems in the third observing run of Advanced LIGO and Advanced Virgo. Not only has the event rate increased, but also the distances to which phenomena can be detected, leading to a rise in the required sky volume coverage to search for counterparts. Additionally, the improvement of the detectors has resulted in the discovery of more compact binary mergers involving neutron stars, revitalizing dedicated follow-up campaigns.
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28

GUO, Tao, Xu ZHOU, Zhi-ping WANG, and Hui TANG. "Mechanism for optimizing P2P traffic between-networks based on network measurement." Journal of Computer Applications 30, no. 4 (2010): 888–91. http://dx.doi.org/10.3724/sp.j.1087.2010.00888.

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29

Kharche, Shubhangi, and Sanjay Pawar. "Optimizing network lifetime and QoS in 6LoWPANs using deep neural networks." Computers & Electrical Engineering 87 (October 2020): 106775. http://dx.doi.org/10.1016/j.compeleceng.2020.106775.

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30

Hébuterne, Gérard, and Jean-Yves Leboudec. "Network design and optimization Building, optimizing and dimensioning networks of tomorrow." Annales Des Télécommunications 56, no. 3-4 (2001): 111–12. http://dx.doi.org/10.1007/bf03002695.

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31

Noutsawo, D. E. H. "OPTIMIZING AN AFRICAN NETWORK: CASE of VIPNET." ELECTRICAL AND COMPUTER SYSTEMS 27, no. 103 (2018): 220–26. http://dx.doi.org/10.15276/eltecs.27.103.2018.25.

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32

Men, Hong, Hai Yan Liu, Lei Wang, and Yun Peng Pan. "An Optimizing Method of Competitive Neural Network." Key Engineering Materials 467-469 (February 2011): 894–99. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.894.

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This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value,and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.
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33

Gogoi, Munmi, Ashim Jyoti Gogoi, and Shahin Ara Begum. "Optimizing Error Function of Backpropagation Neural Network." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1011–16. http://dx.doi.org/10.26438/ijcse/v7i4.10111016.

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34

Lai, Yung-Cheng (Rex), Mark H. Dingler, Chung-En Hsu, and Pei-Chun Chiang. "Optimizing Train Network Routing with Heterogeneous Traffic." Transportation Research Record: Journal of the Transportation Research Board 2159, no. 1 (2010): 69–76. http://dx.doi.org/10.3141/2159-09.

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35

Li, Ping, He Yan Li, Ling Ling Tang, Jun Li Li, and Dong Yang Guo. "Analysis on Optimizing of Antenna Feeding Network." Applied Mechanics and Materials 135-136 (October 2011): 1168–70. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.1168.

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A new feeding form is designed as a discontinued T-shaped in this paper. This paper also models a function with restrict conditions, a set of data is educed by optimizing nonlinear function, which is transformed through adding penalty function. At last, when the two-set coaxial impedance matching apparatus is fixed on the antenna, a comparison of simulator between the impedance and without it indicates that the matching results are satisfactory.
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36

De Vico Fallani, Fabrizio, and Danielle S. Bassett. "Network neuroscience for optimizing brain–computer interfaces." Physics of Life Reviews 31 (December 2019): 304–9. http://dx.doi.org/10.1016/j.plrev.2018.10.001.

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37

Wang, Gang, Gang Feng, Shuang Qin, Ruihan Wen, and Sanshan Sun. "Optimizing Network Slice Dimensioning via Resource Pricing." IEEE Access 7 (2019): 30331–43. http://dx.doi.org/10.1109/access.2019.2902432.

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38

Lytton, William W. "Optimizing Synaptic Conductance Calculation for Network Simulations." Neural Computation 8, no. 3 (1996): 501–9. http://dx.doi.org/10.1162/neco.1996.8.3.501.

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High computational requirements in realistic neuronal network simulations have led to attempts to realize implementation efficiencies while maintaining as much realism as possible. Since the number of synapses in a network will generally far exceed the number of neurons, simulation of synaptic activation may be a large proportion of total processing time. We present a consolidating algorithm based on a recent biophysically-inspired simplified Markov model of the synapse. Use of a single lumped state variable to represent a large number of converging synaptic inputs results in substantial speed
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39

Lieberman, Edward, and Jinil Chang. "Optimizing Traffic Signal Timing through Network Decomposition." Transportation Research Record: Journal of the Transportation Research Board 1925, no. 1 (2005): 167–75. http://dx.doi.org/10.1177/0361198105192500117.

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A signal control system named Real-Time/Internal Metering Policy to Optimize Signal Timing (RT/IMPOST) has been under development for several years. It is designed to compute signal timing plans for the entire range of operating conditions from under- to oversaturation for control systems ranging from first generation (GEN 1) to highly responsive advanced traffic management systems. The different flow regimes of urban traffic control are reviewed. Then the focus is on the treatment to develop cycle-based signal timing plans for inclusion in the data libraries referenced by GEN 1 real-time traf
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Yang, Lixing, Keping Li, Ziyou Gao, and Xiang Li. "Optimizing trains movement on a railway network." Omega 40, no. 5 (2012): 619–33. http://dx.doi.org/10.1016/j.omega.2011.12.001.

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41

Billionnet, Alain. "Integer programming for optimizing habitat network permeability." Management of Environmental Quality: An International Journal 21, no. 5 (2010): 570–88. http://dx.doi.org/10.1108/14777831011067890.

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42

Yin, Keting, Bo Zhou, Shuai Zhang, Honghong Jiang, and Jerry Cristoforo. "Optimizing Services Composition in Multi-Network Environment." Information Technology Journal 9, no. 3 (2010): 399–411. http://dx.doi.org/10.3923/itj.2010.399.411.

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43

Jung-Hua Wang, Jen-Da Rau, and Chung-Yun Peng. "Toward optimizing a self-creating neural network." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 30, no. 4 (2000): 586–93. http://dx.doi.org/10.1109/3477.865177.

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44

Androshchuk, Vasilii, Konstantin Andreev, Iurii Panov, Vyacheslav Terentyev, Alexander Shemyakin, and Aleksandr Ivanov. "Optimizing the route network of the city." IOP Conference Series: Materials Science and Engineering 918 (October 7, 2020): 012056. http://dx.doi.org/10.1088/1757-899x/918/1/012056.

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45

Rangarajan, Anand, Steven Gold, and Eric Mjolsness. "A Novel Optimizing Network Architecture with Applications." Neural Computation 8, no. 5 (1996): 1041–60. http://dx.doi.org/10.1162/neco.1996.8.5.1041.

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We present a novel optimizing network architecture with applications in vision, learning, pattern recognition, and combinatorial optimization. This architecture is constructed by combining the following techniques: (1) deterministic annealing, (2) self-amplification, (3) algebraic transformations, (4) clocked objectives, and (5) softassign. Deterministic annealing in conjunction with self-amplification avoids poor local minima and ensures that a vertex of the hypercube is reached. Algebraic transformations and clocked objectives help partition the relaxation into distinct phases. The problems
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46

Li, KePing, ZiYou Gao, BaoHua Mao, and ChengXuan Cao. "Optimizing Train Network Routing Using Deterministic Search." Networks and Spatial Economics 11, no. 2 (2009): 193–205. http://dx.doi.org/10.1007/s11067-009-9098-7.

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47

Lozano, Manuel, Carlos García-Martínez, Francisco J. Rodríguez, and Humberto M. Trujillo. "Optimizing network attacks by artificial bee colony." Information Sciences 377 (January 2017): 30–50. http://dx.doi.org/10.1016/j.ins.2016.10.014.

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48

Yang, Shu-xia, Xiang Li, Ning Li, and Shang-dong Yang. "Optimizing neural network forecast by immune algorithm." Journal of Central South University of Technology 13, no. 5 (2006): 573–76. http://dx.doi.org/10.1007/s11771-006-0090-9.

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49

Zheng, Xiaoying, and Ye Xia. "Optimizing network objectives in collaborative content distribution." Computer Networks 91 (November 2015): 244–61. http://dx.doi.org/10.1016/j.comnet.2015.08.013.

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50

Saad, Dimitri M., Rana A. Bilbeisi, and Sabla Y. Alnouri. "Optimizing network pathways of CO2 conversion processes." Journal of CO2 Utilization 45 (March 2021): 101433. http://dx.doi.org/10.1016/j.jcou.2020.101433.

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