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Journal articles on the topic 'Multi-model systems'

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Published: 18 April 2023

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1

Green, A. M., and P. Pennanen. "A model for multi-quark systems." Physics Letters B 426, no. 3-4 (May 1998): 243–50. http://dx.doi.org/10.1016/s0370-2693(98)00300-1.

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2

Samadiani, Emad, and Yogendra Joshi. "Multi-parameter model reduction in multi-scale convective systems." International Journal of Heat and Mass Transfer 53, no. 9-10 (April 2010): 2193–205. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2009.12.013.

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3

H., DR SHAHEEN. "A Pervasive Multi-Distribution Perceptron and Hidden Markov Model for Context Aware Systems." Journal of Research on the Lepidoptera 51, no. 2 (June 25, 2020): 818–33. http://dx.doi.org/10.36872/lepi/v51i2/301136.

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4

Lupu, C., C. Dimon, and P. Borne. "MULTI-MODEL ADAPTIVE CONTROL FOR NONLINEAR SYSTEMS." IFAC Proceedings Volumes 40, no. 9 (2007): 108–12. http://dx.doi.org/10.3182/20070723-3-pl-2917.00017.

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5

Benaboud, Rohallah, and Toufik Marir. "Flexibility measurement model of multi-agent systems." Multiagent and Grid Systems 16, no. 3 (October 30, 2020): 309–41. http://dx.doi.org/10.3233/mgs-200334.

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Flexibility is considered as one of the key objectives of agent-based technology. Despite this, we still lack a fundamental understanding of just what “flexibility in multi-agent system (MAS)” really is. Two main questions must be asked. First, how do agents and MAS achieve a high degree of flexibility? Second, what makes one agent or one MAS more flexible than others agents or others MASs? This paper addresses the answer to these two questions by proposing an ontology of the flexibility property and a mathematical measurement model for this property. The proposed ontology gives a comprehensive view of the flexibility by decomposing it on several characteristics and presents several techniques for implementing each characteristic. In addition, it relates these characteristics to MAS components. The proposed model presents a set of metrics for measuring the different characteristics of the flexibility property. The proposed metrics have been applied to JADE applications using a tool developed for this purpose.
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6

Maia, Rosiery S., and Luiz M. G. Gonçalves. "Intellectual Development Model for Multi-Robot Systems." Journal of Intelligent & Robotic Systems 80, S1 (March 31, 2015): 165–87. http://dx.doi.org/10.1007/s10846-015-0224-0.

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7

Zhou, Conghua, Bo Sun, and Zhifeng Liu. "Abstraction for model checking multi-agent systems." Frontiers of Computer Science in China 5, no. 1 (December 1, 2010): 14–25. http://dx.doi.org/10.1007/s11704-010-0358-y.

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8

Tsai, Jia-Lin, Hsin-Haou Huang, and C. T. Sun. "Multi-displacement continuum model for discrete systems." International Journal of Mechanical Sciences 52, no. 12 (December 2010): 1767–71. http://dx.doi.org/10.1016/j.ijmecsci.2010.09.010.

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9

Li, Jiyun, Chenxi Jia, and Chen Qian. "Progressive Breast Cancer Diagnosis Model Based on Multi-classifier and Multi-modal Fusion." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 387–92. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1066.

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10

Gachet, Alexandre, and Patrick Brézillon. "A Multi-Level Model." Journal of Decision Systems 14, no. 1-2 (January 2005): 9–37. http://dx.doi.org/10.3166/jds.14.9-37.

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11

Du, Jingjing, Lei Zhang, Junfeng Chen, Jian Li, and Changping Zhu. "Multi-model predictive control of Hammerstein-Wiener systems based on balanced multi-model partition." Mathematical and Computer Modelling of Dynamical Systems 25, no. 4 (June 3, 2019): 333–53. http://dx.doi.org/10.1080/13873954.2019.1624580.

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12

Lupu, C., C. Petrescu, and C. Dimon. "MULTI-MODEL AND INVERSE MODEL CONTROL METHODS FOR NONLINEAR SYSTEMS." IFAC Proceedings Volumes 40, no. 18 (September 2007): 253–58. http://dx.doi.org/10.3182/20070927-4-ro-3905.00043.

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13

Boschetti, Giovanni, Matteo Bottin, Maurizio Faccio, and Riccardo Minto. "Multi-robot multi-operator collaborative assembly systems: a performance evaluation model." Journal of Intelligent Manufacturing 32, no. 5 (January 8, 2021): 1455–70. http://dx.doi.org/10.1007/s10845-020-01714-7.

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AbstractIn the last decade, collaborative assembly systems (CAS) are becoming increasingly common due to their ability to merge the flexibility of a manual assembly system with the performance of traditional robotics. Technical constraints, e.g., dedicated tools or resources, or performance requirements, e.g., throughput, could encourage the use of a CAS built around a multi-robot and multi-operator layout, i.e., with a number of resources greater than 2. Starting from the development of a prototype multi-robot multi-operator collaborative workcell, a simulation environment was developed to evaluate the makespan and the degree of collaboration in multi-robot multi-operator CAS. From the simulation environment, a mathematical model was conceptualized. The presented model allows estimating, with a certain degree of accuracy, the performances of the system. The results have investigated how several process characteristics, i.e. the number and type of resources, the resources layout, the task allocation method, and the number of feeding devices, influence the degree of collaboration between the resources. Lastly, the authors propose a compact analytic formulation, based on an exponential function, and define the methods and the influence factors to determine its parameters.
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14

Bellalta, Boris, Vanesa Daza, and Miquel Oliver. "An Approximate Queueing Model for Multi-Rate Multi-User MIMO Systems." IEEE Communications Letters 15, no. 4 (April 2011): 392–94. http://dx.doi.org/10.1109/lcomm.2011.020311.102434.

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15

Zhang, Langwen, and Jingcheng Wang. "A Novel Multi-Step Model Predictive Control for Multi-Input Systems." Asian Journal of Control 17, no. 2 (May 8, 2014): 707–15. http://dx.doi.org/10.1002/asjc.901.

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16

De Valois, Russell L., and Karen K. De Valois. "A multi-stage color model." Vision Research 33, no. 8 (May 1993): 1053–65. http://dx.doi.org/10.1016/0042-6989(93)90240-w.

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17

Kalech, Meir, and Avraham Natan. "Model-Based Diagnosis of Multi-Agent Systems: A Survey." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12334–41. http://dx.doi.org/10.1609/aaai.v36i11.21498.

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As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well-known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system's output contradicts the model's expected output. The model is then used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. non-temporal. Temporal diagnosis is used to diagnose the MAS's temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.
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18

Zhao, Cong, and Jingshan Li. "A Bernoulli Model of Multi-Product Manufacturing Systems." IFAC Proceedings Volumes 46, no. 9 (2013): 1268–73. http://dx.doi.org/10.3182/20130619-3-ru-3018.00235.

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19

MIHAI, Gianina. "Multi-Model Database Systems: The State of Affairs." Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics 26, no. 2 (August 31, 2020): 211–15. http://dx.doi.org/10.35219/eai15840409128.

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20

Caruso, Fabio, Giacomo Mantriota, Luciano Afferrante, and Giulio Reina. "A theoretical model for multi-layer jamming systems." Mechanism and Machine Theory 172 (June 2022): 104788. http://dx.doi.org/10.1016/j.mechmachtheory.2022.104788.

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21

Merelli, Emanuela, Nicola Paoletti, and Luca Tesei. "A multi-level model for self-adaptive systems." Electronic Proceedings in Theoretical Computer Science 91 (August 15, 2012): 112–26. http://dx.doi.org/10.4204/eptcs.91.8.

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22

Athamena, Belkacem, and Zina Houhamdi. "An Exception Management Model in Multi-Agents Systems." Journal of Computer Science 13, no. 5 (May 1, 2017): 140–52. http://dx.doi.org/10.3844/jcssp.2017.140.152.

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23

Viksnin, Ilya, Sergey Chuprov, Maria Usova, and Danil Zakoldaev. "Police office model for multi-agent robotic systems." IOP Conference Series: Materials Science and Engineering 497 (April 2, 2019): 012036. http://dx.doi.org/10.1088/1757-899x/497/1/012036.

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24

Campbell, S. L., K. Drake, R. Nikoukhah, and F. Delebecque. "Rapid multi-model identification in systems with delays." IFAC Proceedings Volumes 34, no. 23 (December 2001): 261–66. http://dx.doi.org/10.1016/s1474-6670(17)32901-4.

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25

Ren, X., S. M. Hargrave, H. A. Thompson, and P. J. Fleming. "Multi-Agent Systems for Model-based Fault Diagnosis." IFAC Proceedings Volumes 34, no. 22 (November 2001): 83–88. http://dx.doi.org/10.1016/s1474-6670(17)32917-8.

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26

Alptekinoğlu, Aydın, and Christopher S. Tang. "A model for analyzing multi-channel distribution systems." European Journal of Operational Research 163, no. 3 (June 2005): 802–24. http://dx.doi.org/10.1016/j.ejor.2003.11.005.

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27

Zeferino, João A., António P. Antunes, and Maria C. Cunha. "Multi-objective model for regional wastewater systems planning." Civil Engineering and Environmental Systems 27, no. 2 (June 2010): 95–106. http://dx.doi.org/10.1080/09540250802658988.

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28

Gharbi, Atef. "Five Capabilities Model Applied to Multi-Robot Systems." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 1 (January 2015): 57–88. http://dx.doi.org/10.4018/ijapuc.2015010105.

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This paper deals with distributed Robotic Agents constituting several intelligent agents. Each one has to interact with the other autonomous robots. The problem faced is how to ensure a distributed planning through the cooperation of the distributed robotic agents. To do so, the author proposes to use the concept of five capabilities model which is based on Environment, Self, Planner, Competence, and Communication. A Benchmark Production System is used as a running example to explain the author's contribution.
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29

Ramos, G., A. F. Leal, M. A. Ríos, and L. F. Roa. "Grounding Model in Multi-Train DC Traction Systems." IEEE Latin America Transactions 12, no. 2 (March 2014): 169–75. http://dx.doi.org/10.1109/tla.2014.6749534.

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30

Cheng, Zhijun, Zheng Yang, and Bo Guo. "Optimal opportunistic maintenance model of multi-unit systems." Journal of Systems Engineering and Electronics 24, no. 5 (October 2013): 811–17. http://dx.doi.org/10.1109/jsee.2013.00094.

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31

Marir, Toufik, Farid Mokhati, Hassina Bouchlaghem Seridi, Youghourta Acid, and Maroua Bouzid. "QM4MAS: a quality model for multi-agent systems." International Journal of Computer Applications in Technology 54, no. 4 (2016): 297. http://dx.doi.org/10.1504/ijcat.2016.080485.

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32

Bouzid, Maroua, Youghourta Acid, Hassina Bouchlaghem Seridi, Toufik Marir, and Farid Mokhati. "QM4MAS: a quality model for multi-agent systems." International Journal of Computer Applications in Technology 54, no. 4 (2016): 297. http://dx.doi.org/10.1504/ijcat.2016.10001315.

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33

Lee, Yeonjeong, and Kyoung-Jae Kim. "Product Recommender Systems using Multi-Model Ensemble Techniques." Journal of Intelligence and Information Systems 19, no. 2 (June 30, 2013): 39–54. http://dx.doi.org/10.13088/jiis.2013.19.2.039.

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34

Bollig, Benedikt, Dietrich Kuske, and Roy Mennicke. "The Complexity of Model Checking Multi-Stack Systems." Theory of Computing Systems 60, no. 4 (August 26, 2016): 695–736. http://dx.doi.org/10.1007/s00224-016-9700-6.

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35

Yalcin, Gizem Damla, Nurseda Danisik, Rana Can Baygin, and Ahmet Acar. "Systems Biology and Experimental Model Systems of Cancer." Journal of Personalized Medicine 10, no. 4 (October 19, 2020): 180. http://dx.doi.org/10.3390/jpm10040180.

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Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.
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36

Dauphin-Tanguy, G., O. Moreigne, and P. Borne. "Optimal Control of Multi-time-scale Systems Through a Multi-model Representation." IFAC Proceedings Volumes 18, no. 11 (September 1985): 473–78. http://dx.doi.org/10.1016/s1474-6670(17)60170-8.

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37

Kissoum, Yacine, and Mohammed Redjimi. "Multi-Level Testing Approach for Multi-Agent Systems." International Journal of Organizational and Collective Intelligence 12, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijoci.304883.

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The software development cycles need product testing. There is a crucial lack in testing phases of multi-agent systems. To this end, a call for an investigation of appropriate testing techniques is necessary to provide adequate software development processes and supporting tools. Among all existing solutions of the test, the model-based testing technique –MBT) has gained attention with the popularization of models both in software design and development. This technique uses a so-called abstract test model to generate abstract test cases. After their concretization, concrete test cases are submitted to the system under test. The systems outputs are finally compared to the abstract test model expected results. In this context, a model-based testing approach for multi-agent systems based on the Reference net paradigm is proposed in this paper. A running example supported by a multi-agent testing prototype, which aims at simplifying and providing a uniform and automated way for multi-agent systems testing is presented and discussed.
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38

Di Palma, F., and L. Magni. "A multi-model structure for model predictive control." Annual Reviews in Control 28, no. 1 (January 2004): 47–52. http://dx.doi.org/10.1016/j.arcontrol.2004.01.004.

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39

Bottecchia, Luigi, Pietro Lubello, Pietro Zambelli, Carlo Carcasci, and Lukas Kranzl. "The Potential of Simulating Energy Systems: The Multi Energy Systems Simulator Model." Energies 14, no. 18 (September 11, 2021): 5724. http://dx.doi.org/10.3390/en14185724.

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Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open-source energy system models, it emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system used to reproduce and understand its behaviour under given conditions without seeking an optimal solution. In this paper, a new open-source energy system model is presented. Multi Energy Systems Simulator (MESS) is a modular, multi-energy carrier, multi-node model that allows the investigation of non optimal solutions by simulating an energy system. The model was built for urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be represented as well. In this work, the tool’s features are presented through a comparison between MESS and Calliope, a state of the art optimization model, to analyse and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development. The two models produced coherent results, showing differences that were tracked down to the different approaches. Based on the comparison conducted, general conclusions were drawn on the potential of simulating energy systems in terms of a more realistic description of smaller energy systems, lower computational times and increased opportunity for participatory processes in planning urban energy systems.
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40

Illahi, Ubaid. "Transport Sustainability Performance Evaluation using a Multi-stage Multi-tool Hybrid Model." European Transport/Trasporti Europei 81, ET.2021 (March 2021): 1–15. http://dx.doi.org/10.48295/et.2021.81.6.

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This article proposes a multi-stage and multi-tool hybrid model (MMHM) for evaluating the performance of transportation systems. This model is based on pillars, classes, and indicators. The novelty of MMHM is that the indicators were weighted at three different levels using Principal Component Analysis, Fuzzy Logic, and method of equal weights. The output of the MMHM is an index called Sustainable Mobility Index (SMI). SMI gives the relative performance of transportation systems. MMHM was applied to four metropolitan cities of India. A total of 116 indicators were developed that were divided into ten classes, each corresponding to three sustainability pillars. The results demonstrated that MMHM is useful in ranking the study areas. It also demonstrated that it could be a valuable tool to recognize, track and evaluate the sustainability performance of transportation systems which would be beneficial to transportation evaluators, planners, decision-makers, and policymakers.
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41

Viaña, Raquel, Paola Magillo, Enrico Puppo, and Pedro A. Ramos. "Multi-VMap: A Multi-Scale Model for Vector Maps." GeoInformatica 10, no. 3 (September 2006): 359–94. http://dx.doi.org/10.1007/s10707-006-9832-y.

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42

Anandalingam, G. "A Mathematical Programming Model of Decentralized Multi-Level Systems." Journal of the Operational Research Society 39, no. 11 (November 1988): 1021. http://dx.doi.org/10.2307/2583201.

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43

Youness, Hassan Ali Hassan Ahmed. "DESIGNING AN ARCHITECTURE LEVEL MODEL FOR MULTI-CORE SYSTEMS." JES. Journal of Engineering Sciences 42, no. 6 (November 1, 2014): 1378–91. http://dx.doi.org/10.21608/jesaun.2014.115115.

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44

Lomuscio, Alessio R. "Advances in symbolic model checking for multi-agent systems." Electronic Proceedings in Theoretical Computer Science 161 (August 24, 2014): 1. http://dx.doi.org/10.4204/eptcs.161.1.

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45

Leuthold, R., C. Crawford, S. Gros, and M. Diehl. "Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems." Journal of Physics: Conference Series 1256 (July 2019): 012009. http://dx.doi.org/10.1088/1742-6596/1256/1/012009.

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46

Moon, Ji Hyun, Jaejun Lee, and Ho Jae Lee. "Fuzzy Formation Controlling Phugoid Model-Based Multi-Agent Systems." Journal of Institute of Control, Robotics and Systems 22, no. 7 (July 1, 2016): 508–12. http://dx.doi.org/10.5302/j.icros.2016.16.0068.

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47

Robnik, Marko, Tomaz Prosen, and Jure Dobnikar. "Multi-component random model of diffusion in chaotic systems." Journal of Physics A: Mathematical and General 32, no. 7 (January 1, 1999): 1147–62. http://dx.doi.org/10.1088/0305-4470/32/7/006.

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48

Talmoudi, Samia, and Moufida Lahmari. "The multi-model approach for fractional-order systems modelling." Transactions of the Institute of Measurement and Control 40, no. 1 (July 13, 2016): 331–40. http://dx.doi.org/10.1177/0142331216655396.

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Currently, fractional-order systems are attracting the attention of many researchers because they present a better representation of many physical systems in several areas, compared with integer-order models. This article contains two main contributions. In the first one, we suggest a new approach to fractional-order systems modelling. This model is represented by an explicit transfer function based on the multi-model approach. In the second contribution, a new method of computation of the validity of library models, according to the frequency [Formula: see text], is exposed. Finally, a global model is obtained by fusion of library models weighted by their respective validities. Illustrative examples are presented to show the advantages and the quality of the proposed strategy.
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49

Jr., Frantisek Zboril, Vladimir Janousek, Radek Koci, Frantisek Zboril, and Zdenek Mazal. "Framework for model-based design of multi-agent systems." International Journal of Autonomic Computing 1, no. 2 (2009): 140. http://dx.doi.org/10.1504/ijac.2009.024746.

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50

Lerman, Kristina. "A Model of Adaptation in Collaborative Multi-Agent Systems." Adaptive Behavior 12, no. 3-4 (December 2004): 187–97. http://dx.doi.org/10.1177/105971230401200305.

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