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Books on the topic 'Drive simulation model'

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Published: 28 June 2021
Last updated: 1 February 2022

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1

Davis, William D. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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2

Davis, William D. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Davis, William D. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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4

Davis, William D. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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5

Hatzivasilis, George, and Sotiris Ioannidis, eds. Model-driven Simulation and Training Environments for Cybersecurity. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62433-0.

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6

Bjornar, Solhaug, and Stølen Ketil, eds. Model-driven risk analysis: The CORAS approach. Berlin: Springer, 2010.

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7

Krzysztof, Czarnecki, ed. Model driven engineering languages and systems: 11th international conference, MoDELS 2008, Toulouse, France, September 28 - October 3, 2008 : proceedings. [New York]: Springer, 2008.

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8

MoDELS 2008 (2008 Toulouse, France). Model driven engineering languages and systems: 11th international conference, MoDELS 2008, Toulouse, France, September 28 - October 3, 2008 : proceedings. [New York]: Springer, 2008.

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9

MoDELS 2008 (2008 Toulouse, France). Model driven engineering languages and systems: 11th international conference, MoDELS 2008, Toulouse, France, September 28 - October 3, 2008 : proceedings. [New York]: Springer, 2008.

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10

Katz, Shmuel. Transactions on Aspect-Oriented Software Development VI: Special Issue on Aspects and Model-Driven Engineering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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11

Jürgen, Kazmeier, Breu Ruth, Atkinson Colin, and SpringerLink (Online service), eds. Model Driven Engineering Languages and Systems: 15th International Conference, MODELS 2012, Innsbruck, Austria, September 30–October 5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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12

Atif, Iqbal, and Guzinski Jaroslaw, eds. High performance control of AC drives with MATLAB/Simulink models. Chichester, West Sussex: Wiley, 2012.

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13

David, Hutchison. Model-Driven Development of Reliable Automotive Services: Second Automotive Software Workshop, ASWSD 2006, San Diego, CA, USA, March 15-17, 2006, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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14

Xia, Chang-liang. Permanent magnet brushless DC motor drives and controls. Hoboken, N.J: Wiley, 2012.

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15

Kerckhoffs, Roy C. P. Patient specific modeling of the cardiovascular system: Technology-driven personalized medicine. New York: Springer, 2010.

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16

Mohan, Ned. Advanced electric drives: Analysis, control, and modeling using MATLAB/Simulink. Hoboken, New Jersey: Wiley, 2014.

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17

Topçu, Okan. Distributed Simulation: A Model Driven Engineering Approach. Springer, 2018.

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18

A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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19

P, Forney Glenn, and National Institute of Standards and Technology (U.S.), eds. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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20

P, Forney Glenn, and National Institute of Standards and Technology (U.S.), eds. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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21

Koziel, Slawomir, and Leifur Leifsson. Simulation-Driven Aerodynamic Design Using Variable-Fidelity Models. Imperial College Press, 2015.

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22

Lund, Mass Soldal, Bjørnar Solhaug, and Ketil Stølen. Model-Driven Risk Analysis: The CORAS Approach. Springer, 2010.

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23

Measurement Driven Simulation of Complex Engineering Systems. Amsterdam University Press, 2007.

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24

E, Paxson Daniel, and Lewis Research Center, eds. A simplified model for the investigation of acoustically driven combustion instabilities. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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25

Electric machines and drives : principles, control, modeling, and simulation. CRC Press, 2013.

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26

Morris, Alan R. Musculoskeletal modelling and EMG driven simulation of the human lower body. 2006.

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27

Laver, Michael, and Ernest Sergenti. The Evolutionary Dynamics of Decision Rule Selection. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0008.

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This chapter extends the survival-of-the-fittest evolutionary environment to consider the possibility that new political parties, when they first come into existence, do not pick decision rules at random but instead choose rules that have a track record of past success. This is done by adding replicator-mutator dynamics to the model, according to which the probability that each rule is selected by a new party is an evolving but noisy function of that rule's past performance. Estimating characteristic outputs when this type of positive feedback enters the dynamic model creates new methodological challenges. The simulation results show that it is very rare for one decision rule to drive out all others over the long run. While the diversity of decision rules used by party leaders is drastically reduced with such positive feedback in the party system, and while some particular decision rule is typically prominent over a certain period of time, party systems in which party leaders use different decision rules are sustained over substantial periods.
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28

LAND.TECHNIK AgEng 2019. VDI Verlag, 2019. http://dx.doi.org/10.51202/9783181023617.

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Dieser VDI-Bericht ist ausschließlich als PDF-Dokument erschienen! Content half of it… Analysis of Drive Trains Model-Based Chiptuning Detection of Diesel Engines 1 M. Hinrichs, P. Pickel, John Deere GmbH, Kaiserslautern R. Isermann, Institute of Automatic Control, Darmstadt An Analysis of the Energy Consumption in the High-Pressure System of an Agricultural Tractor through Modeling and Experiment 9 X. Tian, A. Vacca, Purdue University, West Lafayette, IN, USA; S. Fiorati, F. Pintore, CNH Industrial S.p.A, Modena, Italy Multi-Domain Simulation for the Assessment of the NVH Behaviour of a Tractor with Hydrostatic-Mechanical Power Split Transmission 19 G. Pasch, G. Jacobs, G. Höpfner, J. Berroth, Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Aachen Methods to evaluate steering performance of agricultural tractors 29 S. Liljenberg, M. Frederiksen, T. H. Langer, Danfoss Power Solutions, Nordborg, Denmark Tyres and Soil Soil pressure and pulling be...
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29

Breu, Ruth, Robert B. France, Colin Atkinson, and Jürgen Kazmeier. Model Driven Engineering Languages and Systems: 15th International Conference, MODELS 2012, Innsbruck, Austria, September 30 -- October 5, 2012, Proceedings. Springer, 2012.

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30

Breu, Ruth, Robert B. France, and Jürgen Kazmeier. Model Driven Engineering Languages and Systems: 15th International Conference, MODELS 2012, Innsbruck, Austria, September 30 -- October 5, 2012, Proceedings. Springer, 2012.

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31

Theory And Practice Of Model Transformations 5th International Conference Proceedings. Springer, 2012.

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32

Theory And Practice Of Model Transformations Second International Conference Proceedings. Springer, 2009.

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33

Muth, Thomas G. Functional Structures in Networks: AMLn - A Language for Model Driven Development of Telecom Systems. Springer, 2010.

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34

Radiation driven winds of hot stars: Some remarks on stationary models and spectrum synthesis in time-dependent simulations. Mu nchen: Max-Planck-Institut fu r Astrophysik, 1992.

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35

Joachim, Puls, and United States. National Aeronautics and Space Administration., eds. Radiation driven winds of hot stars: Some remarks on stationary models and spectrum synthesis in time-dependent simulations. München: Max-Planck-Institut für Astrophysik, 1992.

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36

Xia, Chang-liang. Permanent Magnet Brushless DC Motor Drives and Controls. Wiley & Sons, Incorporated, John, 2012.

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37

Xia, Chang-liang. Permanent Magnet Brushless DC Motor Drives and Controls. Wiley & Sons, Incorporated, John, 2012.

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38

Xia, Chang-liang. Permanent Magnet Brushless DC Motor Drives and Controls. Wiley & Sons, Incorporated, John, 2012.

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39

Muth, Thomas G. Functional Structures in Networks: AMLn - A Language for Model Driven Development of Telecom Systems (Signals and Communication Technology). Springer, 2005.

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40

Advanced Electric Drives: Analysis, Control and Modeling using Simulink. Mnpere, 2001.

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41

Stamenova, M., and S. Sanvito. Atomistic spin-dynamics. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533046.013.7.

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This article reviews recent advances towards the development of a truly atomistic time-dependent theory for spin-dynamics. The focus is on the s-d tight-binding model [where conduction electrons (s) are exchange-coupled to a number of classical spins (d)], including electrostatic corrections at the Hartree level, as the underlying electronic structure theory. In particular, the article considers one-dimensional (1D) magnetic atomic wires and their electronic structure, described by means of the s-d model. The discussion begins with an overview of the model spin Hamiltonian, followed by molecular-dynamics simulations of spin-wave dispersion in a s-d monoatomic chain and spin impurities in a non-magnetic chain. The current-induced motion in a magnetic domain wall (DW) is also explored, along with how an electric current can affect the magnetization landscape of a magnetic nano-object. The article concludes with an assessment of spin-motive force, and especially whether a driven magnetization dynamics can generate an electrical signal.
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42

Mohan, Ned. Advanced Electric Drives: Analysis, Control, and Modeling Using MATLAB / Simulink. Wiley & Sons, Incorporated, John, 2014.

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43

Mohan, Ned. Advanced Electric Drives: Analysis, Control, and Modeling Using MATLAB / Simulink. Wiley & Sons, Incorporated, John, 2014.

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44

Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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45

United States. National Aeronautics and Space Administration., ed. "Numerical simulation of rotation-driven plasma transport in the Jovian magnetosphere": Final technical report on NASA grant NAGW-4035, period of grant: April 1, 1994 to December 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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46

United States. National Aeronautics and Space Administration., ed. "Numerical simulation of rotation-driven plasma transport in the Jovian magnetosphere": Final technical report on NASA grant NAGW-4035, period of grant: April 1, 1994 to December 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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47

Underman, Kelly. Feeling Medicine. NYU Press, 2020. http://dx.doi.org/10.18574/nyu/9781479897780.001.0001.

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Gynecological teaching associates (GTAs) are trained laypeople who teach medical students the communication and technical skills of the pelvic examination while simultaneously serving as live models on whose bodies these same students practice. These programs are widespread in the United States and present a fascinating case for understanding contemporary emotional socialization in medical education. Feeling Medicine traces the origins of these programs in the Women’s Health Movement and in the nascent field of medical education research in the 1970s. It explores how these programs work at three major medical schools in Chicago using archival sources and interviews with GTAs, medical faculty, and medical students. This book argues that GTA programs embody the tension in medical education between the drive toward science and the ever-presence of emotion. It claims that new regimes of governance in medical education today rely on the modification of affect, or embodied capacities to feel and form attachments. Feeling Medicine thus explores what it means to make good physicians in an era of corporatized healthcare. In the process, it considers the role of simulation and the meaning of patient empowerment in the medical profession, as well as the practices that foster caring commitments between physicians and their patients—and those that are exploitable by for-profit healthcare.
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