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1. Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control

Author

Mohand-Said Mezmaz, Nouredine Melab, Romain Ragonnet, Guillaume Briffoteaux, Daniel Tuyttens, Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Mons [Belgium] (UMONS), Monash University [Melbourne], Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), and Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).

Subjects
Artificial Neural Network, Computer Networks and Communications, Computer science, Monte Carlo method, Context (language use), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Simulation-based optimization, Surrogate-assisted Optimization, 0202 electrical engineering, electronic engineering, information engineering, Massively parallel, Dropout (neural networks), Artificial neural network, Evolution Control, business.industry, Deep learning, 020206 networking & telecommunications, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Supercomputer, Computer engineering, Hardware and Architecture, 020201 artificial intelligence & image processing, Artificial intelligence, Massively Parallel Computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Software, Simulation
Abstract

International audience; In many optimal design searches, the function to optimise is a simulator that is computationally expensive. While current High Performance Computing (HPC) methods are not able to solve such problems efficiently, parallelism can be coupled with approximate models (surrogates or meta-models) that imitate the simulator in timely fashion to achieve better results. This combined approach reduces the number of simulations thanks to surrogate use whereas the remaining evaluations are handled by supercomputers. While the surrogates' ability to limit computational times is very attractive, integrating them into the over-arching optimization process can be challenging. Indeed, it is critical to address the major trade-off between the quality (precision) and the efficiency (execution time) of the resolution. In this article, we investigate Evolution Controls (ECs) which are strategies that define the alternation between the simulator and the surrogate within the optimization process. We propose a new EC based on the prediction uncertainty obtained from Monte Carlo Dropout (MCDropout), a technique originally dedicated to quantifying uncertainty in deep learning. Investigations of such uncertainty-aware ECs remain uncommon in surrogate-assisted evolutionary optimization. In addition, we use parallel computing in a complementary way to address the high computational burden. Our new strategy is implemented in the context of a pioneering application to Tuberculosis Transmission Control. The reported results show that the MCDropout-based EC coupled with massively parallel computing outperforms strategies previously proposed in the field of surrogate-assisted optimization.

Published
2020

2. Parallel Surrogate-assisted Optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO

Author

Guillaume Briffoteaux, Romain Ragonnet, Jan Gmys, Nouredine Melab, Mohand Mezmaz, Maxime Gobert, Daniel Tuyttens, Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Mons [Belgium] (UMONS), Monash University [Melbourne], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques [Mons], Université de Mons (UMons), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), and Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).

Subjects
General Computer Science, Computer science, General Mathematics, Evolutionary algorithm, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, Surrogate model, Genetic algorithm, Surrogate-assisted Optimization, 0202 electrical engineering, electronic engineering, information engineering, Massively parallel, Global optimization, Artificial neural network, Bayesian Optimization, business.industry, 05 social sciences, Bayesian optimization, Evolutionary Algorithm, 050301 education, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Efficient Global Optimization, Benchmark (computing), 020201 artificial intelligence & image processing, Artificial intelligence, Massively Parallel Computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, 0503 education, computer, Simulation
Abstract

International audience; Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger databases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

Published
2020

3. A Spectral Approach to Uncertainty Quantification in Water Distribution Networks

Author

Olivier Piller, Iraj Mortazavi, M. Braun, Angelo Iollo, Environnement, territoires et infrastructures (UR ETBX), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modeling Enablers for Multi-PHysics and InteractionS (MEMPHIS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre d'enseignement Cnam Paris (CNAM Paris), Conservatoire National des Arts et Métiers [CNAM] (CNAM), The work presented in the paper is part of the French-German collaborative research project ResiWater that is funded by the French National Research Agency (ANR, project: ANR-14-PICS-0003) and the German Federal Ministry of Education and Research (BMBF, project: BMBF-13N13690), ANR-14-PICS-0003,ResiWater,Outils, modèles et réseaux sécurisés et innovants de capteurs pour une résilience augmentée des infrastructures liées à l'eau(2014), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)

Subjects
Spectral approach, 010504 meteorology & atmospheric sciences, Distribution networks, Hydraulics, Computer science, Geography, Planning and Development, 0207 environmental engineering, 02 engineering and technology, Management, Monitoring, Policy and Law, 01 natural sciences, law.invention, modelling, [SPI]Engineering Sciences [physics], INCERTITUDE, Computer Science::Systems and Control, law, Mathematics::Metric Geometry, Statistical physics, RESEAU DE DISTRIBUTION D'EAU, Uncertainty quantification, [MATH]Mathematics [math], 020701 environmental engineering, water distribution system, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, statistical uncertainty, Réseau de distribution d'eau, MODELISATION, 6. Clean water, Modélisation, SIMULATION, [SDE]Environmental Sciences
Abstract

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI [ADD1_IRSTEA]Gestion intégrée de la ressource et des infrastructures; International audience; To date, the hydraulics of water distribution networks are calculated using deterministic models. Because many of the parameters in these models are not known exactly, it is important to evaluate the effects of their uncertainties on the results through uncertainty analysis. For the propagation of uncertain parameters, this article for the first time applies the polynomial chaos expansion to a hydraulic model and compares the results with those from classical approaches like the first-order second-moment method and Monte Carlo simulations. Results presented in this article show that the accuracy of the polynomial chaos expansion is on the same level as the Monte Carlo simulation. Further, it is concluded that due to its computational efficiency, polynomial chaos expansion is superior to the Monte Carlo simulation.

Published
2020

4. A Spectral Approach to Uncertainty Quantification in Water Distribution Networks

Author

Braun, M., Piller, O., Iollo, A., Mortazavi, I., Environnement, territoires et infrastructures (UR ETBX), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modeling Enablers for Multi-PHysics and InteractionS (MEMPHIS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre d'enseignement Cnam Paris (CNAM Paris), Conservatoire National des Arts et Métiers [CNAM] (CNAM), The work presented in the paper is part of the French-German collaborative research project ResiWater that is funded by the French National Research Agency (ANR, project: ANR-14-PICS-0003) and the German Federal Ministry of Education and Research (BMBF, project: BMBF-13N13690), and ANR-14-PICS-0003,ResiWater,Outils, modèles et réseaux sécurisés et innovants de capteurs pour une résilience augmentée des infrastructures liées à l'eau(2014)

Subjects
[SPI]Engineering Sciences [physics], Water distribution system, Modélisation, Statistical uncertainty, [SDE]Environmental Sciences, Incertitude, RESEAU DE DISTRIBUTION D'EAU, Réseau de distribution d'eau, [MATH]Mathematics [math], MODELISATION, Simulation, Modelling
Abstract

International audience; To date, the hydraulics of water distribution networks are calculated using deterministic models. Because many of the parameters in these models are not known exactly, it is important to evaluate the effects of their uncertainties on the results through uncertainty analysis. For the propagation of uncertain parameters, this article for the first time applies the polynomial chaos expansion to a hydraulic model and compares the results with those from classical approaches like the first-order second-moment method and Monte Carlo simulations. Results presented in this article show that the accuracy of the polynomial chaos expansion is on the same level as the Monte Carlo simulation. Further, it is concluded that due to its computational efficiency, polynomial chaos expansion is superior to the Monte Carlo simulation.

Published
2020

5. EvaSylv: A user-friendly software to evaluate forestry scenarii including natural risk

Author

Loisel, Patrice, Duvillié, Guillerme, barbeau, denis, Charnomordic, Brigitte, Loisel, patrice, Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Graphes et Optimisation Mathématique [Bruxelles] (GOM), Université Libre de Bruxelles [Bruxelles] (ULB), financial support from the Economic, Human and Social Science Researchers Network of Ecofor, (grant number ECOFOR 2012-24). We thank our funders as well as those who provided assistance with writing this paper., Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université libre de Bruxelles (ULB)

Subjects
FOS: Computer and information sciences, Faustmann, stand level, forest management, [QFIN.RM]Quantitative Finance [q-fin]/Risk Management [q-fin.RM], natural risk, Statistics - Applications, FOS: Economics and business, [QFIN.RM] Quantitative Finance [q-fin]/Risk Management [q-fin.RM], Applications (stat.AP), Quantitative Biology - Populations and Evolution, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q1 - Agriculture, Populations and Evolution (q-bio.PE), JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D81 - Criteria for Decision-Making under Risk and Uncertainty, Decision Support System, simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDE.ES]Environmental Sciences/Environmental and Society, storm risk, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q2 - Renewable Resources and Conservation/Q.Q2.Q23 - Forestry, Risk Management (q-fin.RM), FOS: Biological sciences, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.ES] Environmental Sciences/Environmental and Society, optimization, Quantitative Finance - Risk Management
Abstract

Forest management relies on the evaluation of silviculture practices. The increase in natural risk due to climate change makes it necessary to consider evaluation criteria that take natural risk into account. Risk integration in existing software requires advanced programming skills.We propose a user-friendly software to simulate even-aged and monospecific forest at the stand level, in order to evaluate and optimize forest management. The software gives the possibility to run management scenarii with or without considering the impact of natural risk. The control variables are the dates and rates of thinning and the cutting age.The risk model is based on a Poisson processus. The Faustmann approach, including tree damage risk, is used to evaluate future benefits, economic or ecosystem services. It relies on the calculation of expected values, for which a dedicated mathematical development has been done. The optimized criteria used to evaluate the various scenarii are the Faustmann value and the Averaged yield value.We illustrate the approach and the software on two case studies: economic optimization of a beech stand and carbon sequestration optimization of a pine stand.Software interface makes it easy for users to write their own (growth-tree damage-economic) models without advanced programming skills. The possibility to run management scenarii with/without considering the impact of natural risk may contribute improving silviculture guidelines and adapting them to climate change. We propose future lines of research and improvement.

Published
2019

6. Linear Search by a Pair of Distinct-Speed Robots

Author

Tomasz Kociumaka, Jurek Czyzowicz, David Ilcinkas, Leszek Gąsieniec, Ralf Klasing, Dominik Pająk, Evangelos Bampas, School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Département d'Informatique et d'Ingénierie (DII), Université du Québec en Outaouais (UQO), Department of Computer Science [Liverpool], University of Liverpool, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), University of Warsaw (UW), Institute of informatics [Wrocław], See paper for details., ANR-11-BS02-0014,DISPLEXITY,Calculabilité et complexité en distribué(2011), ANR-13-JS02-0002,MACARON,Bouger et Calculer: Agents, Robots et Réseaux(2013), ANR-16-CE40-0023,DESCARTES,Abstraction modulaire pour le calcul distribué(2016), ANR-10-IDEX-0003,IDEX BORDEAUX,Initiative d'excellence de l'Université de Bordeaux(2010), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Pajak, Dominik S, Laboratoire d'informatique Fondamentale de Marseille (LIF), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Institute of Informatics [Warsaw], Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), University of Warsaw (UW)-University of Warsaw (UW), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Engineering, General Computer Science, Computer science, different speeds, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 0211 other engineering and technologies, 0102 computer and information sciences, 02 engineering and technology, Bang-bang robot, linear search, 01 natural sciences, Article, Computer Science::Robotics, Position (vector), mobile robots, 0202 electrical engineering, electronic engineering, information engineering, Cartesian coordinate robot, Point (geometry), Computer vision, [INFO]Computer Science [cs], Online algorithm, Simulation, Linear search, 021103 operations research, business.industry, Applied Mathematics, Mobile robot, Computer Science Applications, 010201 computation theory & mathematics, group search, Moment (physics), Line (geometry), Robot, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business
Abstract

Two mobile robots are initially placed at the same point on an infinite line. Each robot may move on the line in either direction not exceeding its maximal speed. The robots need to find a stationary target placed at an unknown location on the line. The search is completed when both robots arrive at the target point. The target is discovered at the moment when either robot arrives at its position. The robot knowing the placement of the target may communicate it to the other robot. We look for the algorithm with the shortest possible search time (i.e. the worst-case time at which both robots meet at the target) measured as a function of the target distance from the origin (i.e. the time required to travel directly from the starting point to the target at unit velocity). We consider two standard models of communication between the robots, namely wireless communication and communication by meeting. In the case of communication by meeting, a robot learns about the target while sharing the same location with a robot possessing this knowledge. We propose here an optimal search strategy for two robots including the respective lower bound argument, for the full spectrum of their maximal speeds. This extends the main result of Chrobak et al. (in: Italiano, Margaria-Steffen, Pokorný, Quisquater, Wattenhofer (eds) Current trends in theory and practice of computer science, SOFSEM, 2015) referring to the exact complexity of the problem for the case when the speed of the slower robot is at least one third of the faster one. In the wireless communication model, a message sent by one robot is instantly received by the other robot, regardless of their current positions on the line. For this model, we design a strategy which is optimal whenever the faster robot is at most \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{17}+4\approx 8.123$$\end{document}17+4≈8.123 times faster than the slower one. We also prove that otherwise the wireless communication offers no advantage over communication by meeting.

Published
2019

7. On the Performance of Sliding Window TD-LMMSE Channel Estimation for 5G Waveforms in High Mobility Scenario

Author

Marion Berbineau, Iyad Dayoub, Michel Saideh, Département Composants et Systèmes (IFSTTAR/COSYS), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France, Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), This work was supported in part by the SMARTIES project within theELSAT2020 program supported by FEDER, the French State, and the Haut deFrance Region and in part by the X2RAIL-1 project from H2020 SHIF2RAILprogram. The review of this paper was coordinated by Dr. N.-D. Dao., PRES Université Lille Nord de France-PRES Université Nantes Angers Le Mans (UNAM)-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
CHANNEL ESTIMATION, Computer Networks and Communications, Orthogonal frequency-division multiplexing, Computer science, Monte Carlo method, Aerospace Engineering, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], TRAITEMENT DU SIGNAL, TECHNOLOGIE SANS FIL, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], TELECOMMUNICATION, LMMSE, Sliding window protocol, 0202 electrical engineering, electronic engineering, information engineering, Waveform, [INFO]Computer Science [cs], Time domain, Electrical and Electronic Engineering, ONDE, Computer Science::Information Theory, 020208 electrical & electronic engineering, 020206 networking & telecommunications, FBMC, Filter bank, Computer Science::Other, [SPI.TRON]Engineering Sciences [physics]/Electronics, TRANSPORT FERROVIAIRE, Modulation, Automotive Engineering, SIMULATION, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 5G, Communication channel
Abstract

The upcoming 5G era is considered as a critical answer to future railway communication needs. In this correspondence, we analyze the performance of sliding window time domain (TD) LMMSE channel estimation technique where a generalized development is proposed and analyzed for 5G filter bank multicarrier-offset quadratic-amplitude modulation waveform candidates. In order to enhance performance, the analytical model is extended to exploit multipaths and multiantennas correlation wherever they do exist e.g., tunnels. TD-LMMSE performance for 5G waveforms and achieved enhancements are verified by Monte Carlo simulations.

Published
2018

8. Incorporating the Human Factor within Manufacturing Dynamics

Author

F. Fruggiero, Stefano Riemma, Yassine Ouazene, V. Guglielmi, Roberto Macchiaroli, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), IFAC PAPERS ON LINE, Fruggiero, F., Riemma, S., Ouazene, Y., Macchiaroli, Roberto, and Guglielmi, V.

Subjects
0209 industrial biotechnology, Engineering, Human Factors, Multi Agent Systems, Multi Agent System, Chaotic, 02 engineering and technology, 020901 industrial engineering & automation, Recovery rate, Schema (psychology), 0502 economics and business, Tactical decision making, Fatigue, Simulation, ComputingMilieux_MISCELLANEOUS, Productivity, Random assignment, business.industry, Multi-agent system, 05 social sciences, Human Factor, Dynamics Environment, Control and Systems Engineering, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Industrial engineering, System dynamics, Market behavior, business, 050203 business & management
Abstract

This paper frames a model which summarizes the principal factors and relationships to incorporate human element, as per fatigue engagement, in the strategic and tactical decision making. A characterization of humans as per a dynamic interferer in system is presented. The findings are tested in an U-shaped assembly division of an international motor company. Dual-Resource Constrained sets are proposed. A Multi Agent model architecture, incorporating system dynamics modelling per humans, is implemented. ANOVA is executed for interaction analyses. Results designed optimal job sequence, rest break and task’s sequence, recovery rate and load characters as per enhanced system’s performances. Stable fatigued conditions are under complete recovery. Production rate in system is affected by fatigue that in turn increases with rise in switching rate. Chaotic market behavior prefers decentralized assignment of worker to task. Continuous switching suffers from learning attitude. Chaotic demand impairs fatigue across workforce reporting instable operator fatigue under different allocation rules. Under loudly physical tasks, random assignment is preferable for productivity outcomes. Autonomous control of workers at work considerably impairs fatigue under complete resting schema.

Published
2016

9. A benchmark of kriging-based infill criteria for noisy optimization

Author

Tobias Wagner, David Ginsbourger, Victor Picheny, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Technische Universität Dortmund [Dortmund] (TU), University of Bern, Deutsche Forschungsgemeinschaft (DFG) [SFB/TR TRR 30] Funding Text : The contributions of Tobias Wagner to this paper are based on investigations of the project D5 of the Collaborative Research Center SFB/TR TRR 30, which is kindly supported by the Deutsche Forschungsgemeinschaft (DFG)., and Picheny, Victor

Subjects
Mathematical optimization, Control and Optimization, Covariance function, 0211 other engineering and technologies, 02 engineering and technology, Metamodeling, symbols.namesake, 510 Mathematics, DESIGN, Kriging, Homoscedasticity, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [MATH]Mathematics [math], Gaussian process, GLOBAL OPTIMIZATION, Mathematics, EGO, 021103 operations research, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Covariance, Computer Graphics and Computer-Aided Design, Computer Science Applications, COMPUTER EXPERIMENTS, Control and Systems Engineering, Gaussian noise, SIMULATION, symbols, 020201 artificial intelligence & image processing, Engineering design process, Noise, Software
Abstract

International audience; Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

Published
2013