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Journal articles on the topic 'Distributed Sensing and Control'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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1

Henderson, Tom, Chuck Hansen, and Bir Bhanu. "The specification of distributed sensing and control." Journal of Robotic Systems 2, no. 4 (1985): 387–96. http://dx.doi.org/10.1002/rob.4620020405.

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2

Yuksel, S., and S. Tatikonda. "A Counterexample in Distributed Optimal Sensing and Control." IEEE Transactions on Automatic Control 54, no. 4 (April 2009): 841–44. http://dx.doi.org/10.1109/tac.2008.2009680.

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3

Tang, Yujie, Vikram Ramanathan, Junshan Zhang, and Na Li. "Communication-Efficient Distributed SGD With Compressed Sensing." IEEE Control Systems Letters 6 (2022): 2054–59. http://dx.doi.org/10.1109/lcsys.2021.3137859.

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4

ZHANG, WEIJIAN. "Gain of optical distributed sensing in distributed parameter systems." International Journal of Systems Science 22, no. 12 (December 1991): 2521–40. http://dx.doi.org/10.1080/00207729108910811.

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5

Martin, Jeffrey W., Tammy D. Henson, Joseph C. Wehlburg, James M. Redmond, Patrick S. Barney, and John A. Main. "Distributed Sensing and Shape Control of Piezoelectric Bimorph Mirrors." Journal of Intelligent Materials Systems and Structures 11, no. 10 (October 1, 2000): 744–57. http://dx.doi.org/10.1177/104538900772663784.

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6

Ningxu Cai, M. Gholami, Litao Yang, and R. W. Brennan. "Application-Oriented Intelligent Middleware for Distributed Sensing and Control." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 6 (November 2012): 947–56. http://dx.doi.org/10.1109/tsmcc.2011.2174982.

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7

Panagou, Dimitra, Dusan M. Stipanovic, and Petros G. Voulgaris. "Distributed Dynamic Coverage and Avoidance Control Under Anisotropic Sensing." IEEE Transactions on Control of Network Systems 4, no. 4 (December 2017): 850–62. http://dx.doi.org/10.1109/tcns.2016.2576403.

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8

Martin, Jeffrey W., James M. Redmond, Patrick S. Barney, Tammy D. Henson, Joseph C. Wehlburg, and John A. Main. "Distributed Sensing and Shape Control of Piezoelectric Bimorph Mirrors." Journal of Intelligent Material Systems and Structures 11, no. 10 (October 2000): 744–57. http://dx.doi.org/10.1106/2ulx-mnqh-y1af-8b5v.

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9

Choi, Jinho. "Data-Aided Sensing for Distributed Detection." IEEE Wireless Communications Letters 10, no. 5 (May 2021): 1138–41. http://dx.doi.org/10.1109/lwc.2021.3064690.

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10

Tzou, H. S. "Integrated distributed sensing and active vibration suppression of flexible manipulators using distributed piezoelectrics." Journal of Robotic Systems 6, no. 6 (December 1989): 745–67. http://dx.doi.org/10.1002/rob.4620060606.

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11

Papatheodorou, Sotiris, Anthony Tzes, Konstantinos Giannousakis, and Yiannis Stergiopoulos. "Distributed area coverage control with imprecise robot localization." International Journal of Advanced Robotic Systems 15, no. 5 (September 1, 2018): 172988141879749. http://dx.doi.org/10.1177/1729881418797494.

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This article examines the static area coverage problem by a network of mobile, sensor-equipped agents with imprecise localization. Each agent has uniform radial sensing ability and is governed by first-order kinodynamics. To partition the region of interest, a novel partitioning scheme, the Additively Weighted Guaranteed Voronoi diagram is introduced which takes into account both the agents’ positioning uncertainty and their heterogeneous sensing performance. Each agent’s region of responsibility corresponds to its Additively Weighted Guaranteed Voronoi cell, bounded by hyperbolic arcs. An appropriate gradient ascent-based control scheme is derived so that it guarantees monotonic increase of a coverage objective and is extended with collision avoidance properties. Additionally, a computationally efficient simplified control scheme is offered that is able to achieve comparable performance. Several simulation studies are offered to evaluate the performance of the two control schemes. Finally, two experiments using small differential drive-like robots and an ultra-wideband positioning system were conducted, highlighting the performance of the proposed control scheme in a real world scenario.
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12

Bullock, Darcy, Chris Schwehm, and John Broemmelsiek. "Distributed Sensing and Control Technology for Intelligent Civil Infrastructure Systems." Computer-Aided Civil and Infrastructure Engineering 11, no. 2 (March 1996): 77–86. http://dx.doi.org/10.1111/j.1467-8667.1996.tb00312.x.

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13

Liu, Guangjun, Sajan Abdul, and Andrew A. Goldenberg. "Distributed control of modular and reconfigurable robot with torque sensing." Robotica 26, no. 1 (January 2008): 75–84. http://dx.doi.org/10.1017/s0263574707003608.

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SUMMARYA major technical challenge in controlling modular and reconfigurable robots is associated with the kinematics and dynamic model uncertainties caused by reconfiguration. In parallel, conventional model uncertainties such as uncompensated joint friction still persist. This paper presents a modular distributed control technique for modular and reconfigurable robots that can instantly adapt to robot reconfigurations. Under the proposed control method that is based on joint torque sensing, a modular and reconfigurable robot is stabilized joint by joint, and modules can be added or removed without the need to adjust control parameters of the other modules of the robot. Model uncertainties associated with link and payload masses are compensated using joint torque sensor measurement. The remaining model uncertainties, including uncompensated dynamic coupling and joint friction, are compensated by a decomposition-based robust controller. Simulation results have confirmed the effectiveness of the proposed method.
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14

Mallory, K., M. A. Hsieh, E. Forgoston, and I. B. Schwartz. "Distributed allocation of mobile sensing swarms in gyre flows." Nonlinear Processes in Geophysics 20, no. 5 (September 16, 2013): 657–68. http://dx.doi.org/10.5194/npg-20-657-2013.

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Abstract. We address the synthesis of distributed control policies to enable a swarm of homogeneous mobile sensors to maintain a desired spatial distribution in a geophysical flow environment, or workspace. In this article, we assume the mobile sensors (or robots) have a "map" of the environment denoting the locations of the Lagrangian coherent structures or LCS boundaries. Using this information, we design agent-level hybrid control policies that leverage the surrounding fluid dynamics and inherent environmental noise to enable the team to maintain a desired distribution in the workspace. We discuss the stability properties of the ensemble dynamics of the distributed control policies. Since realistic quasi-geostrophic ocean models predict double-gyre flow solutions, we use a wind-driven multi-gyre flow model to verify the feasibility of the proposed distributed control strategy and compare the proposed control strategy with a baseline deterministic allocation strategy. Lastly, we validate the control strategy using actual flow data obtained by our coherent structure experimental testbed.
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15

Ding, Y., E. A. Elsayed, S. Kumara, J. C. Lu, F. Niu, and J. Shi. "Distributed Sensing for Quality and Productivity Improvements." IEEE Transactions on Automation Science and Engineering 3, no. 4 (October 2006): 344–59. http://dx.doi.org/10.1109/tase.2006.876610.

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16

Tzou, H. S., and R. Ye. "Piezothermoelasticity and Precision Control of Piezoelectric Systems: Theory and Finite Element Analysis." Journal of Vibration and Acoustics 116, no. 4 (October 1, 1994): 489–95. http://dx.doi.org/10.1115/1.2930454.

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Piezothermoelastic effects of distributed piezoelectric sensor/actuator and structural systems are studied. Distributed controls (static and dynamic) of piezoelectric laminates subjected to a steady-state temperature field are investigated. Piezothermoelastic constitutive equations are defined, followed by three energy functionals for the displacement, electric, and temperature fields, respectively. A new 3-D piezothermoelastic thin hexahedron finite element with three internal degrees of freedom is formulated using a variational formulation which includes thermal, electric, and mechanical energies. A system equation for the piezoelectric continuum exposed to combined displacement, electric, and temperature fields is formulated. Distributed sensing and control equations of piezoelectric laminates in a temperature field are derived. Thermal influences on the sensing and control of piezoelectric PZT/steel laminates are investigated in case studies.
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17

Ellmauthaler, Andreas, Brian C. Seabrook, Glenn A. Wilson, John Maida, Jeff Bush, Michel LeBlanc, James Dupree, and Mauricio Uribe. "Distributed acoustic sensing of subsea wells." Leading Edge 39, no. 11 (November 2020): 801–7. http://dx.doi.org/10.1190/tle39110801.1.

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Topside distributed acoustic sensing (DAS) of subsea wells requires advanced optical engineering solutions to compensate for reduced acoustic bandwidth, optical losses, and back reflections that are accumulated through umbilicals, multiple wet- and dry-mate optical connectors, splices, optical feedthrough systems, and downhole fibers. To address these issues, we introduce a novel DAS solution based on subsea fiber topology consisting of two transmission fibers from topside and an optical circulator deployed in the optical flying lead at the subsea tree. This solution limits the sensing fiber portion to the downhole fiber, located below the subsea tree, and enables dry-tree-equivalent acoustic sampling frequencies of more than 10 kHz while eliminating all back reflections from multiple subsea connectors above the tree. When combined with enhanced backscatter single-mode fiber, this gives rise to a DAS interrogation system that is capable of providing dry-tree-equivalent acoustic sensing performance over the entire length of the subsea well, regardless of the tie-back distance. It also enables the same spectral-based DAS processing algorithms developed for seismic, sand control, injector/producer profiling, and well integrity on dry-tree wells to be applied directly to subsea DAS data. The performance of this subsea DAS system has been validated through a series of laboratory and field trials. We show the results of the tests and discuss how the system is deployed within subsea infrastructure.
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18

Abbracciavento, Francesco, Simone Formentin, Jacopo Balocco, Andrea Rota, Vincenzo Manzoni, and Sergio M. Savaresi. "Anomaly detection via distributed sensing: a VAR modeling approach." IFAC-PapersOnLine 54, no. 7 (2021): 85–90. http://dx.doi.org/10.1016/j.ifacol.2021.08.339.

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19

FANG Yuan-kun, 方元坤, 袁斌文 YUAN Bin-wen, 孟子阳 MENG Zi-yang, 尤. 政. YOU Zheng, and 张高飞 ZHANG Gao-fei. "Attitude control in multi-satellite cooperative observations for distributed remote sensing." Optics and Precision Engineering 27, no. 1 (2019): 58–68. http://dx.doi.org/10.3788/ope.20192701.0058.

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20

Sarlette, Alain, and Rodolphe J. Sepulchre. "Control limitations from distributed sensing: Theory and Extremely Large Telescope application." Automatica 50, no. 2 (February 2014): 421–30. http://dx.doi.org/10.1016/j.automatica.2013.12.014.

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21

Ouyang, Jianshu, Xianming Chen, Zehua Huangfu, Cheng Lu, Dahai Huang, and Yangbo Li. "Application of distributed temperature sensing for cracking control of mass concrete." Construction and Building Materials 197 (February 2019): 778–91. http://dx.doi.org/10.1016/j.conbuildmat.2018.11.221.

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22

Shih, Hui-Ru. "Distributed vibration sensing and control of a piezoelectric laminated curved beam." Smart Materials and Structures 9, no. 6 (November 29, 2000): 761–66. http://dx.doi.org/10.1088/0964-1726/9/6/304.

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23

Morye, Akshay A., Chong Ding, Amit K. Roy-Chowdhury, and Jay A. Farrell. "Distributed Constrained Optimization for Bayesian Opportunistic Visual Sensing." IEEE Transactions on Control Systems Technology 22, no. 6 (November 2014): 2302–18. http://dx.doi.org/10.1109/tcst.2014.2300416.

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24

Taira, Tetsuya, and Nobuyuki Yamasaki. "Functionally Distributed Control Architecture for Autonomous Mobile Robots." Journal of Robotics and Mechatronics 16, no. 2 (April 20, 2004): 217–24. http://dx.doi.org/10.20965/jrm.2004.p0217.

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This paper explains the design and implementation of functionally distributed control architecture that realizes real-time control of autonomous mobile robots. To operate successfully in human society, autonomous mobile robots must achieve both local and global control in real-time. We focus on robots operating in parallel, such as moving while sensing, and propose a functionally distributed control architecture designed as a parallel/distributed computer consisting of many functionally distributed modules. Each module has an exclusive Processing Unit (PU) that processes one function of robot, such as image processing, auditory processing, and wheel control, independently in real-time. The robot can perform global action by cooperating with such modules. We then evaluate the efficiency of the proposed architecture by implementing prototype robots based on this architecture.
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25

Yang, B., and C. D. Mote. "Frequency-Domain Vibration Control of Distributed Gyroscopic Systems." Journal of Dynamic Systems, Measurement, and Control 113, no. 1 (March 1, 1991): 18–25. http://dx.doi.org/10.1115/1.2896350.

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A new method is presented for vibration control of distributed gyroscopic systems. The control is formulated in the Laplace transform domain. The transfer function of a closed-loop system, consisting of the plant, a feedback control law and the dynamics of the sensing and actuation devices, is derived. Stability analyses of the closed-loop system use both the root locus method and the generalized Nyquist criterion. Two stability criteria are obtained. Design of stabilizing controllers is carried out for both colocation and noncolocation of the sensor and actuator. The effects of time-delay and noncolocation of the sensor and actuator on the system stability are analyzed. In addition, the relationship between the root locus method and the generalized Nyquist criterion is discussed.
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26

Kharaz, A., and B. Jones. "A Distributed Fibre Optic Sensing System for Humidity Measurement." Measurement and Control 28, no. 4 (May 1995): 101–3. http://dx.doi.org/10.1177/002029409502800402.

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27

Rao, S. S., and M. Sunar. "Piezoelectricity and Its Use in Disturbance Sensing and Control of Flexible Structures: A Survey." Applied Mechanics Reviews 47, no. 4 (April 1, 1994): 113–23. http://dx.doi.org/10.1115/1.3111074.

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Piezoelectric materials are being used at an increasing rate by researchers in the areas of vibration, measurement and control. Due to their distinct features, these materials can be employed in the distributed sensing and control of intelligent structures that have a highly integrated control architecture. By employing piezoelectric materials, it is feasible to achieve an accurate response monitoring and effective control of flexible structures. In this survey paper, the recent research trends addressing piezoelectricity in the context of distributed sensing and control of flexible structures are discussed. A brief history of piezoelectricity is also noted.
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28

Franzini, Giovanni, and Mario Innocenti. "Distributed cooperative deployment of heterogeneous autonomous agents: a Pareto suboptimal approach." Robotica 36, no. 12 (August 30, 2018): 1943–62. http://dx.doi.org/10.1017/s0263574718000814.

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SUMMARYThe paper presents a distributed cooperative control law for autonomous deployment of a team of heterogeneous agents. Deployment problems deal with the coordination of groups of agents in order to cover one or more assigned areas of the operational space. In particular, we consider a team composed by agents with different dynamics, sensing capabilities, and resources available for the deployment. Sensing heterogeneity is addressed by means of the descriptor function framework, an abstraction that provides a set of mathematical tools for describing both agent sensing capabilities and the desired deployment. A distributed cooperative control law is then formally derived finding a suboptimal solution of a cooperative differential game, where the agents are interested in achieving the requested deployment, while optimizing the resources usage according to their dynamics. The control law effectiveness is proven by theoretical arguments, and supported by numerical simulations.
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29

Lu, Xin, Peter James Thomas, and Jon Oddvar Hellevang. "A Review of Methods for Fibre-Optic Distributed Chemical Sensing." Sensors 19, no. 13 (June 28, 2019): 2876. http://dx.doi.org/10.3390/s19132876.

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Chemical sensing is of great importance in many application fields, such as medicine, environmental monitoring, and industrial process control. Distributed fibre-optic sensing received significant attention because of its unique feature to make spatially resolved measurements along the entire fibre. Distributed chemical sensing (DCS) is the combination of these two techniques and offers potential solutions to real-world applications that require spatially dense chemical measurements covering large length scales. This paper presents a review of the working principles, current status, and the emerging trends within DCS.
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30

Rahman, Saifur, Farman Ali, Fazal Muhammad, Muhammad Irfan, Adam Glowacz, Mohammed Shahed Akond, Ammar Armghan, Salim Nasar Faraj Mursal, Amjad Ali, and Fahad Salem Alkahtani. "Analyzing Distributed Vibrating Sensing Technologies in Optical Meshes." Micromachines 13, no. 1 (January 5, 2022): 85. http://dx.doi.org/10.3390/mi13010085.

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Hundreds of kilometers of optical fibers are installed for optical meshes (OMs) to transmit data over long distances. The visualization of these deployed optical fibers is a highlighted issue because the conventional procedure can only measure the optical losses. Thus, this paper presents distributed vibration sensing (DVS) estimation mechanisms to visualize the optical fiber behavior installed for OMs which is not possible by conventional measurements. The proposed technique will detect the power of light inside the optical fiber, as well as different physical parameters such as the phase of transmitted light inside the thread, the frequency of vibration, and optical losses. The applicability of optical frequency domain reflectometry (OFDR) and optical time-domain reflectometry (OTDR) DVS techniques are validated theoretically for various state detection procedures in optical fibers. The simulation model is investigated in terms of elapsed time, the spectrum of a light signal, frequency, and the impact of many external physical accidents with optical fibers.
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31

Liang, Jing, and Chengchen Mao. "Distributed compressive sensing in heterogeneous sensor network." Signal Processing 126 (September 2016): 96–102. http://dx.doi.org/10.1016/j.sigpro.2015.10.026.

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32

Kan, Zhen, Emily A. Doucette, and Warren E. Dixon. "Distributed Connectivity Preserving Target Tracking With Random Sensing." IEEE Transactions on Automatic Control 64, no. 5 (May 2019): 2166–73. http://dx.doi.org/10.1109/tac.2018.2867594.

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33

Akbarpour-Kasgari, Abbas, and Mehrdad Ardebilipour. "Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing." IEEE Wireless Communications Letters 8, no. 2 (April 2019): 376–79. http://dx.doi.org/10.1109/lwc.2018.2873339.

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34

Papatheodorou, Sotiris, and Anthony Tzes. "Fault tolerant area coverage control for multiagent systems." MATEC Web of Conferences 188 (2018): 05010. http://dx.doi.org/10.1051/matecconf/201818805010.

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The fault tolerance characteristics of a distributed multi-agent coverage algorithm are examined. A team of sensor-equipped mobile agents is tasked with covering a planar region of interest. A distributed, gradient-based control scheme is utilized for this purpose. The agents are assumed to consist of three subsystems, each one of which may fail. The subsystems under examination are the actuation, sensing and the communication subsystem. Partial and catastrophic faults are examined. Several simulation studies are conducted highlighting the robustness of the distributed nature of the control scheme to these classes of faults, even when several of them happen at the same time.
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35

Haile, Mulugeta A., Nathaniel E. Bordick, and Jaret C. Riddick. "Distributed acoustic emission sensing for large complex air structures." Structural Health Monitoring 17, no. 3 (June 20, 2017): 624–34. http://dx.doi.org/10.1177/1475921717714614.

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The vast majority of existing work on acoustic emission–based structural health monitoring is for geometrically simple structures with uninterrupted propagation path and constant wave speed. Realistic systems such as a full-scale fuselage, however, are built from interconnected pieces of acoustically mismatched parts such as sandwich core panels, stringer stiffened skin, and fastener holes. The geometric complexity and dynamic operating environment of realistic systems mean that the acoustic emission wave undergoes multiple reflections, refractions, and mode changes resulting in overlapped transducer outputs with no clear beginning and end. The objective of this paper is to outline the fundamental limitations of acoustic emission as applied to complex systems and present a new distributed data-centric acoustic emission sensing network for durability health monitoring and damage tolerance applications in large and complex systems. The study considers the case of a full-scale composite rotorcraft fuselage to introduce several new concepts on acoustic emission data acquisition time control for alleviating effects of wave distortion as well as methods for improving event location analysis in large quasi-isotropic materials. Methods for adaptive front-end signal processing and data volume control are presented. Despite the size and complexity of realistic full-scale systems and the acoustic emission data, we show that it is possible to locate damage with acceptable accuracy.
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36

Denney, Dennis. "Combining Distributed-Temperature Sensing With Inflow-Control Devices for Improved Injection Profile." Journal of Petroleum Technology 62, no. 06 (June 1, 2010): 79–80. http://dx.doi.org/10.2118/0610-0079-jpt.

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37

KIM, Su Min, and Junsu KIM. "Adaptive Sensing Period Based Distributed Medium Access Control for Cognitive Radio Networks." IEICE Transactions on Communications E97.B, no. 11 (2014): 2502–11. http://dx.doi.org/10.1587/transcom.e97.b.2502.

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38

Zhao, F., C. Bailey-Kellogg, and M. P. J. Fromherz. "Physics-based encapsulation in embedded software for distributed sensing and control applications." Proceedings of the IEEE 91, no. 1 (January 2003): 40–63. http://dx.doi.org/10.1109/jproc.2002.805819.

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39

Chandiramani, N. K., and S. P. Purohit. "Semi-Active Control Using Magnetorhelogical Dampers with Output Feedback and Distributed Sensing." Shock and Vibration 19, no. 6 (2012): 1427–43. http://dx.doi.org/10.1155/2012/838140.

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Control of seismic response of a building fitted with magnetorheological dampers is considered using Optimal Static Output Feedback (OSOF) for desired damper forces. The Modified Bouc-Wen damper model is used and two control voltage laws based on the MR constraint filter, i.e., Semi-inverse Quadratic Voltage Law and Semi-inverse On-Off Voltage Law, are proposed. These appear to perform at least as well as an existing Clipped Voltage Law. Comparisons with available results from a robust reliability-based controller show OSOF control to be quite effective. Controlled response using OSOF is compared with Linear Quadratic Guassian (LQG) and passive-on controllers. Moderate to substantial reduction in maximum peak/RMS responses is mostly obtained with base configuration of sensors when using OSOF control, and controller CPU time reduces by two orders of magnitude. Parametric studies regarding sensor configuration and state/control weighting matrices are performed in order to obtain effective control. Effective OSOF control requires drift feedback with drift sensor preferably collocated with damper.
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Ilic, Marija D., Le Xie, Usman A. Khan, and José M. F. Moura. "Modeling of Future Cyber–Physical Energy Systems for Distributed Sensing and Control." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 4 (July 2010): 825–38. http://dx.doi.org/10.1109/tsmca.2010.2048026.

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41

Krieg, Michael, Kevin Nelson, and Kamran Mohseni. "Distributed sensing for fluid disturbance compensation and motion control of intelligent robots." Nature Machine Intelligence 1, no. 5 (April 22, 2019): 216–24. http://dx.doi.org/10.1038/s42256-019-0044-1.

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42

SAKURAI, Hiroshi, and Yasuhiko HANGAI. "SENSING AND VIBRATION CONTROL OF PIEZOELECTRIC SHELLS : Segmented distributed sensors and actuators." Journal of Structural and Construction Engineering (Transactions of AIJ) 63, no. 504 (1998): 65–72. http://dx.doi.org/10.3130/aijs.63.65_1.

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43

Caicedo, David, and Ashish Pandharipande. "Distributed Illumination Control With Local Sensing and Actuation in Networked Lighting Systems." IEEE Sensors Journal 13, no. 3 (March 2013): 1092–104. http://dx.doi.org/10.1109/jsen.2012.2228850.

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44

Chang, Bao Rong, Hsiu-Fen Tsai, Jyong-Lin Lyu, and Chien-Feng Huang. "Distributed sensing units deploying on group unmanned vehicles." International Journal of Distributed Sensor Networks 17, no. 7 (July 2021): 155014772110368. http://dx.doi.org/10.1177/15501477211036877.

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This study aims to use two unmanned vehicles (aerial vehicles and ground vehicles) to implement multi-machine cooperation to complete the assigned tasks quickly. Unmanned aerial/ground vehicles can call each other to send instant inquiry messages using the proposed cooperative communication protocol to hand over the tasks between them and execute efficient three-dimensional collaborative operations in time. This study has demonstrated integrating unmanned aerial/ground vehicles into a group through the control platform (i.e. App operation interface) that uses the Internet of Things. Therefore, pilots can make decisions and communicate through App for cooperative coordination, allowing a group of unmanned aerial/ground vehicles to complete the tasks flexibly. In addition, the payload attached to unmanned air/ground vehicles can carry out multipurpose monitoring that implements face recognition, gas detection, thermal imaging, and video recording. During the experiment of unmanned aerial vehicle, unmanned aerial vehicle will plan the flight path and record the movement trajectory with global positioning system when it is on duty. As a result, the accuracy of the planned flight path achieved 86.89% on average.
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45

Sunar, M., and S. S. Rao. "Recent Advances in Sensing and Control of Flexible Structures Via Piezoelectric Materials Technology." Applied Mechanics Reviews 52, no. 1 (January 1, 1999): 1–16. http://dx.doi.org/10.1115/1.3098923.

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Due to their special characteristics, piezoelectric materials can be used in distributed behavior sensing and control of flexible structures. These materials are usually incorporated with the precision sensing and control of highly adaptive intelligent structures. Many theoretical, numerical, and experimental research activities treating piezoelectricity in sensing and control of various flexible structures have been carried out over the last decade. This survey article aims at collecting the recent research studies in this important field. It contains 336 references which are classified according to their applications. A brief theory and history of piezoelectricity is also presented.
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46

Li, Xiaoyun, and David K. Hunter. "Distributed coordinate-free algorithm for full sensing coverage." International Journal of Sensor Networks 5, no. 3 (2009): 153. http://dx.doi.org/10.1504/ijsnet.2009.026363.

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47

De Falco, Stefano, and Giulia Fiorentino. "Remote sensing based on time variance control in configurable area partitioning." Proceedings of the ICA 4 (December 3, 2021): 1–9. http://dx.doi.org/10.5194/ica-proc-4-25-2021.

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Abstract. In this paper a sensor data fusion approach for characteristics field monitoring, based on time variance control model, is proposed. Distributed sensing and remote processing are the basic features of the employed architecture. In fact, in order to obtain meaningful information about the temporal and spatial variations, which characterize the field levels of some characteristics (electromagnetic, air pollution, seismic, etc), a distributed network of wireless and mobile smart-sensors has been designed.Starting from the partitioned configuration of a monitored geographic areas, this model allows to take into account the different levels of degradation over time in the sensors' performances associated with the different geographic partitions, progressively increasing the severity of the control. To this end, through the introduction of a reliability curve, a revised traditional control chart for variables is proposed.The proposed approach, further constituting an element of the scientific debate, aims to be a useful operational tool for professionals and managers employed in the environment control.
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48

Palangi, Hamid, Rabab Ward, and Li Deng. "Convolutional Deep Stacking Networks for distributed compressive sensing." Signal Processing 131 (February 2017): 181–89. http://dx.doi.org/10.1016/j.sigpro.2016.07.006.

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49

Kwon, Cheolhyeon, and Inseok Hwang. "Sensing-Based Distributed State Estimation for Cooperative Multiagent Systems." IEEE Transactions on Automatic Control 64, no. 6 (June 2019): 2368–82. http://dx.doi.org/10.1109/tac.2018.2867341.

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50

Tanaka, N., S. D. Snyder, and C. H. Hansen. "Distributed Parameter Modal Filtering Using Smart Sensors." Journal of Vibration and Acoustics 118, no. 4 (October 1, 1996): 630–40. http://dx.doi.org/10.1115/1.2888345.

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This paper considers the design of distributed parameter modal sensors called “smart sensors,” with a particular emphasis on filtering the combination of appropriately weighted vibration modes providing a specific performance index in control strategy. First, with a two-dimensional distributed parameter sensor using a PVDF film, the necessary and sufficient condition for sensing the transformed modes of a structure is derived. Then, by considering the practicability of the two-dimensional sensors, an alternative approach based upon one-dimensional smart sensors is presented. It is found that the latter approach holds the necessary condition for sensing the transformed mode. This problem is overcome by introducing multiple one-dimensional smart sensors. Moreover, the design procedure for the multiple one-dimensional smart sensors for measuring the transformed mode is established. Finally, an experiment is conducted, demonstrating the validity of the smart sensors.
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