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Journal articles on the topic '029902 Complex Physical Systems'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Kwapień, Jarosław, and Stanisław Drożdż. "Physical approach to complex systems." Physics Reports 515, no. 3-4 (June 2012): 115–226. http://dx.doi.org/10.1016/j.physrep.2012.01.007.

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2

Fu, Jun, Jin Zhao Wu, Ning Zhou, and Hong Yan Tan. "Quantitative Models for Complex Physical Systems." Advanced Materials Research 1061-1062 (December 2014): 1144–47. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.1144.

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We present a quantitative model, called metric hybrid automata, for quantifying the behaviors of complex physical systems, such as chemical reaction control systems, manufacturing systems etc. Due to the introduction of a metric, the state space of hybrid automata forms a metric space, in which the difference of states can be quantified. Furthermore, in order to reveal the distance of system behaviors, we construct the simulation distance and the bisimulation distance, which quantify the similarity of system behaviors. Our model provides the basis for quantitative analysis for those complex physical systems.
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3

Uzhva, Denis, and Oleg Granichin. "Cluster control of complex cyber-physical systems." Cybernetics and Physics, Volume 10, 2021, Number 3 (November 30, 2021): 191–200. http://dx.doi.org/10.35470/2226-4116-2021-10-3-191-200.

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To our minds, the real world appears as a composition of different interacting entitites, which demonstrate complex behavior. In the current paper, we primarly aim to study such networked systems by developing corresponding approaches to modeling them, given a class of tasks. We derive it from the primary concept of information and a system, with corresponding dynamics emerging from interactions between system components. As we progress through the study, we discover three possible levels of certain synchronous pattern composition in complex systems: microscopic (the level of elementary components), mesoscopic (the level of clusters), and macroscopic (the level of the whole system). Above all, we focus on the clusterization phenomenon, which allows to reduce system complexity by regarding only a small number of stable manifolds, corresponding to cluster synchronization of system component states—as opposed to regarding the system as a whole or each elementary component separately. Eventually, we demonstrate how an optimization problem for cluster control synthesis can be formulated for a simple discrete linear system with clusterization.
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4

Metta, Giorgio, and Giulio Sandini. "Embodiment and complex systems." Behavioral and Brain Sciences 24, no. 6 (December 2001): 1068–69. http://dx.doi.org/10.1017/s0140525x01410120.

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In agreement with the target article, we would like to point out a few aspects related to embodiment which further support the position of biorobotics. We argue that, especially when complex systems are considered, modeling through a physical implementation can provide hints to comprehend the whole picture behind the specific set of experimental data.
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5

Kharitonov, O. V., L. A. Firsova, and E. A. Kozlitin. "Simulating Complex Displacement Chromatography Systems." Russian Journal of Physical Chemistry A 93, no. 4 (April 2019): 758–64. http://dx.doi.org/10.1134/s0036024419040162.

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6

Ebeling, W. "Predictability of Complex Dynamical Systems." Zeitschrift für Physikalische Chemie 206, Part_1_2 (January 1998): 274. http://dx.doi.org/10.1524/zpch.1998.206.part_1_2.274.

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7

Katina, Polinpapilinho F., Charles B. Keating, Adrian V. Gheorghe, and Marcelo Masera. "Complex system governance for critical cyber-physical systems." International Journal of Critical Infrastructures 13, no. 2/3 (2017): 168. http://dx.doi.org/10.1504/ijcis.2017.088230.

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8

Keating, Charles B., Adrian V. Gheorghe, Polinpapilinho F. Katina, and Marcelo Masera. "Complex system governance for critical cyber-physical systems." International Journal of Critical Infrastructures 13, no. 2/3 (2017): 168. http://dx.doi.org/10.1504/ijcis.2017.10009243.

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9

David, Pierre, Vincent Idasiak, and Frédéric Kratz. "Reliability study of complex physical systems using SysML." Reliability Engineering & System Safety 95, no. 4 (April 2010): 431–50. http://dx.doi.org/10.1016/j.ress.2009.11.015.

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10

Zambrano, Samuel, and Miguel A. F. Sanjuán. "Infinite horseshoes and complex dynamics in physical systems." Communications in Nonlinear Science and Numerical Simulation 22, no. 1-3 (May 2015): 866–71. http://dx.doi.org/10.1016/j.cnsns.2014.07.013.

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11

Gielen, Stan. "Cellular automata and modelling of complex physical systems." Computer Physics Communications 61, no. 3 (December 1990): 433–35. http://dx.doi.org/10.1016/0010-4655(90)90055-6.

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12

Sellgren, U., and D. Williamsson. "ARCHITECTING COMPLEX ENGINEERED SYSTEMS." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 2415–24. http://dx.doi.org/10.1017/dsd.2020.335.

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AbstractNovel products are commonly realized by integrating heterogeneous technologies. Product architecting focus on defining the scheme by which the product functions are allocated to physical components. A DSM-based clustering method that integrates technical complexity and strategic concerns has previously been proposed. It has been shown that interaction weights in the DSM may affect the clustering result. A complexity-based interaction strength model to be used in DSM clustering is proposed here. The case study gives promising results from both interaction performance and safety points of view.
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13

Margalli, Carlos A., and J. David Vergara. "From complex holomorphic systems to real systems." International Journal of Modern Physics A 35, no. 13 (May 10, 2020): 2050065. http://dx.doi.org/10.1142/s0217751x20500657.

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Symmetries in modern physics are a fundamental subject of high relevance that allows appreciating more deeply the physical structure of a theory. In this paper, we analyze a gauge symmetry that appears in complex holomorphic systems. We show that a complex system can be reduced to different real systems, using different gauge conditions, and the gauge transformations connect several real systems in the complex space. We prove that the space of solutions of one system is related using a gauge transformation to another one. Gauge transformations are, in some cases, canonical transformations. However, in other cases, these are more general transformations that change the symplectic structure, but there is still a map between systems. We establish a construction to extend the analysis to the quantum case using path integrals through the Batalin–Fradkin–Vilkovisky theorem and within the canonical formalism, where we show explicitly that solutions of the Schrödinger equation are gauge-related.
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14

Ledukhovsky, G. V., V. P. Zhukov, and E. V. Barochkin. "Regularization of physical gas flows in complex power systems." Vestnik IGEU, no. 6 (2016): 5–15. http://dx.doi.org/10.17588/2072-2672.2016.6.005-015.

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15

Thanachareonkit, Anothai, and Jean-Louis Scartezzini. "Modelling Complex Fenestration Systems using physical and virtual models." Solar Energy 84, no. 4 (April 2010): 563–86. http://dx.doi.org/10.1016/j.solener.2009.09.009.

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16

Biswas, G., S. Manganaris, and X. Yu. "Extending component connection modeling for analyzing complex physical systems." IEEE Expert 8, no. 1 (February 1993): 48–57. http://dx.doi.org/10.1109/64.193055.

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17

Shu, Zhaogang, Jiafu Wan, Daqiang Zhang, and Di Li. "Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications." Mobile Networks and Applications 21, no. 5 (November 28, 2015): 865–78. http://dx.doi.org/10.1007/s11036-015-0664-6.

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18

Jansen, Thomas L. C. "Computational spectroscopy of complex systems." Journal of Chemical Physics 155, no. 17 (November 7, 2021): 170901. http://dx.doi.org/10.1063/5.0064092.

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Numerous linear and non-linear spectroscopic techniques have been developed to elucidate structural and functional information of complex systems ranging from natural systems, such as proteins and light-harvesting systems, to synthetic systems, such as solar cell materials and light-emitting diodes. The obtained experimental data can be challenging to interpret due to the complexity and potential overlapping spectral signatures. Therefore, computational spectroscopy plays a crucial role in the interpretation and understanding of spectral observables of complex systems. Computational modeling of various spectroscopic techniques has seen significant developments in the past decade, when it comes to the systems that can be addressed, the size and complexity of the sample types, the accuracy of the methods, and the spectroscopic techniques that can be addressed. In this Perspective, I will review the computational spectroscopy methods that have been developed and applied for infrared and visible spectroscopies in the condensed phase. I will discuss some of the questions that this has allowed answering. Finally, I will discuss current and future challenges and how these may be addressed.
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19

Sebestyen, Gheorghe, and Anca Hangan. "Anomaly detection techniques in cyber-physical systems." Acta Universitatis Sapientiae, Informatica 9, no. 2 (December 20, 2017): 101–18. http://dx.doi.org/10.1515/ausi-2017-0007.

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AbstractNowadays, when multiple aspects of our life depend on complex cyber-physical systems, automated anomaly detection, prevention and handling is a critical issue that inuence our security and quality of life. Recent catastrophic events showed that manual (human-based) handling of anomalies in complex systems is not recommended, automatic and intelligent handling being the proper approach. This paper presents, through a number of case studies, the challenges and possible solutions for implementing computer-based anomaly detection systems.
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20

Zhao, Xiu, Jian Liu, Fangfang Zhang, and Cuimei Jiang. "Complex generalized synchronization of complex-variable chaotic systems." European Physical Journal Special Topics 230, no. 7-8 (June 11, 2021): 2035–41. http://dx.doi.org/10.1140/epjs/s11734-021-00129-6.

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21

Alberty, Robert A. "Chemical equilibrium in complex organic systems." Journal of Physical Chemistry 89, no. 5 (February 1985): 880–83. http://dx.doi.org/10.1021/j100251a033.

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22

Baretta, Roberto, Lee Brammer, Andrew D. Burrows, David Farrusseng, Satoshi Horike, Jianwen Jiang, Masako Kato, et al. "Towards complex systems and devices: general discussion." Faraday Discussions 225 (2021): 431–41. http://dx.doi.org/10.1039/d0fd90036b.

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23

Hammes-Schiffer, Sharon. "Quantum effects in complex systems: summarizing remarks." Faraday Discussions 221 (2020): 582–88. http://dx.doi.org/10.1039/c9fd00097f.

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Quantum mechanical phenomena such as coherence, spin dynamics, and tunneling have been observed in biological, electrochemical, polymeric, and many other condensed phase processes. This paper summarizes the diverse contributions to the Faraday Discussion on quantum effects in complex systems.
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24

Hofacker, G. L., and R. D. Levine. "The Evolution of Complex Systems." Zeitschrift für Naturforschung A 43, no. 1 (January 1, 1988): 73–77. http://dx.doi.org/10.1515/zna-1988-0110.

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Abstract A principle of evolution of highly complex systems is proposed. It is based on extremal properties of the information I (X, Y) characterizing two states X and Y with respect to each other, I(X, Y) = H(Y) -H(Y/X), where H(Y) is the entropy of state Y,H (Y/X) the entropy in state Y given the probability distribu­tion P(X) and transition probabilities P(Y/X).As I(X, Y) is maximal in P(Y) but minimal in P(Y/X), the extremal properties of I(X, Y) con­stitute a principle superior to the maximum entropy principle while containing the latter as a special case. The principle applies to complex systems evolving with time where fundamental equations are unknown or too difficult to solve. For the case of a system evolving from X to Y it is shown that the principle predicts a canonic distribution for a state Y with a fixed average energy .
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25

PAK, CHAEHO, YAOMING XIE, and HENRY F. SCHAEFER. "III: PROPERTIES OF COMPLEX SYSTEMS." Molecular Physics 101, no. 1-2 (January 10, 2003): 211–25. http://dx.doi.org/10.1080/0026897021000026845.

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26

Baas, N. A. "New structures in complex systems." European Physical Journal Special Topics 178, no. 1 (November 2009): 25–44. http://dx.doi.org/10.1140/epjst/e2010-01180-8.

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27

San Miguel, M., J. H. Johnson, J. Kertesz, K. Kaski, A. Díaz-Guilera, R. S. MacKay, V. Loreto, P. Érdi, and D. Helbing. "Challenges in complex systems science." European Physical Journal Special Topics 214, no. 1 (November 2012): 245–71. http://dx.doi.org/10.1140/epjst/e2012-01694-y.

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28

Basios, V., S. C. Nicolis, and J. L. Deneubourg. "Coordinated aggregation in complex systems:." European Physical Journal Special Topics 225, no. 6-7 (September 2016): 1143–47. http://dx.doi.org/10.1140/epjst/e2016-02660-5.

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29

Petrushevskaya, Anastasia, Gennady Korshunov, Sergey Polyakov, and Artemy Varzhapetyan. "Assessment of target and usefulness of complex cyber-physical systems." International Journal of Risk Assessment and Management 24, no. 1 (2021): 42. http://dx.doi.org/10.1504/ijram.2021.10043712.

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30

Rozentsvaig, Alexander. "HYBRID MODELING OF COMPLEX PHYSICAL PROCESSES IN DISPERSED LIQUID SYSTEMS." JP Journal of Heat and Mass Transfer 26 (March 2, 2022): 53–59. http://dx.doi.org/10.17654/0973576322012.

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31

Korshunov, Gennady, Artemy Varzhapetyan, Anastasia Petrushevskaya, and Sergey Polyakov. "Assessment of target and usefulness of complex cyber-physical systems." International Journal of Risk Assessment and Management 24, no. 1 (2021): 42. http://dx.doi.org/10.1504/ijram.2021.119960.

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32

Winsberg, Eric. "Simulations, Models, and Theories: Complex Physical Systems and Their Representations." Philosophy of Science 68, S3 (September 2001): S442—S454. http://dx.doi.org/10.1086/392927.

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33

Klafter, J., G. Zumofen, and A. Blumen. "Non-Brownian transport in complex systems." Chemical Physics 177, no. 3 (December 1993): 821–29. http://dx.doi.org/10.1016/0301-0104(93)85044-9.

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34

Brändas, Erkki J. "Quantum concepts and complex systems." International Journal of Quantum Chemistry 98, no. 2 (2004): 78–86. http://dx.doi.org/10.1002/qua.10830.

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35

Bila, Jiri, Ali H. Reshak, and Jan Chysky. "Modeling Complex Systems by Structural Invariants Approach." Complexity 2021 (September 4, 2021): 1–17. http://dx.doi.org/10.1155/2021/6650619.

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When modeling complex systems, we usually encounter the following difficulties: partiality, large amount of data, and uncertainty of conclusions. It can be said that none of the known approaches solves these difficulties perfectly, especially in cases where we expect emergences in the complex system. The most common is the physical approach, sometimes reinforced by statistical procedures. The physical approach to modeling leads to a complicated description of phenomena associated with a relatively simple geometry. If we assume emergences in the complex system, the physical approach is not appropriate at all. In this article, we apply the approach of structural invariants, which has the opposite properties: a simple description of phenomena associated with a more complicated geometry (in our case pregeometry). It does not require as much data and the calculations are simple. The price paid for the apparent simplicity is a qualitative interpretation of the results, which carries a special type of uncertainty. Attention is mainly focused (in this article) on the invariant matroid and bases of matroid (M, BM) in combination with the Ramsey graph theory. In addition, this article introduces a calculus that describes the emergent phenomenon using two quantities—the power of the emergent phenomenon and the complexity of the structure that is associated with this phenomenon. The developed method is used in the paper for modeling and detecting emergent situations in cases of water floods, traffic jams, and phase transition in chemistry.
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36

Agharazi, Hanieh, Wanchat Theeranaew, Kolacinski Richard M., and Kenneth A. Lopaor. "An Information-Theoretic Framework for Complex Systems." Mechanical Engineering 140, no. 12 (December 1, 2018): S16—S23. http://dx.doi.org/10.1115/1.2018-dec-7.

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We propose an information-theoretic framework for modeling complex systems as a communication network where physical devices can be organized into subsystems and subsystems are communicating through an information channel governed by the dynamics of the system.
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37

Belov, G. V. "Determining the Phase Composition of Complex Thermodynamic Systems." Russian Journal of Physical Chemistry A 93, no. 6 (June 2019): 1017–23. http://dx.doi.org/10.1134/s0036024419060074.

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38

Ren, Zhuyin, and Stephen B. Pope. "Reduced Description of Complex Dynamics in Reactive Systems." Journal of Physical Chemistry A 111, no. 34 (August 2007): 8464–74. http://dx.doi.org/10.1021/jp0717950.

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39

Wen, Guanghui, Mahdi Jalili, Wei Ren, Yongcan Cao, Haibo Du, and Guanrong Chen. "Analysis and control of complex cyber‐physical networks." Asian Journal of Control 24, no. 2 (March 2022): 495–97. http://dx.doi.org/10.1002/asjc.2820.

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40

Sun, Meng. "Challenges on Coordination for Cyber-Physical Systems." Applied Mechanics and Materials 347-350 (August 2013): 2942–46. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2942.

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Cyber-physical systems integrate computing and communication with monitoring and control of physical entities. The complex interaction with the physical world makes coordination models and languages very important for the analysis, design and development of cyber-physical systems. This paper discusses several new challenges on coordination models and languages in the context of the emerging phenomenon of cyber-physical systems.
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41

Kuang, Zhe Jun, Liang Hu, and Chen Zhang. "Characteristic Analyzation of Cyber Physical Systems." Applied Mechanics and Materials 484-485 (January 2014): 427–30. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.427.

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Cyber-physical systems (CPS) are complex distributed heterogeneous systems which integrating cyber and physical processes by computation, communication and control. During interaction between cyber and physical world, the traditional theories and applications has been difficult to satisfy real-time performance and efficient. Cyber-physical systems clearly have a role to play in developing a new theory of computer-mediated physical systems. The aim of this work is to analysis the features and relation technology of CPS that get better understanding for this new field. We summarized the research progresses from different perspectives such as modeling, classical tools and applications. Finally, the research challenges for CPS are in brief outlined.
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42

Svyantek, Daniel J., and Linda L. Brown. "A Complex-Systems Approach to Organizations." Current Directions in Psychological Science 9, no. 2 (April 2000): 69–74. http://dx.doi.org/10.1111/1467-8721.00063.

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The physical sciences have developed new theories of nonlinear behavior of complex systems. Defining characteristics of complex systems include (a) being composed of many variables that interact strongly to determine system behavior, (b) sensitivity to initial conditions, and (c) stability across time. Two complex-system concepts, phase spaces and attractors, provide insight into the evolution of system behavior and make prediction of future behavior possible. It is proposed that complex-systems research has application to the study of organizations and social behavior. Organizational attractors exist and seem to be both sensitive to initial conditions and stable. The discussion of concepts from complex systems, and their application to organizations, provides insight into how organizational research should be conducted. If organizations are assumed to exhibit nonlinear behavior, more historical, longitudinal, and qualitative research methods should be used to provide context-specific descriptions of organizational behavior.
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43

OTSUKA, KENJU. "COMPLEX DYNAMICS IN COUPLED NONLINEAR ELEMENT SYSTEMS." International Journal of Modern Physics B 05, no. 08 (May 10, 1991): 1179–214. http://dx.doi.org/10.1142/s0217979291000572.

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This paper reviews complex dynamics which arise through the interaction of simple nonlinear elements without chaotic response, including self-induced switching among local attractors (chaotic itinerancy) and related phenomena. Several realistic physical systems consisting of coupled nonlinear elements are considered on the basis of computer experiments: coupled nonlinear oscillator (e.g., discrete complex time-dependent Ginzburg-Landau equation) systems, coupled laser arrays, and a coupled multistable optical chain model.
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44

Shcherbinin, Victor V., Ravil R. Zagidullin, Georgy A. Kvetkin, and Marina S. Sidorova. "Computer Simulation of Complex Electronic Systems of Navigation Systems." ITM Web of Conferences 35 (2020): 04021. http://dx.doi.org/10.1051/itmconf/20203504021.

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The paper proposes ways to reduce the time and improve the quality of development of electronic equipment using computer simulation. The paper considers an example of the development and debugging of a radio-electronic module, which has found its direct application in a local navigation system that operates on the basis of the method of point landmarks. The paper considers a hardware-software complex for the implementation of the study of the health of both individual functional units and radio rangefinder equipment as a whole, as well as for the modernization of the radio-electronic module of the navigation system. The various computer simulation environments available today allow the development and design of electronic equipment at various levels. In the proposed work, computer simulation was carried out in MATLAB / Simulink, LabVIEW, Multisim and MicroCap. The work also provided for the possibility of using data collection and processing modules, which provides the opportunity in one software development and modeling environment to compare the results obtained using simulation modeling and physical research of the object. The result of the work is a hardwaresoftware kit of the radio-electronic module of the navigation system through which it is possible to evaluate the influence of various external influences on the accuracy of determining the range from the interrogator (moving object) to the transponder (stationary beacon).
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45

Akbarzadeh, Aida, and Sokratis Katsikas. "Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems." Sensors 21, no. 5 (March 1, 2021): 1685. http://dx.doi.org/10.3390/s21051685.

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Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.
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46

Blanke, M., and M. Knudsen. "EFFICIENT PARAMETERIZATION FOR GREY-BOX MODEL IDENTIFICATION OF COMPLEX PHYSICAL SYSTEMS." IFAC Proceedings Volumes 39, no. 1 (2006): 338–43. http://dx.doi.org/10.3182/20060329-3-au-2901.00049.

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47

Gaylord, Richard J., Paul R. Wellin, Bill Titus, Susan R. McKay, and Wolfgang Christian. "Computer Simulations with Mathematica: Explorations in Complex Physical and Biological Systems." Computers in Physics 10, no. 4 (1996): 350. http://dx.doi.org/10.1063/1.4822455.

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48

Soumelidis, A., and I. Nagy. "Intelligent Modelling of Complex Physical Systems: Application in Diagnostics of NPP's." IFAC Proceedings Volumes 23, no. 8 (August 1990): 13–17. http://dx.doi.org/10.1016/s1474-6670(17)51794-2.

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49

Stanislavsky, Aleksander, Karina Weron, and Aleksander Weron. "Anomalous diffusion approach to non-exponential relaxation in complex physical systems." Communications in Nonlinear Science and Numerical Simulation 24, no. 1-3 (July 2015): 117–26. http://dx.doi.org/10.1016/j.cnsns.2015.01.001.

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50

Xia, Yongxiang, Michael Small, and Jiajing Wu. "Introduction to Focus Issue: Complex Network Approaches to Cyber-Physical Systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 9 (September 2019): 093123. http://dx.doi.org/10.1063/1.5126230.

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