Relevant bibliographies by topics / Distributed parameter systems (Computers)

Contents

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

Academic literature on the topic 'Distributed parameter systems (Computers)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Distributed parameter systems (Computers)"

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Yang, B. "Distributed Transfer Function Analysis of Complex Distributed Parameter Systems." Journal of Applied Mechanics 61, no. 1 (1994): 84–92. http://dx.doi.org/10.1115/1.2901426.

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This paper presents a new analytical and numerical method for modeling and synthesis of complex distributed parameter systems that are multiple continua combined with lumped parameter systems. In the analysis, the complex distributed parameter system is first divided into a number of subsystems; the distributed transfer functions of each subsystem are determined in exact and closed form by a state space technique. The complex distributed parameter system is then assembled by imposing displacement compatibility and force balance at the nodes where the subsystems are interconnected. With the distributed transfer functions and the transfer functions of the constraints and lumped parameter systems, exact, closed-form formulation is obtained for various dynamics and vibration problems. The method does not require a knowledge of system eigensolutions, and is valid for non-self-adjoint systems with inhomogeneous boundary conditions. In addition, the proposed method is convenient in computer coding and suitable for computerized symbolic manipulation.
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Gubarev, V. F. "Rational approximation of distributed parameter systems." Cybernetics and Systems Analysis 44, no. 2 (2008): 234–46. http://dx.doi.org/10.1007/s10559-008-0023-8.

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

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EL JAI, A., M. C. SIMON, E. ZERRIK, and A. J. PRITCHARD. "Regional controllability of distributed parameter systems." International Journal of Control 62, no. 6 (1995): 1351–65. http://dx.doi.org/10.1080/00207179508921603.

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REBIAI, S. E., and A. S. I. ZINOBER. "Stabilization of uncertain distributed parameter systems." International Journal of Control 57, no. 5 (1993): 1167–75. http://dx.doi.org/10.1080/00207179308934438.

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HARA, KEI, TESTSUHIKO YAMAMOTO, and KAZUYUKI OUGITA. "Parameter identification of distributed parameter systems using spline functions." International Journal of Systems Science 19, no. 1 (1988): 49–64. http://dx.doi.org/10.1080/00207728808967587.

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Orlikowski, Cezary, and Rafał Hein. "Port-Based Modeling of Distributed-Lumped Parameter Systems." Solid State Phenomena 164 (June 2010): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.183.

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This paper presents a uniform, port-based approach for modeling of both lumped and distributed parameter systems. Port-based model of the distributed system has been defined by application of bond graph methodology and distributed transfer function method (DTFM). The proposed approach combines versatility of port-based modeling and accuracy of distributed transfer function method. A concise representation of lumped-distributed systems has been obtained. The proposed method of modeling enables to formulate input data for computer analysis by application of DTFM.
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Plotnikov, A. V. "Control of distributed-parameter systems under uncertainty." Cybernetics and Systems Analysis 27, no. 6 (1991): 940–43. http://dx.doi.org/10.1007/bf01246529.

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Bernoussi, A. "Spreadability and vulnerability of distributed parameter systems." International Journal of Systems Science 38, no. 4 (2007): 305–17. http://dx.doi.org/10.1080/00207720601159787.

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KODA, MASATO. "Sensitivity analysis of descriptor distributed parameter systems." International Journal of Systems Science 19, no. 10 (1988): 2103–14. http://dx.doi.org/10.1080/00207728808964102.

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Dissertations / Theses on the topic "Distributed parameter systems (Computers)"

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Agrawal, Janak. "Distributed parameter estimation for complex energy systems." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129082.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 81-83).<br>With multiple energy sources, diverse energy demands, and heterogeneous socioeconomic factors, energy systems are becoming increasingly complex. Multifaceted components have non-linear dynamics and are interacting with each other as well as the environment. In this thesis, we model components in terms of their own internal dynamics and output variables at the interfaces with the neighboring components. We then propose to use a distributed estimation method for obtaining the parameters of the the component's internal model based on the measurements at its interfaces. We check whether theoretical conditions for distributed estimation approach are met and validate the results obtained. The estimated parameters of the system can then be used for advanced control purposes in the HVAC system. We also use the measurements at the terminals to model and verify the components in the energy-space which is a novel approach proposed by our group. The energy space approach reflects conservation of power and rate of change of reactive power. Both power and rate of change of generalized reactive power are obtained from measurements at the input and output ports of the components by measuring flows and efforts associated with their ports. A pair of flow and efforts is measured for electrical and gas ports, as well as for fluids. We show that the energy space model agrees with the conventional state space model with a high accuracy and that standard measurements available in a commercial HVAC can be used for calculating the interaction variables in the energy space model. A novel finding is that unless measurements of both flow and effort variables is used, the sub-model representing rate of change of reactive power can not be validated. This implies that commonly used models in engineering which assume constant effort variables may not be sufficiently accurate to support most efficient control of complex interconnected systems comprising multiple energy conversion processes.<br>by Janak Agrawal.<br>M. Eng.<br>M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Graham, Douglas. "Parameterised verification of randomised distributed systems using state-based models." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/95/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
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Barkman, Patrik. "Grey-box modelling of distributed parameter systems." Thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240677.

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Grey-box models are constructed by combining model components that are derived from first principles with components that are identified empirically from data. In this thesis a grey-box modelling method for describing distributed parameter systems is presented. The method combines partial differential equations with a multi-layer perceptron network in order to incorporate prior knowledge about the system while identifying unknown dynamics from data. A gradient-based optimization scheme which relies on the reverse mode of automatic differentiation is used to train the network. The method is presented in the context of modelling the dynamics of a chemical reaction in a fluid. Lastly, the grey-box modelling method is evaluated on a one-dimensional and two-dimensional instance of the reaction system. The results indicate that the grey-box model was able to accurately capture the dynamics of the reaction system and identify the underlying reaction.<br>Hybridmodeller konstrueras genom att kombinera modellkomponenter som härleds från grundläggande principer med modelkomponenter som bestäms empiriskt från data. I den här uppsatsen presenteras en metod för att beskriva distribuerade parametersystem genom hybridmodellering. Metoden kombinerar partiella differentialekvationer med ett neuronnätverk för att inkorporera tidigare känd kunskap om systemet samt identifiera okänd dynamik från data. Neuronnätverket tränas genom en gradientbaserad optimeringsmetod som använder sig av bakåt-läget av automatisk differentiering. För att demonstrera metoden används den för att modellera kemiska reaktioner i en fluid. Metoden appliceras slutligen på ett en-dimensionellt och ett två-dimensionellt exempel av reaktions-systemet. Resultaten indikerar att hybridmodellen lyckades återskapa beteendet hos systemet med god precision samt identifiera den underliggande reaktionen.
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Ford, Walter Davis Gravagne Ian A. "Development and implementation of real-time distributed network with the CAN protocol." Waco, Tex. : Baylor University, 2005. http://hdl.handle.net/2104/2999.

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Alana, Jorge Enrique. "Optimal measurement locations for parameter estimation of distributed parameter systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/optimal-measurement-locations-for-parameter-estimation-of-distributed-parameter-systems(fffa31d8-2b19-434b-a2b6-7809e314bb55).html.

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Identifying the parameters with the largest influence on the predicted outputs of a model revealswhich parameters need to be known more precisely to reduce the overall uncertainty on themodel output. A large improvement of such models would result when uncertainties in the keymodel parameters are reduced. To achieve this, new experiments could be very helpful,especially if the measurements are taken at the spatio-temporal locations that allow estimate the parameters in an optimal way. After evaluating the methodologies available for optimal sensor location, a few observations were drawn. The method based on the Gram determinant evolution can report results not according to what should be expected. This method is strongly dependent of the sensitivity coefficients behaviour. The approach based on the maximum angle between subspaces, in some cases, produced more that one optimal solution. It was observed that this method depends on the magnitude of outputs values and report the measurement positions where the outputs reached their extrema values. The D-optimal design method produces number and locations of the optimal measurements and it depends strongly of the sensitivity coefficients, but mostly of their behaviours. In general it was observed that the measurements should be taken at the locations where the extrema values (sensitivity coefficients, POD modes and/or outputs values) are reached. Further improvements can be obtained when a reduced model of the system is employed. This is computationally less expensive and the best estimation of the parameter is obtained, even with experimental data contaminated with noise. A new approach to calculate the time coefficients belonging to an empirical approximator based on the POD-modes derived from experimental data is introduced. Additionally, an artificial neural network can be used to calculate the derivatives but only for systems without complex nonlinear behaviour. The latter two approximations are very valuable and useful especially if the model of the system is unknown.
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Chander, R. "Identification of distributed parameter systems with damping." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/13386.

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Liu, Yi. "Grey-box Identification of Distributed Parameter Systems." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220.

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Evans, Katie Allison. "Reduced Order Controllers for Distributed Parameter Systems." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/11063.

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Distributed parameter systems (DPS) are systems defined on infinite dimensional spaces. This includes problems governed by partial differential equations (PDEs) and delay differential equations. In order to numerically implement a controller for a physical system we often first approximate the PDE and the PDE controller using some finite dimensional scheme. However, control design at this level will typically give rise to controllers that are inherently large-scale. This presents a challenge since we are interested in the design of robust, real-time controllers for physical systems. Therefore, a reduction in the size of the model and/or controller must take place at some point. Traditional methods to obtain lower order controllers involve reducing the model from that for the PDE, and then applying a standard control design technique. One such model reduction technique is balanced truncation. However, it has been argued that this type of method may have an inherent weakness since there is a loss of physical information from the high order, PDE approximating model prior to control design. In an attempt to capture characteristics of the PDE controller before the reduction step, alternative techniques have been introduced that can be thought of as controller reduction methods as opposed to model reduction methods. One such technique is LQG balanced truncation. Only recently has theory for LQG balanced truncation been developed in the infinite dimensional setting. In this work, we numerically investigate the viability of LQG balanced truncation as a suitable means for designing low order, robust controllers for distributed parameter systems. We accomplish this by applying both balanced reduction techniques, coupled with LQG, MinMax and central control designs for the low order controllers, to the cable mass, Klein-Gordon, and Euler-Bernoulli beam PDE systems. All numerical results include a comparison of controller performance and robustness properties of the closed loop systems.<br>Ph. D.
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Norris, Mark A. "Parameter identification in distributed structures." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/71164.

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This dissertation develops two new techniques for the identification of parameters in distributed-parameter systems. The first technique identifies the physical parameter distributions such as mass, damping and stiffness. The second technique identifies the modal quantities of self-adjoint distributed-parameter systems. Distributed structures are distributed-parameter systems characterized by mass, damping and stiffness distributions. To identify the distributions, a new identification technique is introduced based on the finite element method. With this approach, the object is to identify "average" values of mass, damping and stiffness distributions over each finite element. This implies that the distributed parameters are identified only approximately, in the same way in which the finite element method approximates the behavior of a structure. It is common practice to represent the motion of a distributed parameter system by a linear combination of the associated modes of vibration. In theory, we have an infinite set of modes although, in practice we are concerned with only a finite linear combination of the modes. The modes of vibration possess certain properties which distinguish them from one another. Indeed, the modes of vibration are uncorrelated in time and orthogonal in space. The modal identification technique introduced in this dissertation uses path these spatial properties. Because both the temporal and spatial properties are used, the method does not encounter problems when the natural frequencies are closely-spaced or repeated.<br>Ph. D.
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Lee, Alexandre J. (Alexandre Jose) 1975. "Integration of handheld computers into distributed database systems." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/49656.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaf 114).<br>by Alexandre J. Lee.<br>S.B.and M.Eng.
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Books on the topic "Distributed parameter systems (Computers)"

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Reiter, Michael. Integrating security in a group oriented distributed system. Dept. of Computer Science, Cornell University, 1992.

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Mihail-Ioan, Abrudean, Unguresan Mihaela-Ligia, Muresan Vlad, and SpringerLink (Online service), eds. Numerical Simulation of Distributed Parameter Processes. Springer International Publishing, 2013.

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Tanenbaum, Andrew S. Distributed systems: Principles and paradigms. Prentice Hall, 2002.

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1944-, Futagami T., Tzafestas S. G. 1939-, Sunahara Yoshifumi, International Association for Mathematics and Computers in Simulation., and International Federation of Automatic Control., eds. Distributed parameter systems: Modelling and simulation :proceedings of the IMACS/IFAC International Symposium on Modelling and Simulation of Distributed Parameter Systems, Hiroshima, Japan, 6-9 October, 1987. North-Holland, 1989.

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Chenkun, Qi, and SpringerLink (Online service), eds. Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems: A Time/Space Separation Based Approach. Springer Netherlands, 2011.

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Vyatkin, Valeriy. IEC 61499 function blocks for embedded and distributed control systems design. ISA-Instrumentation, Systems, and Automation Society, 2007.

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D'Autrechy, C. Lynne. Autoplan: A self-processing network model for an extended blocks world planning environment. University of Maryland, 1990.

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D'Autrechy, C. Lynne. Autoplan: A self-processing network model for an extended blocks world planning environment. University of Maryland, 1990.

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Birman, Kenneth P. ISIS and META projects: Progress report. National Aeronautics and Space Administration?, 1990.

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Ryan, Thomas P. Development and application of a physically based distributed parameter rainfall runoff model in the Gunnison River Basin. U.S. Dept. of the Interior, Bureau of Reclamation, Denver Office, 1996.

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Book chapters on the topic "Distributed parameter systems (Computers)"

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Stehlík, Milan, and Jozef Kiseľák. "On Stochastic Representation of Blow-Ups for Distributed Parameter Systems." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57099-0_12.

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Patan, Maciej. "A Parallel Sensor Scheduling Technique for Fault Detection in Distributed Parameter Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85451-7_88.

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Taylor, J. E. "Distributed Parameter Optimal Structural Design: Some Basic Problem Formulations and their Application." In Computer Aided Optimal Design: Structural and Mechanical Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83051-8_1.

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Ji, Huihui, and Baotong Cui. "H ∞ Filtering Design for a Class of Distributed Parameter Systems with Randomly Occurring Sensor Faults and Markovian Channel Switching." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6442-5_11.

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Chernousko, Felix L., Igor M. Ananievski, and Sergey A. Reshmin. "Control in distributed-parameter systems." In Control of Nonlinear Dynamical Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70784-4_7.

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Carlson, Dean A., Alain B. Haurie, and Arie Leizarowitz. "Extensions to Distributed Parameter Systems." In Infinite Horizon Optimal Control. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76755-5_9.

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Ghasem, Nayef. "Heat Transfer Distributed Parameter Systems." In Modeling and Simulation of Chemical Process Systems. CRC Press, 2018. http://dx.doi.org/10.1201/b22487-6.

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Lee, Chong-Won. "Distributed Parameter Rotor-Bearing Systems." In Vibration Analysis of Rotors. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8173-8_7.

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Carlson, D. A., and A. Haurie. "Extensions to Distributed Parameter Systems." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-662-02529-1_8.

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Butkovskiy, A. G., and L. M. Pustyl’nikov. "Characteristics of interconnected distributed systems." In Characteristics of Distributed-Parameter Systems. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2062-3_2.

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Conference papers on the topic "Distributed parameter systems (Computers)"

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Kar, Soummya, and Jose M. F. Moura. "Distributed linear parameter estimation in sensor networks: Convergence properties." In 2008 42nd Asilomar Conference on Signals, Systems and Computers. IEEE, 2008. http://dx.doi.org/10.1109/acssc.2008.5074638.

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Lee, Yong-Kwan, and Leonid S. Chechurin. "Determination of Parametric Resonances in Distributed Parameter Systems Using Frequency Analysis." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34171.

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Theoretical analysis of parametric instability for the control systems with distributed parameters shall be given. The approach to the solution of such systems can be composed of two parts, i.e. modeling and estimation of the distributed parameters and instability estimation of the periodical time-variant elements using parametric circumference. A control system with mechanical distributed parameters such as robot manipulators is introduced as an example. Theoretical analysis shows that the parametric instabilities occur by digital controllers or time-varying elements which excite the resonance regions of distributed parameters. An electro-mechanical transformer which consists of constant current motor and synchronous generator is applied as another example. Inductance between stator windings and rotor of the synchronous generator serves as a periodical time-varying parameter and long electrical line plays a role of an element with distributed parameters. Instability condition of the transformer rotation owing to the parametric resonance excitement was obtained.
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Werner, Stefan, and Yih-Fang Huang. "Time- and coefficient- selective diffusion strategies for distributed parameter estimation." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190423.

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Werner, S., and Yih-Fang Huang. "Time- and coefficient-selective diffusion strategies for distributed parameter estimation." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757651.

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Abdelhedi, Abdessamad, Saadi Wided, Sbita Lassaad, and Driss Boutat. "Backstepping observer for distributed parameter systems." In 2015 World Congress on Information Technology and Computer Applications Congress (WCITCA). IEEE, 2015. http://dx.doi.org/10.1109/wcitca.2015.7367052.

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Lueschen, Gerhard G. G., and Lawrence A. Bergman. "Green’s Function Synthesis for Layered Distributed Parameter Systems." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0650.

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Abstract Dynamic Green’s functions for a class of layered distributed parameter systems are derived using a new method. The resulting system Green’s function, which is comprised of the elemental Green’s function of each of the substructures, defines the dynamics of the fully coupled system. Green’s functions for sandwiched beams with both identical and different layer properties are derived. The result retains the accuracy of the constituent elemental Green’s functions. The application of the method to other layered structures is immediate as long as the elemental Green’s functions of the substructures are known.
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Hwang, Jai Hyuk, Doo Man Kim, and Kyoung Ho Lim. "Robustness of Natural Controls of Distributed-Parameter Systems." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0576.

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Abstract In this paper, the effect of parameter and spatial discretization errors on the closed-loop behavior of distributed-parameter systems is analyzed for natural controls. If the control force designed on the basis of the postulated system with the parameter and discretization errors is applied to control the actual system, the closed-loop performance of the actual system will be degraded depending on the degree of the errors. The extent of deviation of the closed-loop performance from the expected one is derived and evaluated using operator techniques. It has been found that the extent of the deviation is proportional to the magnitude of the parameter and discretization errors, and that the proportional coefficient depends on the structures of the natural controls.
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Jiang, Feng, Jie Chen, and A. Lee Swindlehurst. "Parameter tracking via optimal distributed beamforming in an analog sensor network." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489255.

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Wang, Randi, and Vadim Shapiro. "Topological Semantics for Lumped Parameter Systems Modeling." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98181.

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Abstract Behaviors of many engineering systems are described by lumped parameter models that encapsulate the spatially distributed nature of the system into networks of lumped elements; the dynamics of such a network is governed by a system of ordinary differential and algebraic equations. Languages and simulation tools for modeling such systems differ in syntax, informal semantics, and in the methods by which such systems of equations are generated and simulated, leading to numerous interoperability challenges. We propose to unify semantics of all such systems using standard notions from algebraic topology. In particular, Tonti diagrams classify all physical theories in terms of physical laws (topological and constitutive) defined over a pair of dual cochain complexes and may be used to describe different types of lumped parameter systems. We show that all possible methods for generating the corresponding state equations within each physical domain correspond to paths over Tonti diagrams. We further propose a generalization of Tonti diagram that captures the behavior and supports canonical generation of state equations for multi-domain lumped parameter systems. The unified semantics provides a basis for greater interoperability in systems modeling, supporting automated translation, integration, reuse, and numerical simulation of models created in different authoring systems and applications. Notably, the proposed algebraic topological semantics is also compatible with spatially and temporally distributed models that are at the core of modern CAD and CAE systems.
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Miller, Tamara Guerra, Songcen Xu, Rodrigo C. de Lamare, Vitor H. Nascimento, and Yuriy Zakharov. "Sparsity-aware distributed conjugate gradient algorithms for parameter estimation over sensor networks." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421407.

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Reports on the topic "Distributed parameter systems (Computers)"

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Seidman, Thomas I. Control Theory and Distributed Parameter Systems. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada182808.

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Hovakimyan, Naira. Reduced Order Adaptive Controllers for Distributed Parameter Systems. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438582.

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Banks, H. T. Modeling and Control in Distributed Parameter Physical Systems. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada346461.

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Smith, Ralph C. Conference on Future Directions in Distributed Parameter Systems. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387596.

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Seidman, Thomas I. Optimal Design and Control of Distributed Parameter Systems. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada254947.

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Wittie, Larry D. Portable Operating Systems for Network Computers: Distributed Operating Systems Support for Group Communications. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada170113.

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Fitzpatrick, Ben G. Statistical Techniques for Modeling, Estimation and Optimization in Distributed Parameter Systems. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada383799.

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Lasiecka, I., and R. Triggiani. Control and Stabilization of Distributed Parameter Systems; Theoretical and Computational Aspects. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada277239.

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9

Russell, David. Instrumentation to Provide an Active Control Capability for Distributed Parameter Systems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada190043.

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Kevrekidis, Ioannis G. Enabling-Dynamic Simulators: Stability, Bifurcation and Control Computations for Distributed Parameter Systems. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada405411.

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