![]() Velocity field is negligible but the absolute location cannot be accurately recovered usingĪccurate characterization of the underground energy resources is crucial in rigorous prediction of their future behavior. Provided the calibration drift is small, as generally observed in PIV, the bias on the estimated Work is to show that volume self-calibration induces a change in the world frame coordinates. These methods are successfullyĪpplied to the tomographic PIV of an air jet experiment. Optimization of the extrinsic parameters of the pinhole model. We propose an original self-calibration method based on global Illustrate how the pinhole framework can be used to provide a quantitative evaluation of Tomo-PIV, we confirm, through a simple experiment, that they are not stable in time, and While the resulting calibrations are accurate enough for It is then used in a calibration procedure based onĪ freely moving calibration plate. ![]() A complete and explicit pinhole model of a camera equipped with a 2-tiltĪngles Scheimpflug adapter is presented. We address calibration and self-calibration of tomographic PIV experiments within a pinhole It is evaluated on standard datasets where it proves to significantly outperform other similar state of the art implementations, without sacrificing generality or accuracy in any way. The proposed solution is integrated in SLAM++, a nonlinear least squares solver focused on robotics and computer vision. Highly efficient algorithms for both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are provided. These operations can be used to construct both direct and iterative solvers as well as to compute eigenvalues. The solution proposed in this thesis covers a broad range of functions: it includes efficient sparse block matrix assembly, matrix-vector and matrix-matrix products as well as triangular solving and Cholesky factorization. ![]() Most of the existing sparse block matrix implementations focus only on a single operation, such as the matrix-vector product. Some of the more specialized solvers in robotics and computer vision use sparse block matrices internally to reduce sparse matrix assembly costs, but finally end up converting such representation to an elementwise sparse matrix for the linear solver. This is perhaps due to the complexity of sparse block formats which reduces computational efficiency, unless the blocks are very large. The majority of the existing state of the art sparse linear algebra implementations use elementwise sparse matrices and only a small fraction of them support sparse block matrices. Sparse block matrices also occur when solving Finite Element Methods (FEMs) or Partial Differential Equations (PDEs) in physics simulations. Simultaneous Localization and Mapping (SLAM) in robotics, Bundle Adjustment (BA) or Structure from Motion (SfM) in computer vision. Sparse block matrices occur naturally in many key problems, such as Nonlinear LEast Squares (NLS) on graphical models. This thesis focuses on data structures for sparse block matrices and the associated algorithms for performing linear algebra operations that I have developed. It was concluded that the utilization of ANN with SFS and NID drastically improved the accuracy of the model, and provided valuable insights on the ANN compressive strength predictions for different UHPC mixes. Finally, a nonlinear regression model based on the four selected material constituents was developed and a parametric study was conducted. ![]() These material constituents were then employed into the ANN to compute more accurate predictions (r 2 = 80.1% and NMSE = 0.012) than the model with all eight material constituents (r 2 = 21.5% and NMSE = 0.035). As a result, four material constituents were selected mainly, cement, fly ash, silica fume and water. 110 UHPC compressive strength tests varying based on the material quantities were compiled into a database to train the ANN. This paper attempts to address this ambiguity by employing two deep machine learning techniques-Sequential Feature Selection (SFS) and Neural Interpretation Diagram (NID)-to identify the critical material constituents that affect the ANN. However, its black-box nature prevents researchers from mathematically describing its contents. Empirically capturing this relationship often requires the utilization of intelligent algorithms, such as the Artificial Neural Network (ANN), to derive a predictive model that fits into an experimental dataset. The compressive strength of Ultra-High Performance Concrete (UHPC) is a function of the type, property and quantities of its material constituents. ![]()
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