Academic literature on the topic 'Estimation des microstructures'
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Journal articles on the topic "Estimation des microstructures":
Amnuaykijvanit, O., S. Anantawaraskul, and T. Rakthanmanon. "Estimation of ethylene/1-butene copolymerization conditions using the autoencoder model." Journal of Physics: Conference Series 2175, no. 1 (January 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2175/1/012028.
Palevicius, Arvydas, and Giedrius Janusas. "Analysis of Periodical Microstructures Using Optical Methods." Advanced Materials Research 433-440 (January 2012): 2021–28. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2021.
Liu, Zaobao, Jianfu Shao, Weiya Xu, and Chong Shi. "Estimation of Elasticity of Porous Rock Based on Mineral Composition and Microstructure." Advances in Materials Science and Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/512727.
Blanc, Rémi, Pierre Baylou, Christian Germain, and Jean-Pierre Da Costa. "Confidence Bounds for the Estimation of the Volume Phase Fraction from a Single Image in a Nickel Base Superalloy." Microscopy and Microanalysis 16, no. 3 (March 30, 2010): 273–81. http://dx.doi.org/10.1017/s1431927610000139.
Ben Ahmed, Amal, Ahmad Bahloul, Mohamed Iben Houria, Anouar Nasr, and Raouf Fathallah. "Multiaxial fatigue life estimation of defective aluminum alloy considering the microstructural heterogeneities effect." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 233, no. 9 (August 16, 2018): 1830–42. http://dx.doi.org/10.1177/1464420718792024.
McNelley, Terry R., Keiichiro Oh-ishi, and Alexandre P. Zhilyaev. "Microstructure Evolution and Microstructure-Property Relationships in Friction Stir Processing of NiAl Bronze." Materials Science Forum 539-543 (March 2007): 3745–50. http://dx.doi.org/10.4028/www.scientific.net/msf.539-543.3745.
Liu, Lishuai, Peng Wu, Yanxun Xiang, and Fu-Zhen Xuan. "Autonomous characterization of grain size distribution using nonlinear Lamb waves based on deep learning." Journal of the Acoustical Society of America 152, no. 3 (September 2022): 1913–21. http://dx.doi.org/10.1121/10.0014289.
Kawa, Marek. "Failure Criterion for Brick Masonry: A Micro-Mechanics Approach." Studia Geotechnica et Mechanica 36, no. 3 (February 28, 2015): 37–48. http://dx.doi.org/10.2478/sgem-2014-0025.
Yuan, Jianhui, Yu Pan, and Xin Zhang. "Ultrahigh Frequency Data Liquidity Duration Estimation: A Case Study of Chinese A Shares." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/371272.
Schouwenaars, Rafael, Víctor H. Jacobo, Sara M. Cerrud, and Armando Ortiz. "Finite Element Simulation of Microstresses in a Traditional FGM: The Case of Soft Tribo-Alloys." Materials Science Forum 492-493 (August 2005): 421–28. http://dx.doi.org/10.4028/www.scientific.net/msf.492-493.421.
Dissertations / Theses on the topic "Estimation des microstructures":
Blatt, Samantha Heidi. "From the Mouths of Babes: Using Incremental Enamel Microstructures to Evaluate the Applicability of the Moorrees Method of Dental Formation to the Estimation of Age of Prehistoric Native American Children." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365696693.
Yang, Zheyi. "Numerical methods to estimate brain micro-structure from diffusion MRI data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAE016.
Diffusion magnetic resonance imaging (diffusion MRI) is a widely used non-invasive imaging modality to probe the micro-structural properties of biological tissues below the spatial resolution, by indirectly measuring the diffusion displacement of water molecules. Due to the geometrical complexity of the brain and intricate diffusion MRI mechanism, it is challenging to directly link the received signals to meaningful biophysical parameters, such as axon radii or volume fraction.In recent years, several biophysical models have been introduced to address the issue of weak interpretability. These models represent the diffusion MRI signals as a mixture of analytical signals under certain assumptions, e.g. impermeable membranes, of various disconnected simple geometries, such as spheres and sticks. Subsequently, they aim to extract the parameters of these geometries, which correlate with biophysical parameters, by inverting the analytical expression.However, the validity of these assumptions remains undetermined in actual experiments.The objective of this thesis is to improve the microstructure estimation reliability and efficiency from two perspectives. First, to facilitate the quantitative study of the valid range of biophysical models and the effect of geometrical deformation and cell membrane permeability via simulation, we proposed two reduced models derived from the Bloch-Torrey equation, respectively. For the case of the presence of permeable membranes, a new simulation approach using impermeable Laplace eigenbasis is proposed. As for the geometrical deformation, we use an asymptotic expansion with respect to the deformation angles to approximate the signal. These two reduced models enable efficient computation of signals for various values of deformation/permeability. Numerical simulations reveal that these two models can fast compute the signals within a reasonable error level compared to existing methods. Several studies have been conducted about the effects of permeability and deformation on the signals or the apparent diffusion coefficient (ADC), using the proposed models.Second, instead of inverting a simplified geometries model, we present a novel approach to associate soma size in gray matter by intermediary biomarkers. Numerical simulations identify a correlation between the volume-weighted soma radius/volume fraction and the inflection point of direction-averaged signals at high b-values (b>2500s/mm^2), offering insights for microstructure estimation. We fit a fully connected neural network using these biomarkers and compared to biophysical models, this approach offers comparable results on both synthetic and in vivo data and fast estimation since no inversion is involved
FitzGerald, Charles Michael. "Tooth crown formation and the variation of enamel microstructural growth markers in modern humans." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360038.
Yevstihnyeyev, Roman. "Estimation of Asset Volatility and Correlation Over Market Microstructure Noise in High-Frequency Data." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:14398547.
Fang, Chengran. "Neuron modeling, Bloch-Torrey equation, and their application to brain microstructure estimation using diffusion MRI." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG010.
Non-invasively estimating brain microstructure that consists of a very large number of neurites, somas, and glial cells is essential for future neuroimaging. Diffusion MRI (dMRI) is a promising technique to probe brain microstructural properties below the spatial resolution of MRI scanners. Due to the structural complexity of brain tissue and the intricate diffusion MRI mechanism, in vivo microstructure estimation is challenging.Existing methods typically use simplified geometries, particularly spheres, and sticks, to model neuronal structures and to obtain analytical expressions of intracellular signals. The validity of the assumptions made by these methods remains undetermined. This thesis aims to facilitate simulationdriven brain microstructure estimation by replacing simplified geometries with realistic neuron geometry models and the analytical intracellular signal expressions with diffusion MRI simulations. Combined with accurate neuron geometry models, numerical dMRI simulations can give accurate intracellular signals and seamlessly incorporate effects arising from, for instance, neurite undulation or water exchange between soma and neurites.Despite these advantages, dMRI simulations have not been widely adopted due to the difficulties in constructing realistic numerical phantoms, the high computational cost of dMRI simulations, and the difficulty in approximating the implicit mappings between dMRI signals and microstructure properties. This thesis addresses the above problems by making four contributions. First, we develop a high-performance opensource neuron mesh generator and make publicly available over a thousand realistic cellular meshes.The neuron mesh generator, swc2mesh, can automatically and robustly convert valuable neuron tracing data into realistic neuron meshes. We have carefully designed the generator to maintain a good balance between mesh quality and size. A neuron mesh database, NeuronSet, which contains 1213 simulation-ready cell meshes and their neuroanatomical measurements, was built using the mesh generator. These meshes served as the basis for further research. Second, we increased the computational efficiency of the numerical matrix formalism method by accelerating the eigendecomposition algorithm and exploiting GPU computing. The speed was increased tenfold. With similar accuracy, the optimized numerical matrix formalism is 20 times faster than the FEM method and 65 times faster than a GPU-based Monte Carlo method. By performing simulations on realistic neuron meshes, we investigated the effect of water exchange between somas and neurites, and the relationship between soma size and signals. We then implemented a new simulation method that provides a Fourier-like representation of the dMRI signals. This method was derived theoretically and implemented numerically. We validated the convergence of the method and showed that the error behavior is consistent with our error analysis. Finally, we propose a simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. By exploiting the powerful modeling and computational capabilities that are mentioned above, we have built a synthetic database containing the dMRI signals and microstructure parameters of 1.4 million artificial brain voxels. We have shown that this database can help approximate the underlying mappings of the dMRI signals to volume and surface fractions using artificial neural networks
Fernandez, Tapia Joaquin. "Modeling, optimization and estimation for the on-line control of trading algorithms in limit-order markets." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066354/document.
This PhD thesis focuses on the quantitative analysis of mathematical problems arising in the field of optimal algorithmic trading. Concretely, we propose a scientific approach in order to optimize processes related to the capture and provision of liquidity in electronic markets. Because of the strongly industry-focused character of this work, not only we are interested in giving rigorous mathematical results but also to understand this research project in the context of the different stages that come into play during the practical implementation of the tools developed throughout the following chapters (e.g. model interpretation, parameter estimation, programming etc.).From a scientific standpoint the core of our work focuses on two techniques taken from the world of optimization and probability; these are, stochastic control and stochastic approximation. In particular, we provide original academic results for the problem of high frequency market making and the problem of portfolio liquidation by using limit orders; both by using a backward optimization approach. We also propose a forward optimization framework to solve the market making problem; the latter approach being quite innovative for optimal trading, as it opens the door for machine learning techniques.From a practical angle, this PhD thesis seeks to create a bridge between academic research and practitioners. Our mathematical findings are constantly put in perspective in terms of their practical implementation. Hence, we focus a large part of our work on studying the different factors that are of paramount importance to understand when transforming our quantitative techniques into industrial value: understanding the underlying market microstructure, empirical stylized facts, data processing, discussion about the models, limitations of our scientific framework etc
Sun, Yucheng. "Essays in volatility estimation based on high frequency data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402831.
Basándonos en datos de precios de alta frecuencia, esta tesis se centra en la estimación de la covarianza realizada y la volatilidad integrada de precios de activos, y la aplicación de la estimación de la volatilidad para la detección de saltos en los precios. El primer capítulo utiliza el procedimiento LASSO para regularizar algunos estimadores de matrices de covarianza realizada de alta dimensión. Establecemos propiedades teóricas de los estimadores regularizados que muestran su precisión de estimación y la probabilidad de que revelen correctamente la estructura de red de los activos. En el segundo capítulo se propone un nuevo estimador de la volatilidad integrada que es la variación cuadrática de la parte continua en el proceso de precios. Este estimador se obtiene truncando el estimador de varianza realizado en dos escalas. Demostramos su consistencia en presencia de ruido de microestructura del mercado y saltos de actividad finitos o infinitos en el proceso de precios. El tercer capítulo emplea este estimador para diseñar un test para explorar la existencia de saltos en los precios con ruido.
Tunyavetchakit, Sophon [Verfasser], and Rainer [Akademischer Betreuer] Dahlhaus. "Volatility Decomposition and Nonparametric Estimation of Spot Volatility of Models with Poisson Sampling under Market Microstructure Noise / Sophon Tunyavetchakit ; Betreuer: Rainer Dahlhaus." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180615786/34.
[Verfasser], Sophon Tunyavetchakit, and Rainer [Akademischer Betreuer] Dahlhaus. "Volatility Decomposition and Nonparametric Estimation of Spot Volatility of Models with Poisson Sampling under Market Microstructure Noise / Sophon Tunyavetchakit ; Betreuer: Rainer Dahlhaus." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-214504.
Bornert, Michel. "Morphologie microstructurale et comportement mécanique ; caractérisations expérimentales, approches par bornes et estimations autocohérentes généralisées." Phd thesis, Ecole Nationale des Ponts et Chaussées, 1996. http://tel.archives-ouvertes.fr/tel-00113078.
Books on the topic "Estimation des microstructures":
Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, Mass: National Bureau of Economic Research, 2005.
Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, MA: National Bureau of Economic Research, 2005.
Deshpande, U. P., T. Shripathi, and A. V. Narlikar. Iron-oxide nanostructures with emphasis on nanowires. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533053.013.23.
Book chapters on the topic "Estimation des microstructures":
Drezet, J. M., G. U. Grün, and M. Gremaud. "Estimation of Boundary Conditions using Inverse Stationary Methods." In Microstructures, Mechanical Properties and Processes - Computer Simulation and Modelling, 299–304. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527606157.ch47.
Khan, I., F. Mohd Nor, and M. M. Abdul Jamil. "A Survey of Human Age Estimation Techniques from Bone Microstructures." In IFMBE Proceedings, 203–7. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0266-3_42.
Jallais, Maëliss, and Demian Wassermann. "Single Encoding Diffusion MRI: A Probe to Brain Anisotropy." In Mathematics and Visualization, 171–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_8.
Rošt’áková, Zuzana, Georg Dorffner, Önder Aydemir, and Roman Rosipal. "Estimation of Sleep Quality by Using Microstructure Profiles." In Artificial Intelligence in Medicine, 105–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59758-4_12.
Li, Yuxing, Yu Qin, Zhiwen Liu, and Chuyang Ye. "Pretraining Improves Deep Learning Based Tissue Microstructure Estimation." In Computational Diffusion MRI, 173–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73018-5_14.
Holzer, Lorenz, Philip Marmet, Mathias Fingerle, Andreas Wiegmann, Matthias Neumann, and Volker Schmidt. "Image Based Methodologies, Workflows, and Calculation Approaches for Tortuosity." In Tortuosity and Microstructure Effects in Porous Media, 91–159. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30477-4_4.
Rafael-Patino, Jonathan, Thomas Yu, Victor Delvigne, Muhamed Barakovic, Marco Pizzolato, Gabriel Girard, Derek K. Jones, Erick J. Canales-Rodríguez, and Jean-Philippe Thiran. "DWI Simulation-Assisted Machine Learning Models for Microstructure Estimation." In Computational Diffusion MRI, 125–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52893-5_11.
Sherbondy, Anthony J., Matthew C. Rowe, and Daniel C. Alexander. "MicroTrack: An Algorithm for Concurrent Projectome and Microstructure Estimation." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 183–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15705-9_23.
Munk, Axel, and Johannes Schmidt-Hieber. "Lower bounds for volatility estimation in microstructure noise models." In Institute of Mathematical Statistics Collections, 43–55. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2010. http://dx.doi.org/10.1214/10-imscoll604.
Ghafaryasl, Babak, Bart H. Bijnens, Erwin van Vliet, Fátima Crispi, and Rubén Cárdenes. "Cardiac Microstructure Estimation from Multi-photon Confocal Microscopy Images." In Functional Imaging and Modeling of the Heart, 80–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38899-6_10.
Conference papers on the topic "Estimation des microstructures":
Soboyejo, A. B. O., S. Shademan, V. Sinha, and W. O. Soboyejo. "Statistical Modeling of Microstructural Effects on Fatigue Behavior of α/β Titanium Alloys." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2645.
Khan, Ijaz, Muhammad Mahadi Abdul Jamil, Tengku Nadzlin Tengku Ibrahim, and Faridah Mohd Nor. "Automated human age estimation at death via bone microstructures." In 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2016. http://dx.doi.org/10.1109/iccsce.2016.7893642.
Nishiura, Hiromi, Atsushi Miyamoto, Akira Ito, Shogo Suzuki, Kouhei Fujii, Hiroshi Morifuji, and Hiroyuki Takatsuka. "Machine-learning-based Quality-level-estimation System for Inspecting Steel Microstructures." In 2021 17th International Conference on Machine Vision and Applications (MVA). IEEE, 2021. http://dx.doi.org/10.23919/mva51890.2021.9511374.
Ogbuanu, Kelechi O., and R. Valéry Roy. "A Novel Computational Framework for the Effective Transport Properties of Heterogeneous Materials Reconstructed From Digital Images." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70817.
Schulz, Volker P., Partha P. Mukherjee, and Heiko Andra¨. "Compression Modeling and Transport Characterization of the PEM Fuel Cell Diffusion Medium." In ASME 2011 9th International Conference on Fuel Cell Science, Engineering and Technology collocated with ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/fuelcell2011-54298.
Rajalingam, A., and Shubhankar Chakraborty. "Estimation of hydrodynamic performance of microchannel heat sink with microstructures of various size and shapes - A comprehensive study." In Proceedings of the 26thNational and 4th International ISHMT-ASTFE Heat and Mass Transfer Conference December 17-20, 2021, IIT Madras, Chennai-600036, Tamil Nadu, India. Connecticut: Begellhouse, 2022. http://dx.doi.org/10.1615/ihmtc-2021.1120.
Henrich, Manuel, Michael Dölz, and Sebastian Münstermann. "Development of a Numerical Framework for Microstructure Sensitive Fatigue Life Investigations." In ASME 2023 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/pvp2023-106419.
Aktharuzzaman, Md, Shoaib Anwar, Dmitry Borisov, Jing Rao, and Jiaze He. "2D Numerical Ultrasound Computed Tomography for Elastic Material Properties in Metals." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-90232.
Vaitheeswaran, Pavan Kumar, and Ganesh Subbarayan. "Estimation of Effective Thermal and Mechanical Properties of Particulate Thermal Interface Materials (TIMs) by a Random Network Model." In ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2017 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ipack2017-74129.
Sokolov, Vadim Gennadjevich, Igor Stanislavovich Potemin, Dmitri Dmitrievich Zhdanov, Sergey Georgievich Pozdnyakov, and Alexey Gennadievich Voloboy. "Virtual Prototyping of Measurement Setup for Complex Light Scattering Properties." In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-53-65.
Reports on the topic "Estimation des microstructures":
Ait-Sahalia, Yacine, Per Mykland, and Lan Zhang. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11380.