Bibliografie tematiche / Estimation des microstructures

Letteratura scientifica selezionata sul tema "Estimation des microstructures"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Estimation des microstructures":

Amnuaykijvanit, O., S. Anantawaraskul e T. Rakthanmanon. "Estimation of ethylene/1-butene copolymerization conditions using the autoencoder model". Journal of Physics: Conference Series 2175, n. 1 (1 gennaio 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2175/1/012028.

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Abstract Chain microstructures of ethylene/1-butene copolymers produced at specific polymerization conditions can be calculated using the copolymerization kinetic model. However, this kinetic model cannot be solved inversely to estimate polymerization conditions from desired microstructures. In this work, the autoencoder (AE) model, the machine learning techniques based on artificial neural network (ANN) concept, was developed to help estimate polymerization conditions to produce polymers with desired microstructures (e.g., molecular weight distribution (MWD), chemical composition distribution (CCD), and number and weight average molecular weight). Two models were developed in this work: one with only microstructural distributions (MWD and CCD) and the other with additional information on average microstructures and polymer yield. The results showed that the proposed AE models can adequately estimate polymerization conditions from desired microstructures with acceptable mean square error (MSE). More specific microstructures lead to better estimation of polymerization conditions with lower MSE.

Palevicius, Arvydas, e Giedrius Janusas. "Analysis of Periodical Microstructures Using Optical Methods". Advanced Materials Research 433-440 (gennaio 2012): 2021–28. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2021.

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In this work we present an optical laser based method for characterization of periodical microstructure. It allows indirect evaluation of geometrical and optical parameters of periodical microstructures. Proposed method is applicable for optimization and control of technological processes in the cases when traditional measurement techniques are not suitable. The main experimental results are compared with the computer simulations where the standard programs PCGrate-SX6.0 and and GSolver V4.20b were employed. Error of estimation of geometrical parameters of periodical microstructures by comparing theoretical and experimental values of diffraction efficiencies of periodical microstructures is less than 5%.

Liu, Zaobao, Jianfu Shao, Weiya Xu e 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.

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Estimation of elastic parameters of porous rock like the compressibility of sandstone is scientifically important and yet an open issue. This study illustrates the estimation of the elastic compressibility of sandstone (ECS) based on the assumption that the ECS is determined closely by the mineral composition and microstructures. In this study, 37 samples are collected to evaluate the estimations of the ECS obtained by different methods. The regression analysis is first implemented using the 37 samples. The results show that ECS exhibits linear relations with the rock minerals, pores, and applied compressive stress. Then the support vector machine (SVM) optimized by the particle swarm optimization algorithm (PSO) is examined to generate estimations of the ECS based on the mineral composition and microstructures. The SVM is trained with 30 samples to search for optimal parameters using the PSO, and thus the estimation model is established. Afterwards, this model is validated to give predictions of the left 7 samples. By comparison with the regression methods, the proposed strategy, that is, the PSO optimized SVM, performs much better on the training samples and shows a good capability in generating estimations of the ECS of the 7 testing samples based on the mineral composition and microstructures.

Blanc, Rémi, Pierre Baylou, Christian Germain e 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, n. 3 (30 marzo 2010): 273–81. http://dx.doi.org/10.1017/s1431927610000139.

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AbstractWe propose an image-based framework to evaluate the uncertainty in the estimation of the volume fraction of specific microstructures based on the observation of a single section. These microstructures consist of cubes organized on a cubic mesh, such as monocrystalline nickel base superalloys. The framework is twofold: a model-based stereological analysis allows relating two-dimensional image observations to three-dimensional microstructure features, and a spatial statistical analysis allows computing approximate confidence bounds while assessing the representativeness of the image. The reliability of the method is assessed on synthetic models. Volume fraction estimation variances and approximate confidence intervals are computed on real superalloy images in the context of material characterization.

Ben Ahmed, Amal, Ahmad Bahloul, Mohamed Iben Houria, Anouar Nasr e 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, n. 9 (16 agosto 2018): 1830–42. http://dx.doi.org/10.1177/1464420718792024.

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The Al–Si–Mg high-cycle fatigue behavior is mainly affected by the microstructural heterogeneities and the presence of casting defects. This attempt aims to develop an analytical approach based on the evaluation of the highly stressed volume caused by local porosities and defined as the affected area methodology. The proposed approach is able to predict the aluminum alloy fatigue response by considering the effect of microstructure described by the secondary dendrite arm spacing and its correlation with the defect size effect. A representative elementary volume model is implemented to evaluate the stress distribution in the vicinity of the defect and to determine its impact on the high-cycle fatigue resistance. Work hardening due to cyclic loading is considered by applying the Lemaitre–Chaboche model. The Kitagawa–Takahashi diagrams corresponding to different microstructures and for two loading ratios: R σ = 0 and R σ = −1 were simulated based on the AA method. Simulations were compared to the experimental results carried out on cast aluminium alloy A356 with T6 post heat-treatment. The results show clearly that the proposed approach provides a good estimation of the A356-T6 fatigue limit and exhibits good ability in simulating the Kitagawa–Takahashi diagrams for fine and coarse microstructures.

McNelley, Terry R., Keiichiro Oh-ishi e Alexandre P. Zhilyaev. "Microstructure Evolution and Microstructure-Property Relationships in Friction Stir Processing of NiAl Bronze". Materials Science Forum 539-543 (marzo 2007): 3745–50. http://dx.doi.org/10.4028/www.scientific.net/msf.539-543.3745.

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Friction stir processing (FSP) has been employed for localized modification and control of microstructures in NiAl bronze materials, which are widely utilized for marine components. The thermomechanical cycle of FSP results in homogenization and refinement and the conversion of microstructures from a cast to a wrought condition within stir zones in the material. However, the direct measurement of stir zone temperatures, strains, strain rates and cooling rates is difficult due to steep gradients and transients in these quantities, and this is an impediment in the assessment of FSP-induced microstructures and properties. Quantitative microstructure analyses following FSP of cast NiAl bronze materials have been used to develop estimates of stir zone thermomechanical cycles. The estimation procedures will be reviewed and the microstructure-based estimates will be compared to results from computational models and embedded thermocouples measurements. Stir zone microstructures comprise a mixture of primary α grains and transformation products of the β that formed during processing. Recrystallization in the primary α occurred due to particle-stimulated nucleation in this low stacking fault energy material. Factors that influence the distribution of strength and ductility in the stir zone appear to include the mixture of microstructure constituents and gradients in microstructure due to gradients in processing conditions.

Liu, Lishuai, Peng Wu, Yanxun Xiang e 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, n. 3 (settembre 2022): 1913–21. http://dx.doi.org/10.1121/10.0014289.

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Characterization of grain microstructures of metallic materials is crucial to materials science and engineering applications. Unfortunately, the universal electron microscopic methodologies can only capture two-dimensional local observations of the microstructures in a time-consuming destructive way. In this regard, the nonlinear ultrasonic technique shows the potential for efficient and nondestructive microstructure characterization due to its high sensitivity to microstructural features of materials, but is hindered by the ill-posed inverse problem for multiparameter estimation induced by the incomplete understanding of the complicated nonlinear mechanical interaction mechanism. We propose an explainable nonlinearity-aware multilevel wavelet decomposition-multichannel one-dimensional convolutional neural network to hierarchically extracts multilevel time-frequency features of the acoustic nonlinearity and automatically model latent nonlinear dynamics directly from the nonlinear ultrasonic responses. The results demonstrate that the proposed approach establishes the complex mapping between acoustic nonlinearity and microstructural features, thereby determining the lognormal distribution of grain size in metallic materials rather than only average grain size. In the meantime, the integration of the designed nonlinearity-aware network and the quantitative analysis of component importance provides an acceptable physical explainability of the deep learning approach for the nonlinear ultrasonic technique. Our study shows the promise of this technique for real-time in situ evaluation of microstructural evolution in various applications.

Kawa, Marek. "Failure Criterion for Brick Masonry: A Micro-Mechanics Approach". Studia Geotechnica et Mechanica 36, n. 3 (28 febbraio 2015): 37–48. http://dx.doi.org/10.2478/sgem-2014-0025.

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Abstract The paper deals with the formulation of failure criterion for an in-plane loaded masonry. Using micro-mechanics approach the strength estimation for masonry microstructure with constituents obeying the Drucker-Prager criterion is determined numerically. The procedure invokes lower bound analysis: for assumed stress fields constructed within masonry periodic cell critical load is obtained as a solution of constrained optimization problem. The analysis is carried out for many different loading conditions at different orientations of bed joints. The performance of the approach is verified against solutions obtained for corresponding layered and block microstructures, which provides the upper and lower strength bounds for masonry microstructure, respectively. Subsequently, a phenomenological anisotropic strength criterion for masonry microstructure is proposed. The criterion has a form of conjunction of Jaeger critical plane condition and Tsai-Wu criterion. The model proposed is identified based on the fitting of numerical results obtained from the microstructural analysis. Identified criterion is then verified against results obtained for different loading orientations. It appears that strength of masonry microstructure can be satisfactorily described by the criterion proposed.

Yuan, Jianhui, Yu Pan e 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.

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Liquidity has always been a hot spot for researchers of financial market microstructures. Analysis of liquidity is of great significance for investors and market regulators. Ultrahigh frequency data records the whole dynamic change of the trading process, so it has advantages in depicting the market microstructure. This study analyzes Asian emerging market equities liquidity using ultrahigh frequency data. We used various forms of WACD models and let trading duration be indicators of liquidity. Through the residual test, we were able to select the best model to describe the overall liquidity.

Schouwenaars, Rafael, Víctor H. Jacobo, Sara M. Cerrud e Armando Ortiz. "Finite Element Simulation of Microstresses in a Traditional FGM: The Case of Soft Tribo-Alloys". Materials Science Forum 492-493 (agosto 2005): 421–28. http://dx.doi.org/10.4028/www.scientific.net/msf.492-493.421.

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Journal bearings are graded systems with a metal-metal composite as the functional layer. Estimation of the microscale stress distributions is used to analyse the interaction between microstructure, material properties and damage mechanisms during wear. The analysis is executed by means of simple plane-strain finite element models mimicking experimentally observed microstructures. It is found that under realistic macrosstress conditions no tensile microstresses are induced in the triboalloy and that plastic flow is inhibited by the graded structure.

Più fonti

Tesi sul tema "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.

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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.

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L'imagerie par résonance magnétique de diffusion (IRM de diffusion) est une modalité d'imagerie non invasive couramment utilisée pour mesurer les propriétés micro-structurales des tissus biologiques au dessous de la résolution spatiale, en mesurant indirectement le déplacement de diffusion des molécules d'eau. En raison de la complexité géométrique du cerveau et du mécanisme complexe de l'IRM de diffusion, il est difficile de relier directement les signaux reçus à des paramètres biophysiques significatifs, tels que le diamètre des axones ou la densité. Ces dernières années, plusieurs modèles biophysiques ont été introduits pour répondre à ce problème de la faible interprétabilité. Ces modèles représentent les signaux d'IRM de diffusion comme un mélange de signaux analytiques sous certaines hypothèses, par exemple des membranes imperméables, de différentes géométries simples et non connectées, par exemple des sphères et des bâtonnets. Par la suite, ils visent à extraire les paramètres de ces géométries simples, qui sont corrélés avec des paramètres biophysiques, en inversant la formulation analytique. Cependant, la validité de ces hypothèses reste indéterminée dans les expériences réelles. L'objectif de cette thèse est d'améliorer la fiabilité et l'efficacité de l'estimation de la microstructure par deux moyens. Tout d'abord, pour faciliter l'étude quantitative de la domaine de validité des modèles biophysiques et de l'effet de la déformation géométrique et de la perméabilité de la membrane cellulaire par simulation, nous avons proposé deux modèles réduits dérivés de l'équation de Bloch-Torrey, respectivement. Dans le cas de membranes perméables, une nouvelle approche de simulation utilisant une base propre de Laplace imperméable est proposée. Quant à la déformation géométrique, nous utilisons une expansion asymptotique par rapport aux angles de déformation pour approximer le signal. Ces deux modèles réduits permettent de faire les calculs efficaces des signaux pour diverses valeurs de déformation/perméabilité. Des simulations numériques montrent que ces deux modèles peuvent rapidement calculer les signaux avec un niveau d'erreur raisonnable par rapport aux méthodes existantes. Plusieurs études ont été menées sur les effets de la perméabilité et de la déformation sur les signaux ou sur le coefficient de diffusion efficace (ADC en anglais), en utilisant les modèles proposés. Deuxièmement, au lieu d'inverser un modèle de géométries simplifiées, nous présentons une nouvelle approche pour associer la taille des somas dans la matière grise par des biomarqueurs intermédiaires. Des simulations numériques identifient une corrélation entre le diamètre/densité des somas et le point d'inflexion des signaux moyennés sur la direction à des amplitudes élevées (b>2500s/mm^2), offrant des perspectives pour l'estimation de la microstructure. Nous adaptons un réseau neuronal entièrement connecté en utilisant ces biomarqueurs et comparé aux modèles biophysiques, cette approche offre des résultats comparables sur les données synthétiques et in vivo et une estimation rapide car aucune inversion n'est impliquée
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.

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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.

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Accurate measurement of asset return volatility and correlation is an important problem in financial econometrics. The presence of market microstructure noise in high-frequency data complicates such estimations. This study extends a prior application of a model-based volatility estimator with autocorrelated market microstructure noise to estimation of correlation. The model is applied to a high-frequency dataset including a stock and an index, and the results are compared to some existing models. This study supports previous findings that including an autocorrelation factor produces an estimator potentially less vulnerable to market microstructure noise, and finds that the same is true about the extended correlation estimator that is introduced here.

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.

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L'estimation non invasive de la microstructure du cerveau, qui se compose de nombreux neurites, de somas et de cellules gliales, est essentielle pour l'imagerie cérébrale. L'IRM de diffusion (IRMd) est une technique prometteuse pour sonder les propriétés microstructurelles du cerveau en dessous de la résolution spatiale des scanners IRM. En raison de la complexité structurelle du tissu cérébral et du mécanisme complexe de l'IRM de diffusion, l'estimation de la microstructure in vivo est un défi. Les méthodes existantes utilisent généralement des géométries simplifiées, notamment des sphères et des bâtons, pour modéliser les structures neuronales et obtenir des expressions analytiques des signaux intracellulaires. La validité des hypothèses faites par ces méthodes reste indéterminée. Cette thèse vise à faciliter l'estimation de la microstructure du cerveau par simulation en remplaçant les géométries simplifiées par des modèles réalistes de la géométrie des neurones et les expressions analytiques des signaux intracellulaires par des simulations d'IRM de diffusion. Combinées à des modèles précis de la géométrie des neurones, les simulations numériques d'IRMd peuvent donner des signaux intracellulaires précis et incorporer les effets dus, par exemple, à l'ondulation des neurites ou à l'échange d'eau entre le soma et les neurites.Malgré ces avantages, les simulations d'IRMd n'ont pas été largement adoptées en raison de l'inaccessibilité des fantômes numériques, de la faible efficacité de calcul des simulateurs d'IRMd et de la difficulté d'approximer les mappings implicites entre les signaux d'IRMd et les propriétés de la microstructure. Cette thèse contribue à la résolution des problèmes susmentionnés de la manière suivante : (1) en développant un générateur de maillage de neurones open-source et en rendant accessibles au public plus d'un millier de maillages cellulaires réalistes ; (2) en augmentant d'un facteur dix l'efficacité de calcul de la méthode du formalisme matriciel numérique ; (3) en mettant en œuvre une nouvelle méthode de simulation qui fournit une représentation de type Fourier des signaux IRMd ; (4) en proposant un cadre d'apprentissage supervisé basé sur la simulation pour estimer la microstructure du cerveau par IRM de diffusion
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.

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L'objectif de ce travail de thèse est une étude quantitive des differents problèmes mathematiques qui apparaissent en trading algorithmique. Concrètement, on propose une approche scientifique pour optimiser des processus relatifs a la capture et provision de liquidités pour des marchés electroniques.Du au fort caractère appliqué de ce travail, on n'est pas seulement intéressés par la rigeur mathématique de nos résultats, mais on souhaite aussi a comprendre ce travail de recherche dans le contexte des differentes étapes qui font partie de l'implementation pratique des outils que l'on developpe; par exemple l'interpretation du modèle, l'estimation de parametres, l'implementation informatique etc.Du point de vue scientifique, le coeur de notre travail est fondé sur deux techniques empruntées au monde de l'optimisation et des probabilités, celles sont : le contrôle stochastique et l'approximation stochastique.En particulier, on présente des resultats academiques originaux pour le probleme de market-making haute fréquence et le problème de liquidation de portefeuille en utilisant des limit-orders; dans le deux cas on utilise une approche d'optimisation dite backwards. De la même façon, on résout le problème de market-making en utilisant une approche "forward", ceci étant innovateur dans la litterature du trading optimal car il ouvre la porte à des techniques d'apprentissage automatique.Du pont de vue pratique, cette thèse cherches à creer un point entre la recherche academique et l'industrie financière. Nos resultats sont constamment considérés dans la perspective de leur implementation pratique. Ainsi, on concentre une grande partie de notre travail a étudier les differents facteurs qui sont importants a comprendre quand on transforme nos techniques quantitatives en valeur industrielle: comprendre la microstructure des marchés, des faits stylisés, traitrement des données, discussions sur les modèles, limitations de notre cadre scientifique etc
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.

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Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the quadratic variation of the continuous part in the price process. This estimator is obtained by truncating the two-scales realized variance estimator. We show its consistency in the presence of market microstructure noise and finite or infinite activity jumps in the price process. The third chapter employs this estimator to design a test to explore the existence of price jumps with noisy price data.
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], e 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.

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[Verfasser], Sophon Tunyavetchakit, e 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.

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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.

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Prévoir le comportement de matéiaux hétérogènes aléatoires en fonction de la répartition spatiale des constituants reste un problème largement ouvert, dont on expose les enjeux, le formalisme et les diverses approches micromécaniques associées. Une étude expérimentale sur des matériaux biphasés fer/argent et fer/cuivre de morphologie "matrice/inclusions" ou à phases co-continues montre que l'influence des paramètres morphologiques est surtout sensible à l'échelle locale et concerne notamment les hétérogénéités de déformation, caractérisées en termes de moyennes par phase et de fonctions de distribution, mesurées grâce à une technique originale de microextensométrie. Les modèles classiques ne rendant pas compte des phénomènes observés, une nouvelle approche fondée sur la notion de "motif morphologique représentatif" est proposée. Un premier modèle à motifs multicouches admet une expression semi-analytique mais s'avère toujours insuffisant. Une description morphologique plus riche obtenue avec des motifs de structure interne quelconque en distribution ellipsoïdale conduit à des encadrements rigoureux et des estimations autocohérentes du comportement linéaire effectif. La théorie sous-jacente est exposée en détail, la signification physique véritable de ces modèles étant précisée. La mise en œuvre effective par des outils numériques conduit par exemple à de nouveaux résultats pour des composites anisotropes à renforts particulaires, dont on décrit ainsi correctement les interactions locales. La prise en compte de certains phénomènes de corrélation à grande distance observés expérimentalement dans le domaine non linéaire reste en revanche une question ouverte.

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Libri sul tema "Estimation des microstructures":

Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, MA: National Bureau of Economic Research, 2005.

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Deshpande, U. P., T. Shripathi e A. V. Narlikar. Iron-oxide nanostructures with emphasis on nanowires. A cura di A. V. Narlikar e Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533053.013.23.

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This article examines the properties of iron-oxide nanostructures, with particular emphasis on nanowires. It begins with an overview of iron-oxide nanostructures and nanowires, followed by a discussion of the synthesis of aligned ?-Fe2O3 nanowires and nanosheets by a simple thermal oxidation route. It then describes the preferential bending of [110] grown ?-Fe2O3 nanowires about the C-axis and quantitative estimation of nanowire alignment using X-ray diffraction and grazing incidence X-ray diffraction. It also considers the growth mechanism of ?-Fe2O3 nanowires and nanosheets, different nanowire morphologies, rotational slip in ?-Fe2O3 nanosheets, and the influence of local environment and substrate microstructure on nanowire growth.

Capitoli di libri sul tema "Estimation des microstructures":

Drezet, J. M., G. U. Grün e 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.

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Khan, I., F. Mohd Nor e 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.

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Jallais, Maëliss, e 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.

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AbstractThis chapter covers anisotropy in the context of probing microstructure of the human brain using single encoded diffusion MRI. We will start by illustrating how diffusion MRI is a perfectly adapted technique to measure anisotropy in the human brain using water motion, followed by a biological presentation of human brain. The non-invasive imaging technique based on water motions known as diffusion MRI will be further presented, along with the difficulties that come with it. Within this context, we will first review and discuss methods based on signal representation that enable us to get an insight into microstructure anisotropy. We will then outline methods based on modeling, which are state-of-the-art methods to get parameter estimations of the human brain tissue.

Rošt’áková, Zuzana, Georg Dorffner, Önder Aydemir e 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.

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Li, Yuxing, Yu Qin, Zhiwen Liu e 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.

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Holzer, Lorenz, Philip Marmet, Mathias Fingerle, Andreas Wiegmann, Matthias Neumann e 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.

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AbstractIn this chapter, modern methodologies for characterization of tortuosity are thoroughly reviewed. Thereby, 3D microstructure data is considered as the most relevant basis for characterization of all three tortuosity categories, i.e., direct geometric, indirect physics-based and mixed tortuosities. The workflows for tortuosity characterization consists of the following methodological steps, which are discussed in great detail: (a) 3D imaging (X-ray tomography, FIB-SEM tomography and serial sectioning, Electron tomography and atom probe tomography), (b) qualitative image processing (3D reconstruction, filtering, segmentation) and (c) quantitative image processing (e.g., morphological analysis for determination of direct geometric tortuosity). (d) Numerical simulations are used for the estimation of effective transport properties and associated indirect physics-based tortuosities. Mixed tortuosities are determined by geometrical analysis of flow fields from numerical transport simulation. (e) Microstructure simulation by means of stochastic geometry or discrete element modeling enables the efficient creation of numerous virtual 3D microstructure models, which can be used for parametric studies of micro–macro relationships (e.g., in context with digital materials design or with digital rock physics). For each of these methodologies, the underlying principles as well as the current trends in technical evolution and associated applications are reviewed. In addition, a list with 75 software packages is presented, and the corresponding options for image processing, numerical simulation and stochastic modeling are discussed. Overall, the information provided in this chapter shall help the reader to find suitable methodologies and tools that are necessary for efficient and reliable characterization of specific tortuosity types.

Rafael-Patino, Jonathan, Thomas Yu, Victor Delvigne, Muhamed Barakovic, Marco Pizzolato, Gabriel Girard, Derek K. Jones, Erick J. Canales-Rodríguez e 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.

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Sherbondy, Anthony J., Matthew C. Rowe e 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.

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Munk, Axel, e 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.

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Ghafaryasl, Babak, Bart H. Bijnens, Erwin van Vliet, Fátima Crispi e 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.

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Atti di convegni sul tema "Estimation des microstructures":

Soboyejo, A. B. O., S. Shademan, V. Sinha e 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.

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Abstract This paper presents the results of combined experimental and theoretical studies of the effects of colony microstructure on fatigue crack growth in Ti-6Al-4V. Colony microstructures with controlled lath widths and colony sizes are produced by controlled cooling from the β phase field. For colony microstructures with approximately 20 vol% of β phase, the fatigue crack growth rates are shown to decrease with increasing α lath and colony size. A new statistical multiparameter modeling methodology framework is proposed for the assessment of the combined effects of mechanical and microstructural random variables on the fatigue crack growth rate. Excellent statistical correlation has been observed between the theoretical model and experimental data. The implications of the results are also discussed for the estimation of fatigue life.

Khan, Ijaz, Muhammad Mahadi Abdul Jamil, Tengku Nadzlin Tengku Ibrahim e 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.

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Nishiura, Hiromi, Atsushi Miyamoto, Akira Ito, Shogo Suzuki, Kouhei Fujii, Hiroshi Morifuji e 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.

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Ogbuanu, Kelechi O., e 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.

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Abstract In computational material science, Digital Image Processing and Big Data analysis play a crucial role in Microstructure Characterization and Reconstruction (MCR), especially in the estimation of structure-property relationships. In this work, we are interested in the calculation of the effective transport properties of composite materials from digital images. Because most MCR techniques are heavily statistical, they may suffer from significant microstructure information loss as they are incapable of reproducing exact images of microstructures. Here, we take advantage of pattern recognition algorithms to extract nearly exact morphological information pertaining to the interphase boundaries from digital microstructural images, thereby minimizing information loss. The data extracted then serves as the basis for our effective transport property module for calculating the effective properties of two-phase composite materials in a way that is automated, fast, stable, memory efficient, and accurate. Our current formulation is limited to circular or near-circular inclusions, with very large contrast properties. Preliminary numerical tests on four cases of 2D, two-phase microstructure images yielded relative errors ranging from 0.1% to 2.0%, for image pixel density around 1000 × 1000 pixels. These relative errors are perfectly acceptable without having to resort to unrealistic image sizes.

Schulz, Volker P., Partha P. Mukherjee e 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.

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Despite tremendous progress in recent years, a pivotal performance limitation in PEM fuel cells manifests in terms of mass transport loss owing to liquid water transport and resulting flooding. A key contributor to the mass transport loss is the cathode gas diffusion layer (GDL) due to the blockage of available pore space by liquid water thus rendering hindered oxygen transport to the active reaction sites in the electrode. The GDL, typically a non-woven carbon paper or woven carbon cloth, thus plays an important role in the overall water management in PEM fuel cells. The underlying pore-morphology and the pore wetting characteristics have significant influence on the flooding dynamics in the GDL. Another important factor is the role of cell compression on the GDL microstructural change. In this work, we investigate the influence of GDL microstructure change under compression on the transport behavior. We will present an improved compression model based on the micro finite element approach. The compression of reconstructed GDL microstructures along with effective property estimation and transport characterization are elucidated.

Rajalingam, A., e 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.

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Henrich, Manuel, Michael Dölz e 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.

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Abstract Modern steel grades with increasingly complex microstructure morphologies demand advanced modeling methods. Especially in terms of geometrical microstructure modeling these steels tend to be very challenging. In this study a framework is presented that tackles the modelling of modern microstructures using a new generator for representative volume elements called DRAGen (Discrete Rve Automation and Generation), and a strategy for the numerical investigation of the influence of the surface roughness and residual stresses on the fatigue life and endurance is proposed. The proposed framework consists mainly of three parts: microstructural characterization, generation of statistical representative volume elements and the investigation of micromechanical behavior under extrinsic loading conditions. The microstructural characterization is supported by neural networks while the generation algorithms of the representative volume elements uses the information generated by those neural networks. The mechanical behavior of these representative volume elements is simulated with Abaqus. Due to the usage of a neural network DRAGen can produce very realistic microstructure models following the statistical behavior of the real world material with a high level of detail. It is also possible to introduce features such as surface roughness and residual stresses into the simulations of DRAGen’s representative volume elements. Therefore, the fatigue life estimations performed with these representative volume elements enable a better understanding of the influences that single microstructural features have on fatigue life. This understanding will help to generate a good basis for numerical optimization of the structural integrity of safety-related components.

Aktharuzzaman, Md, Shoaib Anwar, Dmitry Borisov, Jing Rao e 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.

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Abstract Adequate knowledge of the materials through characterization during the development, production, and processing of the material is required for quality assurance and in-service safety. Material characterization involves the evaluation of properties such as elastic coefficients, material microstructures, morphological features, and associated mechanical properties. Ultrasonic signals are sensitive to useful acoustic properties, including wave speeds, attenuation, diffusion backscattering, variations in microstructure, and elastic properties (e.g., elastic modulus, hardness, etc.). To obtain a quantitative estimation of the material properties, an emerging imaging technique known as ultrasound computed tomography (USCT) can be utilized. This paper proposes to map the wave speeds (i.e., longitudinal and shear) inside elastic parts employing a wave-based methodology (known as full waveform inversion (FWI)) for USCT. FWI is a partial differential equation-constraint, nonlinear optimization technique. It is based on full wavefield modeling and inversion to extract material parameter distribution using wave equations. FWI consequently produces high-resolution images by iteratively determining and minimizing a waveform residual, which is the difference between the modeled and the observed signals. The performance of FWI based ultrasound tomography in material property reconstruction in numerical studies has been presented. The results show its application potential in nondestructive material characterization.

Vaitheeswaran, Pavan Kumar, e 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.

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Particulate thermal interface materials (TIMs) are commonly used to transport heat from chip to heat sink. While high thermal conductance is achieved by large volume loadings of highly conducting particles in a compliant matrix, small volume loadings of stiff particles will ensure reduced thermal stresses in the brittle silicon device. Developing numerical models to estimate effective thermal and mechanical properties of TIM systems would help optimize TIM performance with respect to these conflicting requirements. Classical models, often based on single particle solutions or regular arrangement of particles, are insufficient as real-life TIM systems contain a distriubtion of particles at high volume fractions, where classical models are invalid. In our earlier work, a computationally efficient random network model was developed to estimate the effective thermal conductivity of TIM systems [1,2]. This model is extended in this paper to estimate the effective elastic modulus of TIMs. Realistic microstructures are simulated and analyzed using the proposed method. Factors affecting the modulus (volume fraction and particle size distribution) are also studied.

Sokolov, Vadim Gennadjevich, Igor Stanislavovich Potemin, Dmitri Dmitrievich Zhdanov, Sergey Georgievich Pozdnyakov e 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.

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The objects with complex light scattering, for example, light guiding plates with surface and volume microstructures are widely used in modern optical devices and computer graphics. Typically, such properties are described with Bi-Directional Scattering Distribution Function (BSDF) which totally describes the dependence of scattering properties on illumination and observation conditions. The precise BSDF measurements require complex setups with high resolution and launching numerous measurements in the entire angular space. The virtual prototyping of BSDF measurement devices is a difficult and problematic task because of the very small efficiency of similar systems. In the given paper the optimal and more effective ways to simulate such devices are presented based on example of patented scheme for portable BSDF measurements setup, including tolerance calculation and accuracy estimation.

Rapporti di organizzazioni sul tema "Estimation des microstructures":

Ait-Sahalia, Yacine, Per Mykland e Lan Zhang. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Cambridge, MA: National Bureau of Economic Research, maggio 2005. http://dx.doi.org/10.3386/w11380.

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