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Evolutionary algorithms and fuzzy sets for discovering temporal rules

Transactions on Fuzzy Systems 15(4): 616-635. Ale, J.M. and Rossi, G. H. (2000). An approach to discovering temporal association rules, Proceedings of the 2000 ACM Symposium on Applied Computing (SAC’00), Como, Italy, pp. 294-300. Bayardo, Jr., R.J. and Agrawal, R. (1999). Mining the most interesting rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 145-154. Ben Aicha, F., Bouani, F. and Ksouri, M. (2013). A multivariable multiobjective predictive

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Ontology–based access to temporal data with Ontop: A framework proposal

References Abiteboul, S., Hull, R. and Vianu, V. (1995). Foundations of Databases , Addison Wesley Publ. Co., Boston, MA. Allen, J.F. (1983). Maintaining knowledge about temporal intervals, Communications of the ACM 26 (11): 832–843. Alur, R. and Henzinger, T.A. (1993). Real-time logics: Complexity and expressiveness, Information and Computation 104 (1): 35–77. Anicic, D., Fodor, P., Rudolph, S. and Stojanovic, N. (2011). EP-SPARQL: A unified language for event processing and stream reasoning, Proceedings of the 20th International

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Evolving small-board Go players using coevolutionary temporal difference learning with archives

. Kim, K.-J., Choi, H. and Cho, S.-B. (2007). Hybrid of evolution and reinforcement learning for Othello players, IEEE Symposium on Computational Intelligence and Games, CIG 2007, Honolulu, HI, USA , pp. 203-209. Krawiec, K. and Szubert, M. (2010). Coevolutionary temporal difference learning for small-board Go, IEEE Congress on Evolutionary Computation, Barcelona, Spain , pp. 1-8. Lasker, E. (1960). Go and Go-Moku: The Oriental Board Games , Dover Publications, New York, NY. Lubberts, A

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Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

, ACM SIGMOD Record 32 (2): 5-14. Hulten, G., Spencer, L. and Domingos, P. (2001). Mining time-changing data streams, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA , pp. 97-106. Ikonomovska, E., Gama, J. and Dzeroski, S. (2011). Learning model trees from evolving data streams, Data Mining and Knowledge Discovery 23 (1): 128-168. Krizanovic, K., Galic, Z. and Baranovic, M. (2011). Data types and operations for spatio-temporal data streams

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Distributed scheduling of sensor networks for identification of spatio-temporal processes

-843. Patan, M. (2009a). Decentralized mobile sensor routing for parameter estimation of distributed systems, Proceedings of the 1st IFAC Workshop on Estimation and Control of Networked Systems, NecSys 2009, Venice, Italy , pp. 210-215. Patan, M. (2009b). Distributed configuration of sensor networks for parameter estimation in spatio-temporal systems, Proceedings of the European Control Conference, ECC'09, Budapest, Hungary , pp. 4871-4876. Patan, M., Chen, Y. and Tricaud, C. (2008). Resource-constrained sensor routing

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A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamics

A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamics

Our aim is to adapt Fletcher's filter approach to solve optimal control problems for systems described by nonlinear Partial Differential Equations (PDEs) with state constraints. To this end, we propose a number of modifications of the filter approach, which are well suited for our purposes. Then, we discuss possible ways of cooperation between the filter method and a PDE solver, and one of them is selected and tested.

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Distributed scheduling of measurements in a sensor network for parameter estimation of spatio-temporal systems

-137. Patan, M. (2006). Optimal activation policies for continuous scanning observations in parameter estimation of distributed systems, International Journal of Systems Science 37(11): 763-775. Patan, M. (2008). A parallel sensor scheduling technique for fault detection in distributed parameter systems, in E. Luque et al. (Eds.), Parallel Processing-Euro Par 2008, Lecture Notes in Computer Science, Vol. 5168, Springer, Berlin/Heidelberg, pp. 833-843. Patan, M. (2012a). Distributed scheduling of sensor networks for identification of spatio-temporal

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A BGK model for charge transport in graphene

Abstract

The classical Boltzmann equation describes well temporal behaviour of a rarefied perfect gas. Modified kinetic equations have been proposed for studying the dynamics of different type of gases. An important example is the transport equation, which describes the charged particles flow, in the semi-classical regime, in electronic devices. In order to reduce the difficulties in solving the Boltzmann equation, simple expressions of a collision operator have been proposed to replace the standard Boltzmann integral term. These new equations are called kinetic models. The most popular and widely used kinetic model is the Bhatnagar-Gross-Krook (BGK) model. In this work we propose and analyse a BGK model for charge transport in graphene.

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A system for deduction-based formal verification of workflow-oriented software models

-332. Dijkman, R.M., Dumas, M. and Ouyang, C. (2008). Semantics and analysis of business process models in BPMN, Information and Software Technology 50(12): 1281-1294. Dijkstra, E.W. (1972). Structured Programming, Academic Press, London, pp. 1-82. Duan, Y. and Ma, H. (2005). Modeling flexible workflow based on temporal logic, in W. Shen, A.E. James, K.-M. Chao, M. Younas, Z. Lin and J.-P.A. Barth`es (Eds.), Proceedings of the 9th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2005, 24-26 May

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The performance profile: A multi–criteria performance evaluation method for test–based problems

information using genetic programming, Genetic Programming and Evolvable Machines 9 (4): 281–294. Jaśkowski, W., Liskowski, P., Szubert, M. and Krawiec, K. (2013). Improving coevolution by random sampling, in C. Blum (Ed.), GECCO’13: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation , ACM, Amsterdam, pp. 1141–1148. Jaśkowski, W., Szubert, M. and Liskowski, P. (2014). Multi-criteria comparison of coevolution and temporal difference learning on Othello, in A.I. Esparcia-Alcazar and A.M. Mora (Eds.), EvoApplications 2014

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