Relevant bibliographies by topics / Matlab Simulation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Matlab Simulation'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Matlab Simulation"

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Elmajdub, Naser. "Aircraft Takeoff Simulation with MATLAB." IOSR Journal of Electrical and Electronics Engineering 7, no. 1 (2013): 28–34. http://dx.doi.org/10.9790/1676-0712834.

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Selvarani, P., and N. Malarvizhi. "Multibiometric authentication with MATLAB simulation." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 47. http://dx.doi.org/10.14419/ijet.v7i1.7.9389.

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Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.
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Jan, Shau-Shiun, Wyant Chan, and Todd Walter. "MATLAB Algorithm Availability Simulation Tool." GPS Solutions 13, no. 4 (February 18, 2009): 327–32. http://dx.doi.org/10.1007/s10291-009-0117-4.

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Yadav, Rahul. "Finite Element Analysis using MATLAB." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 764–69. http://dx.doi.org/10.22214/ijraset.2022.39902.

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Abstract: Finite Element Analysis or FEA is the simulation of a physical phenomenon using a numerical mathematic technique referred to as the Finite Element Method or FEM. This process is at the core of mechanical engineering, as well as a variety of other disciplines. It also is one of the key principles used in the development of simulation software. Engineers can use these FEM to reduce the number of physical prototypes and run virtual experiments to optimize their designs. Finite Element Analysis is used in various fields like structural, fluid flow, heat transfer to estimate the behavior of a component in real environment. There now exists growing body of knowledge connected with the development of mathematical models and numerical simulations of physical model. There are various software packages like Ansys, OptiStruct, COMSOL, Solidworks and many more which provide a close estimate in simulation models. Apart from these software MATLAB also has a Partial Differential Equation (PDE) toolbox which enables us to perform these simulations using some built-in functions and codes. However, a computational numerical technique is not an end of design rather it just provides a great estimate of the final component for which the results are only as good as the input provided and the final component in most of the cases require a physical testing in environment which it is meant to perform as a validation. Keywords: Brake pedal, MATLAB, Partial differential equation (PDE) toolbox, Tetrahedral mesh.
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Han, Cong, Pei Liu, and Tong Wang. "Study on Simulating Characteristic Parameters of PEMFC Using MATLAB/SIMULINK." Advanced Materials Research 588-589 (November 2012): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1230.

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A computer simulation program is presented for simulating the characteristic parameters of proton exchange membrane fuel cells (PEMFC) by using the MATLAB/SIMULINK and Visual Basic (VB). The simulation program is developed based on the empirical models for PEMFC. The simulation results show that the developed simulation program can be used as a useful tool for simulating the characteristic parameters of PEMFC.
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Kuchhal, Shikha. "Simulation of the Burst Error Correction using Matlab." International Journal of Scientific Research 2, no. 9 (June 1, 2012): 162–65. http://dx.doi.org/10.15373/22778179/sep2013/58.

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Dudwadka, Asawari, and Dr Y. S. Rao Dr. Y. S. Rao. "MATLAB Simulation of Inverters for Induction Heating System." International Journal of Scientific Research 2, no. 6 (June 1, 2012): 152–55. http://dx.doi.org/10.15373/22778179/june2013/49.

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Rigkova, E. "Simulation of control systems using MATLAB." Журнал технических исследований 2, no. 2 (April 25, 2016): 4. http://dx.doi.org/10.12737/19441.

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Veettikazhy, Madhu, Anders Kragh Hansen, Dominik Marti, Stefan Mark Jensen, Anja Lykke Borre, Esben Ravn Andresen, Kishan Dholakia, and Peter Eskil Andersen. "BPM-Matlab: an open-source optical propagation simulation tool in MATLAB." Optics Express 29, no. 8 (March 31, 2021): 11819. http://dx.doi.org/10.1364/oe.420493.

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Xie, Xiao Peng, and Yun Yi Li. "Computer Simulation Study Based on Matlab." Applied Mechanics and Materials 513-517 (February 2014): 3049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3049.

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This paper describes the use of one of the Matlab toolbox dynamic simulation tool Simulink simulation methods, and improve simulation speed, the simulation results analysis conducted in-depth elaboration. Also describes the use of SIMULINK simulation tools to achieve automatic control system modeling, analysis and design, simulation methods and Simulink-based video and image processing module sets into visualization, modular modeling idea.
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Dissertations / Theses on the topic "Matlab Simulation"

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Appiah, Frank [Verfasser]. "Simulation Analysis Using Matlab / Frank Appiah." München : GRIN Verlag, 2019. http://d-nb.info/1196689822/34.

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Gu, Chenchen. "Option Pricing Using MATLAB." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/382.

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This paper describes methods for pricing European and American options. Monte Carlo simulation and control variates methods are employed to price call options. The binomial model is employed to price American put options. Using daily stock data I am able to compare the model price and market price and speculate as to the cause of difference. Lastly, I build a portfolio in an Interactive Brokers paper trading [1] account using the prices I calculate. This project was done a part of the masters capstone course Math 573: Computational Methods of Financial Mathematics.
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Pan, Linlin. "Research and simulation on speech recognition by Matlab." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-16950.

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With the development of multimedia technology, speech recognition technology has increasingly become a hotspot of research in recent years. It has a wide range of applications, which deals with recognizing the identity of the speakers that can be classified into speech identification and speech verification according to decision modes.The main work of this thesis is to study and research the techniques, algorithms of speech recognition, thus to create a feasible system to simulate the speech recognition. The research work and achievements are as following: First: The author has done a lot of investigation in the field of speech recognition with the adequate research and study. There are many algorithms about speech recognition, to sum up, the algorithms can divided into two categories, one of them is the direct speech recognition, which means the method can recognize the words directly, and another prefer the second method that recognition based on the training model. Second: find a useable and reasonable algorithm and make research about this algorithm. Besides, the author has studied algorithms, which are used to extract the word's characteristic parameters based on MFCC(Mel frequency Cepstrum Coefficients) , and training the Characteristic parameters based on the GMM(Gaussian mixture mode) . Third: The author has used the MATLAB software and written a program to implement the speech recognition algorithm and also used the speech process toolbox in this program. Generally speaking, whole system includes the module of the signal process, MFCC characteristic parameter and GMM training. Forth: Simulation and analysis the results. The MATLAB system will read the wav file, play it first, and then calculate the characteristic parameters automatically. All content of the speech signal have been distinguished in the last step. In this paper, the author has recorded speech from different people to test the systems and the simulation results shown that when the testing environment is quiet enough and the speaker is the same person to record for 20 times, the performance of the algorithm is approach to 100% for pair of words in different and same syllable. But the result will be influenced when the testing signal is surrounded with certain noise level. The simulation system won’t work with a good output, when the speaker is not the same one for recording both reference and testing signal.
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Kheirollah, Amir. "Monte Carlo Simulation of Heston Model in MATLAB GUI." Thesis, Mälardalen University, Mälardalen University, Department of Mathematics and Physics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-4253.

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In the Black-Scholes model, the volatility considered being deterministic and it causes some

inefficiencies and trends in pricing options. It has been proposed by many authors that the

volatility should be modelled by a stochastic process. Heston Model is one solution to this

problem. To simulate the Heston Model we should be able to overcome the correlation

between asset price and the stochastic volatility. This paper considers a solution to this issue.

A review of the Heston Model presented in this paper and after modelling some investigations

are done on the applet.

Also the application of this model on some type of options has programmed by MATLAB

Graphical User Interface (GUI).

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Fason, Brandon J. "Modeling and simulation of synthetic aperture radars in MATLAB." Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/34661.

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Approved for public release; distribution is unlimited
The goal of this research is to produce a synthetic aperture radar (SAR) simulation in MATLAB that will provide a framework to investigate the effects of signal type and emission parameters on the resolution and accuracy of the simulated image. A total of three simulations were created for this research. The first two, which are range imaging and cross-range imaging, are the fundamental components of SAR and were vital to understanding the complexities of SAR. The signal types to be studied are a sinusoidal rectangular pulse, a linearly frequency modulated chirp, and band-limited Gaussian white noise. The simulations show that it is possible to produce usable images from each of the signal types examined, with the LFM chirp signal consistently producing the greatest resolution. Using noise as an input signal produced an exciting result with results almost on par with a sinusoidal pulse.
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Deist, Heino. "A dynamic CIP/CIL process simulation using MATLAB SIMULINK." Thesis, Cape Peninsula University of Technology, 2008. http://hdl.handle.net/20.500.11838/1923.

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Thesis (Masters Degree(Chemical Engineering))--Cape Peninsula University of Technology, 2008
ABSTRACT Carbon-in-pulp (CIP) technology is firmly entrenched in the mining sector due to numerous advances in the last few decades. CIP technology recovers complexed gold in solution from slurry streams by contacting the pulp with carbon and separating the two by screening. The carbon-in-leach (CIL) process, where the pulp contains free cyanide, is closely related to CIP. Both these processes, especially CIL, are complex and are governed by a number of interacting unit processes. The overall process efficiency is dependant on a large number of variables, making the process difficult to optimize. This study uses simple adsorption and leaching rate equations in order to predict dynamic CIL/CIP plant performance under varying operating conditions. This will assist plant metallurgists to achieve optimum efficiency, highlight plant sensitivity to certain variables and will ultimately result in proactive process control. Various assumptions were made in order to keep the modeling considerations as simple and realistic as possible. The process was assumed to have linear kinetic and equilibrium operation. The process was simulated using Simulink in Matlab. Variables are solved on a time step basis by Matlab’s built in optimization algorithms. Simulation results illustrated that the use of simple leaching and adsorption rate expressions are effective for investigating dynamic plant behavior under changing operating conditions.
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Gollee, Christian, and Clemens Troll. "Mehrkörpersimulation eines ebenen Koppelgetriebes mittels Matlab / ADAMS -Co-Simulation." Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-225922.

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Mit Hilfe der Matlab/ADAMS-Co-Simulation wird ein ebenes Koppelgetriebe (5- Gelenk) untersucht und anschließend die Wirkpaarung mit einem Stückgut betrachtet. Dabei werden verschiedene Modellierungsstufen angewendet und die Simulationsergebnisse Messergebnissen vom Versuchsstand gegenübergestellt. Daneben wird die grundlegende Herangehensweise beim Einsatz dieser Simulationswerkzeuge erläutert.
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Deist, Heino. "A dynamic CIP/CIL process simulation using MATLAB SIMULINK /." [S.l. : s.n.], 2005. http://dk.cput.ac.za/cgi/viewcontent.cgi?article=1006&context=td_cput.

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Chromiak, Michael. "AURIX target v systému MATLAB Simulink." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413260.

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This diploma thesis deals with the implementation of SIL and PIL simulation for the microcontroller Aurix TriCore TC277D performed in the Matlab Simulink. The realization of the simulation represents, among other things, the implementation of the simulated model into the microcontroller, as well as the creation of an interface for the communication of the microcontroller with the Matlab Simulink. The accuracy of SIL and PIL simulations was verified by comparing the simulated waveforms from the simulation in Simulink. The simulated model used a thermal model of a car cabin created in the previous bachelor's thesis of the author of this diploma thesis. The model in TC277D, as well as the configuration for SIL / PIL is created for use with the C programming language. The work also includes instructions according to which the model and configuration can be modified so that the simulations can be performed on any device containing the necessary software. From the comparison of the simulated data is it clear that the created configuration can be used for SIL or PIL simulation.
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Havlát, Petr. "Simulátor mobilních robotů v prostředí Matlab/Simulink." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217850.

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The main goal is the programming scene MATLAB/Simulink creation of graphic user interface (GUI), which allows the simulation of mobile robots movement. The work covers two types of these robots – first one is the robot with differentially controlled truck and second one auto robot (car-like robot). As a part of this draft GUI, there are also possibilities of showing all trajectory or only actual position, selection of step after which the robot can delineate and possibility of layout between the positions by using the button back and forwards.
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Books on the topic "Matlab Simulation"

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Marghitu, Dan B., Hamid Ghaednia, and Jing Zhao. Mechanical Simulation with MATLAB®. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88102-3.

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Liu, Jinkun. Intelligent Control Design and MATLAB Simulation. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5263-7.

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Lapresté, Jean-Thieerry. Aide-mémoire MATLAB. Paris: Ellipses, 2002.

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Bode, Helmut. MATLAB-SIMULINK: Analyse und Simulation dynamischer Systeme. 2nd ed. Wiesbaden: Teubner, 2006.

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Blanchet, Gérard. Traitement numérique du signal: Simulation sous Matlab. Paris: Hermès, 1998.

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Brown, Arik D. Electronically steered arrays: MATLAB modeling and simulation. Boca Raton: CRC Press, 2013.

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Electronically steered arrays: MATLAB modeling and simulation. Boca Raton: CRC Press, 2013.

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Klee, Harold. Simulation of dynamic systems with MATLAB and Simulink. 2nd ed. Boca Raton, FL: CRC Press, 2011.

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Spiegel, Colleen. PEM fuel cell modeling and simulation using Matlab. Boston: Academic Press/Elsevier, 2008.

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Dynamic simulation of electric machinery: Using MATLAB/SIMULINK. Upper Saddle River, N.J: Prentice Hall PTR, 1998.

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Book chapters on the topic "Matlab Simulation"

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Pietruszka, Wolf Dieter. "Simulation unter MATLAB." In MATLAB in der Ingenieurpraxis, 145–210. Wiesbaden: Vieweg+Teubner Verlag, 2005. http://dx.doi.org/10.1007/978-3-322-93103-0_4.

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Pietruszka, Wolf Dieter. "Simulation unter MATLAB." In MATLAB® und Simulink® in der Ingenieurpraxis, 235–334. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06420-4_5.

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Turk, Irfan. "Applications in Simulation." In Practical MATLAB, 105–20. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5281-9_5.

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Wanhammar, Lars. "Topological Simulation." In Analog Filters Using MATLAB, 277–89. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-92767-1_10.

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Wanhammar, Lars. "Immitance Simulation." In Analog Filters Using MATLAB, 253–62. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-92767-1_8.

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Tlelo-Cuautle, Esteban, José de Jesús Rangel-Magdaleno, and Luis Gerardo De la Fraga. "Matlab-Simulink Co-Simulation." In Engineering Applications of FPGAs, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34115-6_3.

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Orlowski, Peter F. "Simulation mit MATLAB Simulink." In Praktische Regeltechnik, 400–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41233-2_8.

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Das, Saptarshi, and Indranil Pan. "MATLAB Based Simulation Tools." In Fractional Order Signal Processing, 97–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23117-9_7.

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Pietruszka, Wolf Dieter. "Simulation unter MATLAB®." In MATLAB® und Simulink® in der Ingenieurpraxis, 225–316. Wiesbaden: Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8630-9_5.

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Pietruszka, Wolf Dieter, and Michael Glöckler. "Simulation unter MATLAB®." In MATLAB® und Simulink® in der Ingenieurpraxis, 265–374. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-29740-4_5.

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Conference papers on the topic "Matlab Simulation"

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Ivanov, S. "MATLAB-SIMULINK." In International Conference on Simulation (1998). IEE, 1998. http://dx.doi.org/10.1049/cp:19980637.

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Gordievsky, Evgeny, Alexey Miroshnichenko, and Evgeny Solomin. "Wind Turbine Matlab Simulation Model." In 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2020. http://dx.doi.org/10.1109/icieam48468.2020.9112076.

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Rizescu, Dana G., and Ciprian Ion Rizescu. "Lenses behavior simulation using Matlab." In Advances in 3OM: Opto-Mechatronics, Opto-Mechanics, and Optical Metrology, edited by Jannick P. Rolland, Virgil-Florin Duma, and Adrian G. H. Podoleanu. SPIE, 2022. http://dx.doi.org/10.1117/12.2601969.

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"Simulation Deployment Blockset for MATLAB/Simulink." In 2016 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2016. http://dx.doi.org/10.22360/springsim.2016.tmsdevs.026.

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Mladenov, Valeri M., and Stoyan M. Kirilov. "Memristor Modeling In MATLAB & PSPICE." In 29th Conference on Modelling and Simulation. ECMS, 2015. http://dx.doi.org/10.7148/2015-0432.

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Esken, Bruce L., and Brian L. Clayton. "MATLAB/Simulink analytic radar modeling environment." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Alex F. Sisti and Dawn A. Trevisani. SPIE, 2001. http://dx.doi.org/10.1117/12.440023.

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Guan, Xiao-han, Meng-meng Zhang, and Yong Zheng. "Matlab Simulation in Signals & Systems Using Matlab at Different Levels." In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.476.

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Veettikazhy, Madhu, Anders K. Hansen, Dominik Marti, Stefan M. Jensen, Anja L. Borre, Esben R. Andresen, Kishan Dholakia, and Peter E. Andersen. "BPM-Matlab: an open-source optical propagation simulation tool in MATLAB." In Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXI, edited by Israel Gannot and Katy Roodenko. SPIE, 2021. http://dx.doi.org/10.1117/12.2582547.

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Naderlinger, Andreas. "Simulating execution time variations in Matlab/Simulink." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247891.

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Zhang, Yuhong. "Simulation of Optical Phenomena with Matlab." In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.539.

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Reports on the topic "Matlab Simulation"

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Singh, M., E. Muljadi, J. Jonkman, V. Gevorgian, I. Girsang, and J. Dhupia. Simulation for Wind Turbine Generators -- With FAST and MATLAB-Simulink Modules. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1130628.

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Ueda, Jason, David Daniszewski, John Monroe, Abul Masrur, Eric Charbeneau, Eric Jochum, and Rakesh Patel. Electrical Modeling and Simulation With Matlab/Simulink and Graphical User Interface Software. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada489033.

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Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.
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Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.
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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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6

Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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8

Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ytgv8834.

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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining functionality in the aftermath of moderate to strong earthquakes, referred to as “resiliency” in earthquake engineering research. Moreover, the associated construction philosophy is being modernized with the utilization of accelerated bridge construction (ABC) techniques, which strive to reduce the impact of construction on traffic, society, economy and on-site safety. This report presents two bridge systems that target the aforementioned issues. A study that combined numerical and experimental research was undertaken to characterize the seismic performance of these bridge systems. The first part of the study focuses on the structural system-level response of highway bridges that incorporate a class of innovative connecting devices called the “V-connector,”, which can be used to connect two components in a structural system, e.g., the column and the bridge deck, or the column and its foundation. This device, designed by ACII, Inc., results in an isolation surface at the connection plane via a connector rod placed in a V-shaped tube that is embedded into the concrete. Energy dissipation is provided by friction between a special washer located around the V-shaped tube and a top plate. Because of the period elongation due to the isolation layer and the limited amount of force transferred by the relatively flexible connector rod, bridge columns are protected from experiencing damage, thus leading to improved seismic behavior. The V-connector system also facilitates the ABC by allowing on-site assembly of prefabricated structural parts including those of the V-connector. A single-column, two-span highway bridge located in Northern California was used for the proof-of-concept of the proposed V-connector protective system. The V-connector was designed to result in an elastic bridge response based on nonlinear dynamic analyses of the bridge model with the V-connector. Accordingly, a one-third scale V-connector was fabricated based on a set of selected design parameters. A quasi-static cyclic test was first conducted to characterize the force-displacement relationship of the V-connector, followed by a hybrid simulation (HS) test in the longitudinal direction of the bridge to verify the intended linear elastic response of the bridge system. In the HS test, all bridge components were analytically modeled except for the V-connector, which was simulated as the experimental substructure in a specially designed and constructed test setup. Linear elastic bridge response was confirmed according to the HS results. The response of the bridge with the V-connector was compared against that of the as-built bridge without the V-connector, which experienced significant column damage. These results justified the effectiveness of this innovative device. The second part of the study presents the HS test conducted on a one-third scale two-column bridge bent with self-centering columns (broadly defined as “resilient columns” in this study) to reduce (or ultimately eliminate) any residual drifts. The comparison of the HS test with a previously conducted shaking table test on an identical bridge bent is one of the highlights of this study. The concept of resiliency was incorporated in the design of the bridge bent columns characterized by a well-balanced combination of self-centering, rocking, and energy-dissipating mechanisms. This combination is expected to lead to minimum damage and low levels of residual drifts. The ABC is achieved by utilizing precast columns and end members (cap beam and foundation) through an innovative socket connection. In order to conduct the HS test, a new hybrid simulation system (HSS) was developed, utilizing commonly available software and hardware components in most structural laboratories including: a computational platform using Matlab/Simulink [MathWorks 2015], an interface hardware/software platform dSPACE [2017], and MTS controllers and data acquisition (DAQ) system for the utilized actuators and sensors. Proper operation of the HSS was verified using a trial run without the test specimen before the actual HS test. In the conducted HS test, the two-column bridge bent was simulated as the experimental substructure while modeling the horizontal and vertical inertia masses and corresponding mass proportional damping in the computer. The same ground motions from the shaking table test, consisting of one horizontal component and the vertical component, were applied as input excitations to the equations of motion in the HS. Good matching was obtained between the shaking table and the HS test results, demonstrating the appropriateness of the defined governing equations of motion and the employed damping model, in addition to the reliability of the developed HSS with minimum simulation errors. The small residual drifts and the minimum level of structural damage at large peak drift levels demonstrated the superior seismic response of the innovative design of the bridge bent with self-centering columns. The reliability of the developed HS approach motivated performing a follow-up HS study focusing on the transverse direction of the bridge, where the entire two-span bridge deck and its abutments represented the computational substructure, while the two-column bridge bent was the physical substructure. This investigation was effective in shedding light on the system-level performance of the entire bridge system that incorporated innovative bridge bent design beyond what can be achieved via shaking table tests, which are usually limited by large-scale bridge system testing capacities.
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9

Development of an Adaptive Efficient Thermal/Electric Skipping Control Strategy Applied to a Parallel Plug-in Hybrid Electric Vehicle. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0737.

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In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy - ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications.
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10

An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).
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