Littérature scientifique sur le sujet « Multiparameter optimization »
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Articles de revues sur le sujet "Multiparameter optimization":
Hajima, R., et R. Nagai. « Multiparameter optimization of an ERL injector ». Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment 557, no 1 (février 2006) : 103–5. http://dx.doi.org/10.1016/j.nima.2005.10.060.
Daboczi, T., et I. Kollar. « Multiparameter optimization of inverse filtering algorithms ». IEEE Transactions on Instrumentation and Measurement 45, no 2 (avril 1996) : 417–21. http://dx.doi.org/10.1109/19.492758.
Court, L. E., et R. Speller. « A multiparameter optimization of digital mammography ». Physics in Medicine and Biology 40, no 11 (1 novembre 1995) : 1841–61. http://dx.doi.org/10.1088/0031-9155/40/11/006.
Elkattan, M., et A. Kamel. « Multiparameter Optimization for Electromagnetic Inversion Problem ». Advanced Electromagnetics 6, no 3 (21 octobre 2017) : 94. http://dx.doi.org/10.7716/aem.v6i3.438.
Chandrasekar, V., Eugenio Gorgucci et Gianfranco Scarchilli. « Optimization of Multiparameter Radar Estimates of Rainfall ». Journal of Applied Meteorology 32, no 7 (juillet 1993) : 1288–93. http://dx.doi.org/10.1175/1520-0450(1993)032<1288:oomreo>2.0.co;2.
Cernea, Aurelian. « Minimum principle and controllability for multiparameter discrete inclusions via derived cones ». Discrete Dynamics in Nature and Society 2006 (2006) : 1–12. http://dx.doi.org/10.1155/ddns/2006/96505.
Shmitko, E. I., A. A. Rezanov et A. A. Bedarev. « Multiparameter structure optimization of the cellular silicate concrete ». Magazine of Civil Engineering 38, no 3 (avril 2013) : 15–23. http://dx.doi.org/10.5862/mce.38.2.
Segall, Matthew. « Advances in multiparameter optimization methods forde novodrug design ». Expert Opinion on Drug Discovery 9, no 7 (3 mai 2014) : 803–17. http://dx.doi.org/10.1517/17460441.2014.913565.
Li, Na, Xinchen Huang, Huijie Zhao, Xianfei Qiu, Ruonan Geng, Xiuping Jia et Daming Wang. « Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery ». IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no 4 (avril 2018) : 1348–57. http://dx.doi.org/10.1109/jstars.2018.2814617.
Nomura, Laurel, Vernon C. Maino et Holden T. Maecker. « Standardization and optimization of multiparameter intracellular cytokine staining ». Cytometry Part A 73A, no 11 (8 juillet 2008) : 984–91. http://dx.doi.org/10.1002/cyto.a.20602.
Thèses sur le sujet "Multiparameter optimization":
Kremers, Gert-Jan. « Optimization of fluorescent proteins for novel quantitative multiparameter microscopy approaches ». [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2006. http://dare.uva.nl/document/123454.
Pleva, František. « Metoda odezvových ploch ve spojení s CFD pro tvarovou optimalizaci ». Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-449797.
Pinard, Hugo. « Imagerie électromagnétique 2D par inversion des formes d'ondes complètes : Approche multiparamètres sur cas synthétiques et données réelles ». Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAU041/document.
Ground Penetrating Radar (GPR) is a geophysical investigation method based on electromagnetic waves propagation in the underground. With frequencies ranging from 5 MHz to a few GHz and a high sensitivity to electrical properties, GPR provides reflectivity images in a wide variety of contexts and scales: civil engineering, geology, hydrogeology, glaciology, archeology. However, in some cases, a better understanding of some subsurface processes requires a quantification of the physical parameters of the subsoil. For this purpose, inversion of full waveforms, a method initially developed for seismic exploration that exploits all the recorded signals, could prove effective. In this thesis, I propose methodological developments using a multiparameter inversion approach (dielectric permittivity and conductivity), for two-dimensional transmission configurations. These developments are then applied to a real data set acquired between boreholes.In a first part, I present the numerical method used to model the propagation of electromagnetic waves in a heterogeneous 2D environment, a much-needed element to carry out the process of imaging. Then, I introduce and study the potential of standard local optimization methods (nonlinear conjugate gradient, l-BFGS, Newton truncated in its Gauss-Newton and Exact-Newton versions) to fight the trade-off effects related to the dielectric permittivity and to the electrical conductivity. In particular, I show that effective decoupling is possible only with a sufficiently accurate initial model and the most sophisticated method (truncated Newton). As in the general case, this initial model is not available, it is necessary to introduce a scaling factor which distributes the relative weight of each parameter class in the inversion. In a realistic medium and for a cross-hole acquisition configuration, I show that the different optimization methods give similar results in terms of parameters decoupling. It is eventually the l-BFGS method that is used for the application to the real data, because of lower computation costs.In a second part, I applied the developed Full waveform inversion methodology to a set of real data acquired between two boreholes located in carbonate formations, in Rustrel (France, 84). This inversion is carried out together with a synthetic approach using a model representative of the studied site and with a similar acquisition configuration. This approach enables us to monitor and validate the observations and conclusions derived from data inversion. It shows that reconstruction of dielectrical permittivity is very robust. Conversely, conductivity estimation suffers from two major couplings: the permittivity and the amplitude of the estimated source. The derived results are successfully compared with independent data (surface geophysics and rock analysis on plugs) and provides a high resolution image of the geological formation. On the other hand, a 3D analysis confirms that 3D structures presenting high properties contrasts, such as the buried gallery present in our site, would require a 3D approach, notably to better explain the observed amplitudes
Chapitres de livres sur le sujet "Multiparameter optimization":
Benzaoui, Abderrahmane, et Régis Duvigneau. « Multiparameter Shape Optimization ». Dans Multidisciplinary Design Optimization in Computational Mechanics, 265–85. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118600153.ch6.
Talevi, Alan. « Central Nervous System Multiparameter Optimization Desirability ». Dans The ADME Encyclopedia, 1–8. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_150-1.
Segall, Matthew D., et Edmund J. Champness. « Multiparameter Optimization of ADMET for Drug Design ». Dans Predictive ADMET, 145–66. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch8.
Balac, Martina, et Aleksandar Grbovic. « Multiparameter Structural Optimization of Pressure Vessel with Two Nozzles ». Dans Experimental and Numerical Investigations in Materials Science and Engineering, 148–58. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99620-2_12.
« Multiparameter Extremum Seeking ». Dans Real-Time Optimization by Extremum-Seeking Control, 21–45. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471669784.ch2.
Polajnar, D., L. Lukic et V. Solaja. « AN INTERACTIVE SIMULATION MODEL FOR MULTIPARAMETER OPTIMIZATION OF CUTTING PROCESSES IN FMS ». Dans Information Control Problems in Manufacturing Technology 1989, 197–202. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-08-037023-1.50038-8.
Asmaryan, Albert, Alexey Levanov et Irina Borovik. « Research of Multichannel User Data to Identify the Degree of Similarity ». Dans Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking, 30–46. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8188-8.ch002.
Actes de conférences sur le sujet "Multiparameter optimization":
Jafroudi, Hamid, E. P. Muntz et Robert J. Jennings. « Multiparameter optimization of mammography : an update ». Dans Medical Imaging 1994, sous la direction de Rodney Shaw. SPIE, 1994. http://dx.doi.org/10.1117/12.174244.
Ma, Bo, Chienliu Chang, Huseyin Kagan Oguz, Kamyar Firouzi et Butrus T. Khuri-Yakub. « Multiparameter optimization of vented CMUTs for airborne applications ». Dans 2017 IEEE International Ultrasonics Symposium (IUS). IEEE, 2017. http://dx.doi.org/10.1109/ultsym.2017.8091604.
Januszkiewicz, Lukasz, Slawomir Hausman, Lukasz Jopek et Paolo Di Barba. « Hierarchical multiparameter optimization of dual-band wearable antenna ». Dans 2017 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF). IEEE, 2017. http://dx.doi.org/10.1109/isef.2017.8090667.
de Almeida, Breno Vincenzo, et Renato Pavanello. « PIEZOELECTRIC HARVESTER TOPOLOGY OPTIMIZATION USING A MULTIPARAMETER MATERIAL MODEL ». Dans XXXVIII Iberian-Latin American Congress on Computational Methods in Engineering. Florianopolis, Brazil : ABMEC Brazilian Association of Computational Methods in Engineering, 2017. http://dx.doi.org/10.20906/cps/cilamce2017-0892.
Jafroudi, Hamid, Robert J. Jennings, E. P. Muntz, Matthew T. Freedman et Seong K. Mun. « Multiparameter optimization of mammography with alternative x-ray sources ». Dans Medical Imaging 1995, sous la direction de Richard L. Van Metter et Jacob Beutel. SPIE, 1995. http://dx.doi.org/10.1117/12.208329.
Chen, Guanbo, et Mahta Moghaddam. « GPU accelerated 3D nonlinear time domain inversion of realistic breast phantoms with multiparameter optimization ». Dans 2013 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2013. http://dx.doi.org/10.1109/usnc-ursi.2013.6715512.
Toteff, Jens, et Miguel Asuaje Tovar. « Design and Multiparameter Optimization of Jet-Pumps in a Pipeline Loops Using CFD Tools ». Dans ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83440.
Allevato, Adam, Mitch Pryor et Andrea L. Thomaz. « Multiparameter Real-World System Identification Using Iterative Residual Tuning ». Dans ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22734.
Lin, Yun-Jeng, et Sherif T. Noah. « Using Genetic Algorithms for the Optimal Design of Fluid Journal Bearing ». Dans ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8171.
Niffenegger, M., D. F. Mora et H. Kottmann. « Non-Destructive Evaluation of RPV Embrittlement by Means of the Thermoelectric Power Method ». Dans ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21446.