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Journal articles on the topic 'Topology Control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

HAN, Shuang-xia, Yi-ming FAN, Lu ZHANG, and Fu-rong LUO. "Three-layer topology architecture and topology control algorithm." Journal of Computer Applications 29, no. 6 (August 5, 2009): 1523–26. http://dx.doi.org/10.3724/sp.j.1087.2009.01523.

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2

Christensen, René Depont, and Hans Jørgen Munkholm. "Topology with monoidal control." Homology, Homotopy and Applications 4, no. 1 (2002): 213–34. http://dx.doi.org/10.4310/hha.2002.v4.n1.a12.

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3

Osborne, Ian S. "Nonlinear control of topology." Science 372, no. 6537 (April 1, 2021): 43.18–45. http://dx.doi.org/10.1126/science.372.6537.43-r.

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4

Lorenzo, Beatriz, and Savo Glisic. "Traffic adaptive relaying topology control." IEEE Transactions on Wireless Communications 8, no. 11 (November 2009): 5612–20. http://dx.doi.org/10.1109/twc.2009.081613.

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5

Liu, Ren Ping, Glynn Rogers, Sihui Zhou, and John Zic. "Topology control with Hexagonal Tessellation." International Journal of Sensor Networks 2, no. 1/2 (2007): 91. http://dx.doi.org/10.1504/ijsnet.2007.012987.

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6

Tereshchenko, T. О., Y. S. Yamnenko, D. V. Kuzin, and L. E. Klepach. "MULTILEVEL INVERTER TOPOLOGY AND CONTROL SIGNALS DEFINITION BASED ON ORTHOGONAL SPECTRAL TRANSFORMATIONS." Tekhnichna Elektrodynamika 2018, no. 4 (May 15, 2018): 57–60. http://dx.doi.org/10.15407/techned2018.04.057.

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7

M.S, Brindha. "A Survey on Cross Layer Distributed Topology Control in Mobile Adhoc Network." Bonfring International Journal of Networking Technologies and Applications 04, no. 01 (October 31, 2017): 01–03. http://dx.doi.org/10.9756/bijnta.8346.

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8

Karunakaran. "Topology Control Using Efficient Power Management." Journal of Computer Science 7, no. 4 (April 1, 2011): 561–67. http://dx.doi.org/10.3844/jcssp.2011.561.567.

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9

Escobedo, Adolfo R., Erick Moreno-Centeno, and Kory W. Hedman. "Topology Control for Load Shed Recovery." IEEE Transactions on Power Systems 29, no. 2 (March 2014): 908–16. http://dx.doi.org/10.1109/tpwrs.2013.2286009.

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10

Neumann, Florentin, and Hannes Frey. "Foundation of Reactive Local Topology Control." IEEE Communications Letters 19, no. 7 (July 2015): 1213–16. http://dx.doi.org/10.1109/lcomm.2015.2432019.

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11

Mojica-Nava, Eduardo, Jimmy Salgado, Duvan Tellez, and Alvaro Lopez. "Optimal Control of Switching Topology Networks." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/268541.

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We present an extension of a previously proposed approach based on the method of moments for solving the optimal control problem for a switching system considering now a continuous external input. This method is based on the transformation of a nonlinear, nonconvex optimal control problem, into an equivalent optimal control problem with linear and convex structure, which allows us to obtain an equivalent convex formulation more appropriate to be solved by high-performance numerical computing. Finally, the design of optimal logic-based controllers for networked systems with a dynamic topology is presented as an application of this work.

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12

ZHANG, Xue. "Topology Control for Wireless Sensor Networks." Journal of Software 18, no. 4 (2007): 943. http://dx.doi.org/10.1360/jos180943.

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13

Ritter, Helge, and Klaus Schulten. "Topology-conserving maps for motor control." Neural Networks 1 (January 1988): 357. http://dx.doi.org/10.1016/0893-6080(88)90385-1.

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14

Borkar, Vivek S., and D. Manjunath. "Distributed topology control of wireless networks." Wireless Networks 14, no. 5 (January 10, 2007): 671–82. http://dx.doi.org/10.1007/s11276-006-0008-3.

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15

de Ruiter, M. J., and F. van Keulen. "Topology optimization using a topology description function." Structural and Multidisciplinary Optimization 26, no. 6 (April 1, 2004): 406–16. http://dx.doi.org/10.1007/s00158-003-0375-7.

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16

Zhang, Wei, Hong Ma, Tao Wu, Xueshu Shi, and Yiwen Jiao. "Efficient topology control for time-varying spacecraft networks with unreliable links." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771987937. http://dx.doi.org/10.1177/1550147719879377.

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In spacecraft networks, the time-varying topology, intermittent connectivity, and unreliable links make management of the network challenging. Previous works mainly focus on information propagation or routing. However, with a large number of nodes in the future spacecraft networks, it is very crucial regarding how to make efficient network topology controls. In this article, we investigate the topology control problem in spacecraft networks where the time-varying topology can be predicted. We first develop a model that formalizes the time-varying spacecraft network topologies as a directed space–time graph. Compared with most existing static graph models, this model includes both temporal and spatial topology information. To capture the characteristics of practical network, links in our space–time graph model are weighted by cost, efficiency, and unreliability. The purpose of our topology control is to construct a sparse (low total cost) structure from the original topology such that (1) the topology is still connected over space–time graph; (2) the cost efficiency ratio of the topology is minimized; and (3) the unreliability parameter is lower than the required bound. We prove that such an optimization problem is NP-hard. Then, we provide five heuristic algorithms, which can significantly maintain low topology cost efficiency ratio while achieving high reliable connectivity. Finally, simulations have been conducted on random space networks and hybrid low earth orbit/geostationary earth orbit satellite-based sensor network. Simulation results demonstrate the efficiency of our model and topology control algorithms.

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17

Mudali, Pragasen, and Matthew Olusegun Adigun. "Context-Based Topology Control for Wireless Mesh Networks." Mobile Information Systems 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/9696348.

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Topology Control has been shown to provide several benefits to wireless ad hoc and mesh networks. However these benefits have largely been demonstrated using simulation-based evaluations. In this paper, we demonstrate the negative impact that the PlainTC Topology Control prototype has on topology stability. This instability is found to be caused by the large number of transceiver power adjustments undertaken by the prototype. A context-based solution is offered to reduce the number of transceiver power adjustments undertaken without sacrificing the cumulative transceiver power savings and spatial reuse advantages gained from employing Topology Control in an infrastructure wireless mesh network. We propose the context-based PlainTC+ prototype and show that incorporating context information in the transceiver power adjustment process significantly reduces topology instability. In addition, improvements to network performance arising from the improved topology stability are also observed. Future plans to add real-time context-awareness to PlainTC+ will have the scheme being prototyped in a software-defined wireless mesh network test-bed being planned.

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18

Behera, Rashmi Ranjan, and Amarnath Thakur. "Finite-Control-Set Predictive Current Control Based Real and Reactive Power Control of Grid-Connected Hybrid Modular Multilevel Converter." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 2 (June 1, 2018): 660. http://dx.doi.org/10.11591/ijpeds.v9.i2.pp660-667.

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<p>This paper proposes the grid application of modified three-phase topology of Modular Multilevel Converter (MMC) using finite-control-set predictive control. This topology has reduced number of switch counts compared to the conventional MMC, eliminates the problem of circulating current and having higher efficiency. A single dc source is required to produce sinusoidal outputs. The number of sub-modules (SMs) in this topology is half of the SMs required in case of MMC, in addition to a single H-bride circuit per phase. The finite-control-set predictive current control scheme for the grid connected dc source through the Hybrid Modular Multilevel Converter (HMMC). This controller controls the desired real and reactive power demand of the grid instantaneously. The simulation study of a three phase grid connected system has been done in Matlab/Simulink and the results are provided for the different real and reactive power demands, to validate the concepts.</p>

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19

Han, Shuang Xia, Lu Zhang, and Jian Wen Fang. "An Improved Control for Large-Scale Wireless Sensor Networks." Key Engineering Materials 474-476 (April 2011): 2315–19. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2315.

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A 3-layer topology is proposed to solve the problem of the incompatibility of the traditional topology structure in large-scale WSN. The data communication strategy for each level have been analysed, and an topology control algorithm for top-level is brought up based on the bottleneck-nodes, which will provide higher reliability control for the key-level. The experimental results indicated that, the new topology control strategy will contribute to balance the communication load of the nodes, and the energy consumption in the key-level reduce remarkably.

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20

Park, Seongjoon, Hyeong Tae Kim, and Hwangnam Kim. "Energy-Efficient Topology Control for UAV Networks." Energies 12, no. 23 (November 27, 2019): 4523. http://dx.doi.org/10.3390/en12234523.

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Following striking developments in Unmanned Aerial Vehicle (UAV) technology, the use of UAVs has been researched in various industrial fields. Furthermore, a number of studies on operating multiple autonomous networking UAVs suggest a potential to use UAVs in large-scale environments. To achieve efficiency of performance in multi-UAV operations, it is essential to consider a variety of factors in UAV network conditions, such as energy efficiency, network overhead, and so on. In this paper, we propose a novel scheme that improves the energy efficiency and network throughputs by controlling the topology of the network. Our proposed network topology control scheme functions between the data link layer (L3) and the network layer (L2). Accordingly, it can be considered to be layer 2.5 in the network hierarchy model. In addition, our methodology includes swarm intelligence, meaning that whole topology control can be generated with less cost and effort, and without a centralized controller. Our experimental results confirm the notable performance of our proposed method compared to previous approaches.

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21

Gateau, G., T. A. Meynard, L. Delmas, and H. Foch. "Stacked Multicell Converter (SMC): Topology and Control." EPE Journal 12, no. 2 (May 2002): 14–18. http://dx.doi.org/10.1080/09398368.2002.11463500.

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22

Krishna, K. Hari, Y. Suresh Babu, and Tapas Kumar. "Wireless Sensor Network Topology Control Sing Clustering." Procedia Computer Science 79 (2016): 893–902. http://dx.doi.org/10.1016/j.procs.2016.03.106.

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23

Hu, L. "Topology control for multihop packet radio networks." IEEE Transactions on Communications 41, no. 10 (1993): 1474–81. http://dx.doi.org/10.1109/26.237882.

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24

Pasquale, Liliana, Carlo Ghezzi, Edoardo Pasi, Christos Tsigkanos, Menouer Boubekeur, Blanca Florentino-Liano, Tarik Hadzic, and Bashar Nuseibeh. "Topology-Aware Access Control of Smart Spaces." Computer 50, no. 7 (2017): 54–63. http://dx.doi.org/10.1109/mc.2017.189.

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25

Chen, J., J. E. Mueller, Y. Zhang, S. M. Du, Y. Wang, T. J. Fu, H. Wang, H. Qiu, S. Zhang, and N. C. Seeman. "The control of DNA structure and topology." Acta Crystallographica Section A Foundations of Crystallography 49, s1 (August 21, 1993): c134. http://dx.doi.org/10.1107/s0108767378096129.

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26

Ruiz, Pablo A., Justin M. Foster, Aleksandr Rudkevich, and Michael C. Caramanis. "Tractable Transmission Topology Control Using Sensitivity Analysis." IEEE Transactions on Power Systems 27, no. 3 (August 2012): 1550–59. http://dx.doi.org/10.1109/tpwrs.2012.2184777.

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27

Korad, Akshay S., and Kory W. Hedman. "Robust Corrective Topology Control for System Reliability." IEEE Transactions on Power Systems 28, no. 4 (November 2013): 4042–51. http://dx.doi.org/10.1109/tpwrs.2013.2267751.

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28

Weber, Gerhard-W. "On the topology of parametric optimal control." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 39, no. 4 (April 1998): 463–97. http://dx.doi.org/10.1017/s033427000000775x.

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AbstractWe will study one-parameter families of differentiable optimal control problems given by:Here, at given times t the inequality constraint functions are of semi-infinite nature, the objective functional may also be of max-type. For each s ∈ ℝ the problem is equivalent to a one-parameter family (Ps (t))t∈[a,b] of differentiable optimization problems. From these the consideration of generalized critical trajectories, such as a local minimum trajectory, comes into our investigation. According to a concept introduced by Hettich, Jongen and Stein in optimization, we distinguish eight types of generalized critical trajectories. Under suitable continuity, compactness and integrability assumptions, those problems, which exclusively have generalized critical points being of one of these eight types, are generic. We study normal forms and characteristic examples, locally around these trajectories.Moreover, we indicate the related concept of structural stability of optimal control problems due to the topological behaviour of the lower level sets under small data perturbations. Finally, we discuss the numerical consequences of our investigations for pathfollowing techniques with jumps.

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29

Scholz, Jan C., and Martin O. W. Greiner. "Topology control with IPD network creation games." New Journal of Physics 9, no. 6 (June 28, 2007): 185. http://dx.doi.org/10.1088/1367-2630/9/6/185.

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30

Borrvall, Thomas, and Joakim Petersson. "Topology optimization using regularized intermediate density control." Computer Methods in Applied Mechanics and Engineering 190, no. 37-38 (June 2001): 4911–28. http://dx.doi.org/10.1016/s0045-7825(00)00356-x.

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31

Langbort, Cédric, and Vijay Gupta. "Minimal Interconnection Topology in Distributed Control Design." SIAM Journal on Control and Optimization 48, no. 1 (January 2009): 397–413. http://dx.doi.org/10.1137/06067897x.

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32

Bian, Xiaojun, Li-Yi Wei, and Sylvain Lefebvre. "Tile-based Pattern Design with Topology Control." Proceedings of the ACM on Computer Graphics and Interactive Techniques 1, no. 1 (July 25, 2018): 1–15. http://dx.doi.org/10.1145/3203204.

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33

Frisch, Hendrik, Kai Mundsinger, Berwyck L. J. Poad, Stephen J. Blanksby, and Christopher Barner-Kowollik. "Wavelength-gated photoreversible polymerization and topology control." Chemical Science 11, no. 10 (2020): 2834–42. http://dx.doi.org/10.1039/c9sc05381f.

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We exploit the wavelength dependence of [2 + 2] photocycloadditions and -reversions of styrylpyrene to exert unprecedented control over the photoreversible polymerization and topology of telechelic building blocks.

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34

Liu, Jikai, Jinyuan Tang, Rafiq Ahmad, and Yongsheng Ma. "Meta-Material Topology Optimization with Geometric Control." Computer-Aided Design and Applications 16, no. 5 (January 21, 2019): 951–61. http://dx.doi.org/10.14733/cadaps.2019.951-961.

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35

Paul, Prosanta, Hongyi Wu, ChunSheng Xin, and Min Song. "Beamforming Oriented Topology Control for mmWave Networks." IEEE Transactions on Mobile Computing 19, no. 7 (July 1, 2020): 1519–31. http://dx.doi.org/10.1109/tmc.2019.2911577.

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36

Madhusudan, G., and TNR Kumar. "Energy Management Dynamic Control Topology In MANET." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012190. http://dx.doi.org/10.1088/1757-899x/225/1/012190.

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37

Zanni, C., M. Gleicher, and M. P. Cani. "N-ary implicit blends with topology control." Computers & Graphics 46 (February 2015): 1–13. http://dx.doi.org/10.1016/j.cag.2014.09.012.

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38

Chen, Shikui, Michael Yu Wang, and Ai Qun Liu. "Shape feature control in structural topology optimization." Computer-Aided Design 40, no. 9 (September 2008): 951–62. http://dx.doi.org/10.1016/j.cad.2008.07.004.

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39

Hétroy, Franck, Stéphanie Rey, Carlos Andújar, Pere Brunet, and Àlvar Vinacua. "Mesh repair with user-friendly topology control." Computer-Aided Design 43, no. 1 (January 2011): 101–13. http://dx.doi.org/10.1016/j.cad.2010.09.012.

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40

Chen, Siheng, Gary P. T. Choi, and L. Mahadevan. "Deterministic and stochastic control of kirigami topology." Proceedings of the National Academy of Sciences 117, no. 9 (February 13, 2020): 4511–17. http://dx.doi.org/10.1073/pnas.1909164117.

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Kirigami, the creative art of paper cutting, is a promising paradigm for mechanical metamaterials. However, to make kirigami-inspired structures a reality requires controlling the topology of kirigami to achieve connectivity and rigidity. We address this question by deriving the maximum number of cuts (minimum number of links) that still allow us to preserve global rigidity and connectivity of the kirigami. A deterministic hierarchical construction method yields an efficient topological way to control both the number of connected pieces and the total degrees of freedom. A statistical approach to the control of rigidity and connectivity in kirigami with random cuts complements the deterministic pathway, and shows that both the number of connected pieces and the degrees of freedom show percolation transitions as a function of the density of cuts (links). Together, this provides a general framework for the control of rigidity and connectivity in planar kirigami.

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41

Xing, Jian, and Longfei Qie. "A Weighted Control Scheme for Topology Optimization." Journal of Physics: Conference Series 1838, no. 1 (March 1, 2021): 012067. http://dx.doi.org/10.1088/1742-6596/1838/1/012067.

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42

Wu, Huarui, and Li Zhu. "A Network Topology Control Algorithm Based on Mobile Nodes." International Journal of Online Engineering (iJOE) 12, no. 10 (October 31, 2016): 76. http://dx.doi.org/10.3991/ijoe.v12i10.6198.

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<p style="margin: 0in 0in 10pt;"><span style="font-family: Times New Roman; font-size: small;">Topology control is of great significance to reduce energy consumption of wireless sensor network nodes and prolong network lifetime. Different tasks taken by nodes may lead to node failures and fractures of data transmission links, hence undermining the overall network performance. In response to such problems, this paper presents a network topology control algorithm based on mobile nodes that fully considers node energy, node degree and network connectivity. Furthermore, a topology control model is established to analyze weak network topology areas and carry out local topology refactoring. Finally, a simulation experiment demonstrates that the presented algorithm is advantageous in balanced network energy consumption and network connectivity.</span></p>

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43

Genda, Kouichi. "Topology control adopting optimal topology over update interval in mobile ad hoc networks." IEICE Communications Express 9, no. 3 (2020): 83–88. http://dx.doi.org/10.1587/comex.2019xbl0151.

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44

Lutonin, A. S., and J. E. Shklyarskiy. "Topology and control algorithms for a permanent magnet synchronous motor as a part of a vehicle with in-wheel motors." E3S Web of Conferences 266 (2021): 04001. http://dx.doi.org/10.1051/e3sconf/202126604001.

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This article describes an electric drive system’s topology with a permanent magnet synchronous motor for a wide speed range applications. Topology consists of a synchronous motor with permanent magnets (PMSM) and two inverters connected to the beginnings and to the ends of the PMSM’s stator windings. The first inverter is connected to a storage battery, while the other one to a floating bridge capacitor, which acts as a back-EMF compensator. The article proposes electric drive system topolo-gy and its control algorithm. Simulation modeling was implemented by the MATLAB/Simulink software package. Simulation results shows that the proposed electric drive system, in comparison with the standard topology with a «star» stator windings connection, is able to increase the maximum speed of PMSM in the field weakening mode by 17%. The maximum achievable torque on the rotor shaft at the maximum speed of the PMSM motor was increased by 16.6%. Also, developed topology allows to in-crease the speed range in the constant torque mode by 34%.

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45

Kaur, Jaspreet, and Amit Kumar Bindal. "Adaptive Topology Control for Hierarchical Routing Over Resource Constrained Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3917–24. http://dx.doi.org/10.1166/jctn.2019.8271.

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Topology defines the standards used by sensors to communicate with each other. Frequent updates in topology may cause excessive control overhead. Overall resource requirements for network operations can be reduced by optimizing the topology control schemes. In this paper, traditional methods for topology management are investigated and a new approach is introduced to manage the frequent topological updates.

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46

Li, Fei Fei, Hua Rui Wu, Ling Yuan, Yi Sheng Miao, and Li Zhu. "A Farmland Wireless Sensor Network Optimization Topology Control Algorithm." Applied Mechanics and Materials 441 (December 2013): 1005–9. http://dx.doi.org/10.4028/www.scientific.net/amm.441.1005.

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Different node layout density exist in farmland wireless sensor network monitoring, but the existing wireless sensor network topology control algorithm does not take uneven node distribution into consideration. And the node residual energy issue is not considered in the commonly used LMST topology control algorithm either. This paper presents a wireless sensor network hybrid topology control algorithm which considered both the uneven distribution of network nodes and the residual energy issue. Firstly, this algorithm clustering in the high node density areas based on node degree to reduce overall energy consumption. Secondly this algorithm use nodes residual energy based LMST topology control algorithm to achieve energy balance among network nodes. This improved algorithm ultimately achieved the purpose of prolonging the network lifetime.

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47

Borkar, Vivek S. "A topology for Markov controls." Applied Mathematics & Optimization 20, no. 1 (July 1989): 55–62. http://dx.doi.org/10.1007/bf01447645.

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48

Qiu, Ying, and Ding Zhong Tan. "Greenhouse Control System Based on WSN." Key Engineering Materials 486 (July 2011): 254–57. http://dx.doi.org/10.4028/www.scientific.net/kem.486.254.

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According to the demands of the Greenhouse Control System, the WSN(Wireless sensor networks) and WSN node hardware are designed, the network topology, data communication protocol, Encrypt Algorithm, time synchronization algorithm and Routing protocol are researched and realized. The network topology can be changed in accordance with environment needs. The routing path of WSN is multi-hop, and the routing protocol can choose the routing path adaptively. The WSN is deployed and running steadily.

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49

Chen, Yu, Lingyan Sun, Zonghui Wang, and Jinghua Wang. "Distribution Network Topology Identification Based on IEC 61850 Logical Nodes." Scientific Programming 2021 (February 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/6639432.

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Distributed control has good real-time performance and can better meet the control requirements of active distribution networks with a large number of distributed generations. Some distributed applications require real-time feeder topology to achieve control. In this paper, the demand for distributed control applications for feeder real-time topology is analyzed. Based on IEC 61850 modeling method, a new cell topology logic node and a new topology slice node are built to express feeder topology. Using the topology information of smart terminal unit (STU) configuration and the current status information of switchgear, based on the depth-first search, the feeder real-time topology identification can be realized, which meets the application requirements of distributed control. The study case verified the effectiveness of the method.

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50

Sigmund, Ole, and Kurt Maute. "Topology optimization approaches." Structural and Multidisciplinary Optimization 48, no. 6 (August 21, 2013): 1031–55. http://dx.doi.org/10.1007/s00158-013-0978-6.

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