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Relevant bibliographies by topics / Identification of multimass systems

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Academic literature on the topic 'Identification of multimass systems'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Identification of multimass systems"

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Gennady, Kaniuk, Vasylets Tetiana, Varfolomiyev Oleksiy, Mezerya Andrey, and Antonenko Nataliia. "Development of neuralnetwork and fuzzy models of multimass electromechanical systems." Eastern-European Journal of Enterprise Technologies 3, no. 2(99) (2019): 51–63. https://doi.org/10.15587/1729-4061.2019.169080.

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The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study. A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesiz

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Rau, Reina, and Andrew J. Darwin. "Identification of YsaP, the Pilotin of the Yersinia enterocolitica Ysa Type III Secretion System." Journal of Bacteriology 197, no. 17 (2015): 2770–79. http://dx.doi.org/10.1128/jb.00238-15.

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ABSTRACTSecretins are multimeric outer membrane pore-forming proteins found in complex export systems in Gram-negative bacteria. All type III secretion systems (T3SSs) have a secretin, and one of these is the YsaC secretin of the chromosomally encoded Ysa T3SS ofYersinia enterocolitica. In some cases, pilotin proteins, which are outer membrane lipoproteins, are required for their cognate secretins to multimerize and/or localize to the outer membrane. However, if secretin multimers mislocalize to the inner membrane, this can trigger the protective phage shock protein (Psp) stress response. Duri

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Kuznetsov, B. I., T. B. Nikitina, I. V. Bovdui, O. V. Voloshko, V. V. Kolomiets, and B. B. Kobylianskyi. "The method of multi objective synthesis of nonlinear robust control by multimass electromechanical systems." Electrical Engineering & Electromechanics, no. 4 (July 8, 2022): 12–20. http://dx.doi.org/10.20998/2074-272x.2022.4.02.

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Aim. Development of the method of multi objective synthesis of nonlinear robust control by multimass electromechanical systems to satisfy various requirements for the operation of multi-mass systems in various modes. Methodology. The problem of multi objective synthesis of nonlinear robust control of multimass electromechanical systems is formulated and the possibility of satisfying various requirements for the operation of such systems in various modes based on the concept of functionally multiple membership of the state vector and the solution of the Hamilton-Jacobi-Isaacs equation is shown.

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B., I. Kuznetsov, B. Nikitina T., V. Bovdui I., V. Voloshko O., V. Kolomiets V., and B. Kobylianskyi B. "The method of multi objective synthesis of nonlinear robust control by multimass electromechanical systems." Electrical Engineering & Electromechanics, no. 4 (July 8, 2022): 12–20. https://doi.org/10.20998/2074-272X.2022.4.02.

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<strong><em>Aim.</em></strong><em> Development of the method of multi objective synthesis of nonlinear robust control by multimass electromechanical systems to satisfy various requirements for the operation of multi-mass systems in various modes. <strong>Methodology. </strong>The problem of multi objective synthesis of nonlinear robust control of multimass electromechanical systems is formulated and the possibility of satisfying various requirements for the operation of such systems in various modes based on the concept of functionally multiple membership of the state vector and

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Kuznetsov, B. I., T. B. Nikitina, I. V. Bovdui, O. V. Voloshko, V. V. Kolomiets, and B. B. Kobylianskyi. "The method of multi objective synthesis of stochastic robust control by multimass electromechanical systems under non-gausian random external disturbances." Electrical Engineering & Electromechanics, no. 5 (September 6, 2022): 21–30. http://dx.doi.org/10.20998/2074-272x.2022.5.04.

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Aim. Development of the method of multi objective synthesis of stochastic robust control by multimass electromechanical systems to satisfy various requirements for the operation of such systems in various modes under non-gausian random external disturbances. Methodology. The problem of multi objective synthesis of stochastic robust control by multimass electromechanical systems to satisfy various requirements for the operation of such systems in various modes under non-gausian random external disturbances solved based on the choosing of weight matrices in the robust control goal vector.The cal

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B., I. Kuznetsov, B. Nikitina T., V. Bovdui I., V. Voloshko O., V. Kolomiets V., and B. Kobylianskyi B. "The method of multi objective synthesis of stochastic robust control by multimass electromechanical systems under non-gausian random external disturbances." Electrical Engineering & Electromechanics, no. 5 (September 7, 2022): 21–30. https://doi.org/10.20998/2074-272X.2022.5.04.

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<strong><em>Aim</em></strong><em>. Development of the method of multi objective synthesis of stochastic robust control by multimass electromechanical systems to satisfy various requirements for the operation of such systems in various modes under non-gausian random external disturbances. <strong>Methodology</strong>. The problem of multi objective synthesis of stochastic robust control by multimass electromechanical systems to satisfy various requirements for the operation of such systems in various modes under non-gausian random external disturbances</em> <em>solved based on the cho

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Tiongco, Maria, Angela Collier, and Anna Lisa Varri. "Central dynamics of multimass rotating star clusters." Monthly Notices of the Royal Astronomical Society 506, no. 3 (2021): 4488–98. http://dx.doi.org/10.1093/mnras/stab1968.

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ABSTRACT We investigate the evolutionary nexus between the morphology and internal kinematics of the central regions of collisional, rotating, multimass stellar systems, with special attention to the spatial characterization of the process of mass segregation. We report results from idealized, purely N-body simulations that show multimass, rotating, and spherical systems rapidly form an oblate, spheroidal massive core, unlike single-mass rotating, or multimass non-rotating configurations with otherwise identical initial properties, indicating that this evolution is a result of the interplay be

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Sahoo, Arpita, Debangshu Mukherjee, Dhrubajyoti Mahata, and Gayatri Mukherjee. "Peptide–MHC complexes: dressing up to manipulate T cells against autoimmunity and cancer." Immunotherapy 14, no. 5 (2022): 337–50. http://dx.doi.org/10.2217/imt-2021-0230.

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Antigen-specificity of T cells provides important clues to the pathogenesis of T cell-mediated autoimmune diseases and immune-evasion strategies of tumors. Identification of T cell clones involved in autoimmunity or cancer is achieved with soluble peptide–MHC (pMHC) complex multimers. Importantly, these complexes can also be used to manipulate disease-relevant T cells to restore homeostasis of T cell-mediated immune response. While auto-antigen-specific T cells can be deleted or anergized by T cell receptor engagement with cognate pMHC complexes in the absence of costimulation, integration of

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Manyumwa, Colleen Varaidzo, Reza Zolfaghari Emameh, and Özlem Tastan Bishop. "Alpha-Carbonic Anhydrases from Hydrothermal Vent Sources as Potential Carbon Dioxide Sequestration Agents: In Silico Sequence, Structure and Dynamics Analyses." International Journal of Molecular Sciences 21, no. 21 (2020): 8066. http://dx.doi.org/10.3390/ijms21218066.

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With the increase in CO2 emissions worldwide and its dire effects, there is a need to reduce CO2 concentrations in the atmosphere. Alpha-carbonic anhydrases (α-CAs) have been identified as suitable sequestration agents. This study reports the sequence and structural analysis of 15 α-CAs from bacteria, originating from hydrothermal vent systems. Structural analysis of the multimers enabled the identification of hotspot and interface residues. Molecular dynamics simulations of the homo-multimers were performed at 300 K, 363 K, 393 K and 423 K to unearth potentially thermostable α-CAs. Average be

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Pielorz, Amalia, and Monika Skóra. "Modeling of multimass systems torsionally deformed with variable inertia." Differential Equations and Nonlinear Mechanics 2006 (2006): 1–11. http://dx.doi.org/10.1155/denm/2006/20758.

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Dynamic investigations of multimass discrete-continuous systems having variable moment of inertia are performed. The systems are torsionally deformed and consist of an arbitrary number of elastic elements connected by rigid bodies. The problem is nonlinear and it is linearized after appropriate transformations. It is shown that such problems can be investigated using the wave approach. Some analytical considerations and numerical calculations are done for a two-mass system with a special case of boundary conditions.

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Dissertations / Theses on the topic "Identification of multimass systems"

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Nguyên, Thiên-Lôc 1978. "National identification systems." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87367.

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Bennia, Abdelhak. "Mimo systems parameters identification." Thesis, Virginia Tech, 1986. http://hdl.handle.net/10919/41579.

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<p>In this thesis, a presentation of a new canonical representation of multi-input multioutput systems is given. The new characterization covers the full range of practical situations in linear systems according to the structural properties and model of the perturbations which are known. Its direct link to ARMA processes as well as to classical state space representation ls also given.</p> <p> The importance of the new representation lies in the fact that all unknown parameters and state variables appear linearly multlplied by either external variables (inputs and outputs) that appear in the

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Hidayat, Egi. "On Identification of Biological Systems." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-215699.

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System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology. The first topic of the thesis is parameter estimation of the classi

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Hidayat, Egi. "On identification of endocrine systems." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227630.

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System identification finds nowadays application in various areas of biomedical research as a tool of empiric mathematical modeling and model individualization. Hormone regulation is a classical example of biological feedback where control theories, in general, and system identification, in particular, are indispensable in unraveling the regulation mechanisms and explicating the complex dynamical phenomena arising in endocrine systems. The main function of endocrine feedback regulation is to maintain the hormone levels within a particular physiological range as well as to sustain an appropriat

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Venkatesh, Saligrama Ramaswamy. "System-identification for complex-systems." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10440.

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Hancke, Gerhard P. "Security of proximity identification systems." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612509.

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Zhou, Linghui. "Polar Codes for Identification Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232176.

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Identication systems are ubiquitous, for example, biometric identication systemswith ngerprints and Face IDs, etc. Basically, the identication problemconsists of two steps. The enrollment phase where the user's data are captured,compressed and stored, for example taking the ngerprint or capturing some importantfeatures of your face. In the identication phase, an observation, yourngerprint or your face, is compared with the stored information in the databaseto provide an armative answer. Since the system involves many users, bothstoring and searching for the correct user is challenging.This pro

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Krauss, Ryan. "Experimental Identification of Nonlinear Systems." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36912.

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A procedure is presented for using a primary resonance excitation in experimentally identifying the nonlinear parameters of a model approximating the response of a cantilevered beam by a single mode. The model accounts for cubic inertia and stiffness nonlinearities and quadratic damping. The method of multiple scales is used to determine the frequency-response function for the system. Experimental frequency- and amplitude-sweep data are compared with the prediction of the frequency-response function in a least-squares curve-fitting algorithm. The algorithm is improved by maki

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Pepona, Eleni. "Identification of nonlinear interconnected systems." Thesis, Brunel University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540757.

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In this work we address the problem of identifying a discrete-time nonlinear system composed of a linear dynamical system connected to a static nonlinear component. We use linear fractional representation to provide a united framework for the identification of two classes of such systems. The first class consists of discrete-time systems consists of a linear time invariant system connected to a continuous nonlinear static component. The identification problem of estimating the unknown parameters of the linear system and simultaneously fitting a math order spline to the nonlinear data is addres

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Scotton, Francesco. "Modeling and Identification for HVAC Systems." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-101439.

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Heating, Ventilation and Air Conditioning (HVAC) systems consist of all the equipment that control the conditions and distribution of indoor air. Indoor air must be confortable and healthy for the occupants to maximize their productivity. Moreover, HVAC energy consumption is between 20% and 40% of the total energy consumption in developed countries and accounts around 33% of the global CO 2emissions. So the study of HVAC systems plays an important role in building science. The aim of this project is to identify mathematical models that will be employed by intelligent control algorithms which g

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Books on the topic "Identification of multimass systems"

1

inc, International Resource Development, ed. Identification & verification systems. International Resource Development, 1986.

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Maine, R. E. Identification of dynamic systems. Agard, 1985.

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Doraiswami, Rajamani, Chris Diduch, and Maryhelen Stevenson. Identification of Physical Systems. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118536483.

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Alberer, Daniel, Håkan Hjalmarsson, and Luigi del Re, eds. Identification for Automotive Systems. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2221-0.

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Isermann, Rolf, and Marco Münchhof. Identification of Dynamic Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-78879-9.

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Ten-Huei, Guo, Duyar Ahmet, and United States. National Aeronautics and Space Administration., eds. Identification of propulsion systems. NASA, 1991.

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Unbehauen, Heinz. Identification of continuous systems. North-Holland, 1987.

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Westwick, David T., and Robert E. Kearney. Identification of Nonlinear Physiological Systems. John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471722960.

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Van Overschee, Peter, and Bart De Moor. Subspace Identification for Linear Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0465-4.

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Heij, C., ed. Deterministic Identification of Dynamical Systems. Springer-Verlag, 1989. http://dx.doi.org/10.1007/bfb0043065.

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Book chapters on the topic "Identification of multimass systems"

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Krasnosel’skiǐ, Mark A., and Aleksei V. Pokrovskiǐ. "Identification theorem." In Systems with Hysteresis. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-61302-9_2.

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Vyas, J. Jaidev, Balamurugan Gopalsamy, and Harshavardhan Joshi. "System Identification." In Electro-Hydraulic Actuation Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2547-2_4.

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Gu, Guoxiang. "System Identification." In Discrete-Time Linear Systems. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2281-5_8.

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Keesman, Karel J. "Static Systems Identification." In Advanced Textbooks in Control and Signal Processing. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-522-4_5.

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Keesman, Karel J. "Dynamic Systems Identification." In Advanced Textbooks in Control and Signal Processing. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-522-4_6.

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Jelali, Mohieddine, and Andreas Kroll. "Experimental Modelling (Identification)." In Hydraulic Servo-systems. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0099-7_5.

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Westphal, L. C. "System identification." In Sourcebook of Control Systems Engineering. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_30.

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Van Overschee, Peter, and Bart De Moor. "Deterministic Identification." In Subspace Identification for Linear Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0465-4_2.

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Van Overschee, Peter, and Bart De Moor. "Stochastic Identification." In Subspace Identification for Linear Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0465-4_3.

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Westphal, Louis C. "System identification." In Handbook of Control Systems Engineering. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_30.

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Conference papers on the topic "Identification of multimass systems"

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Rahul, Dakshayani Araddy, and Venkatesh. "Trust-Object Identification: Approach Towards Trustworthy Objects Identification in S-IoT." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616460.

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Friedland, Bernard, Sofia Mentzelopoulou, and Young-Jin Park. "Friction Estimation in Multimass Systems." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793213.

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Sulikowski, Bartlomiej, Daniel Trzcinski, Krzysztof Gatkowski, and Eric Rogers. "Modeling and control of multimass systems in terms of 2D systems*." In 2020 Mechatronics Systems and Materials (MSM). IEEE, 2020. http://dx.doi.org/10.1109/msm49833.2020.9202178.

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Kolesnikov, Artem, Evgeny Glushkin, Pavel Podborsky, and Stefan Winternheimer. "A new method of adaptive predictive control in multimass electromechanical systems with variable parameters." In 2007 European Conference on Power Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/epe.2007.4417630.

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Vislousova, I. N., V. V. Kotov, O. N. Lesnjak, A. A. Matrosov, and A. A. Kotova. "CORRELATION METHOD FOR ESTIMATING DYNAMIC LOADING OF DRIVES OF ACTIVE WORKING ORGANS OF HARVEST MACHINES." In STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS. DSTU-PRINT, 2020. http://dx.doi.org/10.23947/interagro.2020.1.75-80.

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The correlation method of a research of dynamics of multimass systems with nonlinear performances is considered.The method is based on immediate transformation of correlation functions of nonlinear terms of the equations of small oscillations. The method is applicable for systems with nonanalytic nonlinear functions. The considered methods allow investigate dynamic processes in drives of active working organs of harvest machines.

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Vu, Minh Thanh, Tobias J. Oechtering, and Mikael Skoglund. "Testing in Identification Systems." In 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613310.

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Brouri, Adil. "Identification of nonlinear systems." In APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science. Author(s), 2017. http://dx.doi.org/10.1063/1.4981971.

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Hoffmann, I., and S. Engell. "Identification of hybrid systems." In Proceedings of the 1998 American Control Conference (ACC). IEEE, 1998. http://dx.doi.org/10.1109/acc.1998.703499.

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Brouri, Adil, Laila Kadi, and Smail Slassi Sennou. "Identification of Nonlinear Systems." In 2017 European Conference on Electrical Engineering and Computer Science (EECS). IEEE, 2017. http://dx.doi.org/10.1109/eecs.2017.59.

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Vu, Minh Thanh, Tobias J. Oechtering, Mikael Skoglund, and Holger Boche. "Uncertainty in Identification Systems." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437760.

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Reports on the topic "Identification of multimass systems"

1

Pearson, Allan E. Control and Identification of Time Varying Systems. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177567.

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Pearson, A. E. Control and Identification of Time Varying Systems. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada159067.

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Jung, Kiwook, Katherine Morris, Kevin W. Lyons, Swee Leong, and Hyunbo Cho. Performance Challenges Identification Method for Smart Manufacturing Systems. National Institute of Standards and Technology, 2016. http://dx.doi.org/10.6028/nist.ir.8108.

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Cicci, David A., John E. Cochran, and Jr. Identification and Motion Prediction of Tethered Satellite Systems. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387974.

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Karygiannis, A. T., B. Eydt, G. Barber, L. Bunn, and T. Phillips. Guidelines for securing Radio Frequency Identification (RFID) systems. National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.sp.800-98.

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Godil, Afzal, Roger Eastman, and Tsai Hong Hong. Review of Research on the Ground Truth Systems for Evaluating Part Identification Systems. National Institute of Standards and Technology, 2013. http://dx.doi.org/10.6028/nist.ir.7926.

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Massoumnia, Mohammad-Ali, George C. Verghese, and Alan S. Willsky. Failure Detection and Identification in Linear Time-Invariant Systems,. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada188277.

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Georgiou, Tryphon T. Aspects of Modeling, Identification and Control of Dynamical Systems. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada299411.

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Basar, Tamer. Performance-Driven Robust Identification and Control of Uncertain Dynamical Systems. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/900284.

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Zhuang, Y., and J. S. Baras. Identification of Infinite Dimensional Systems via Adaptive Wavelet Neural Networks. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada454923.

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