A Two-stage Classification Method for High-dimensional Data and Point Clouds

High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.Comment: 21 pages, 4 figure

Study on microstructure mechanism of sandstone based on complex network theory

Rock contains a large number of micro-pores, which are of different shapes and complex structures. The structure information of sandstones is extracted based on different porosities through X-ray CT (Computer Tomography) scanning, photo processing techniques and complex network method to explore the topological structure of sandstone seepage network. The results show that sandstone seepage network has scale-free property. The minute quantities of pores with more throat connections have vital functions of overall connectivity of sandstone seepage network, while sandstone seepage network has strong robustness with random error. This research can provide reference for across scales research of porous seepage and multi-disciplinary application of complex network theory

Electron Tomography Analysis of Thylakoid Assembly and Fission in Chloroplasts of a Single-Cell C4 plant, Bienertia sinuspersici

Bienertia sinuspersici is a single-cell C4 plant species of which chlorenchyma cells have two distinct groups of chloroplasts spatially segregated in the cytoplasm. The central vacuole encloses most chloroplasts at the cell center and confines the rest of the chloroplasts near the plasma membrane. Young chlorenchyma cells, however, do not have large vacuoles and their chloroplasts are homogenous. Therefore, maturing Bienertia chlorenchyma cells provide a unique opportunity to investigate chloroplast proliferation in the central cluster and the remodeling of chloroplasts that have been displaced by the vacuole to the cell periphery. Chloroplast numbers and sizes increased, more notably, during later stages of maturation than the early stages. Electron tomography analyses indicated that chloroplast enlargement is sustained by thylakoid growth and that invaginations from the inner envelope membrane contributed to thylakoid assembly. Grana stacks acquired more layers, differentiating them from stroma thylakoids as central chloroplasts matured. In peripheral chloroplasts, however, grana stacks stretched out to a degree that the distinction between grana stacks and stroma thylakoids was obscured. In central chloroplasts undergoing division, thylakoids inside the cleavage furrow were kinked and severed. Grana stacks in the division zone were disrupted, and large complexes in their membranes were dislocated, suggesting the existence of a thylakoid fission machinery.11Ysciescopu

Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science

Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final reconstruction. To demonstrate the versatility of the technique, a comprehensive list of applications is also summarized, ranging from batteries to shale rocks and even some types of soft materials. Moreover, the continuous technological development, such as the introduction of the latest models of plasma and cryo-FIB, can open the way towards the analysis with this technique of a large class of soft materials, while the introduction of new machine learning and deep learning systems will not only improve the resolution and the quality of the final data, but also expand the degree of automation and efficiency in the dataset handling. These future developments, combined with a technique that is already reliable and widely used in various fields of research, are certain to become a routine tool in electron microscopy and material characterization

Non-Destructive Evaluation for Composite Material

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify the structural defects in composite materials. The research focused on developing an autonomous Non-Destructive Evaluation system which detects, identifies, and characterizes crack and delamination in composite materials from computed tomography (CT scans) images. The identification and visualization of cracking and delamination will allow researchers to use volumetric models to better understand the propagation of damage in materials, leading to design optimizations that will prevent catastrophic failure

Radial Basis Functions: Biomedical Applications and Parallelization

Radial basis function (RBF) is a real-valued function whose values depend only on the distances between an interpolation point and a set of user-specified points called centers. RBF interpolation is one of the primary methods to reconstruct functions from multi-dimensional scattered data. Its abilities to generalize arbitrary space dimensions and to provide spectral accuracy have made it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc. The present thesis discusses three applications of RBF interpolation in biomedical engineering areas: (1) Calcium dynamics modeling, in which we numerically solve a set of PDEs by using meshless numerical methods and RBF-based interpolation techniques; (2) Image restoration and transformation, where an image is restored from its triangular mesh representation or transformed under translation, rotation, and scaling, etc. from its original form; (3) Porous structure design, in which the RBF interpolation used to reconstruct a 3D volume containing porous structures from a set of regularly or randomly placed points inside a user-provided surface shape. All these three applications have been investigated and their effectiveness has been supported with numerous experimental results. In particular, we innovatively utilize anisotropic distance metrics to define the distance in RBF interpolation and apply them to the aforementioned second and third applications, which show significant improvement in preserving image features or capturing connected porous structures over the isotropic distance-based RBF method. Beside the algorithm designs and their applications in biomedical areas, we also explore several common parallelization techniques (including OpenMP and CUDA-based GPU programming) to accelerate the performance of the present algorithms. In particular, we analyze how parallel programming can help RBF interpolation to speed up the meshless PDE solver as well as image processing. While RBF has been widely used in various science and engineering fields, the current thesis is expected to trigger some more interest from computational scientists or students into this fast-growing area and specifically apply these techniques to biomedical problems such as the ones investigated in the present work

Innovation Of Petrophysical And Geomechanical Experiment Methodologies: The Application Of 3D Printing Technology

The petrophysical and geomechanical properties of rocks link the geology origin with engineering practice, which serves as the fundamental of various disciplinaries associated with subsurface porous media, including civil engineering, underground water, geological exploration, and petroleum engineering. The research methodologies can be mainly divided into three aspects: theoretical modelling, numerical simulation, and experiments, in which the last approach plays a critical role that can support, validate, calibrate, or even refute a hypothesis. Only replying on repeatable trials and consolidate analysis of precise results can the experiments be successful and convincing, though uncertainties, due to multiple factors, need to be scrutinized and controlled. The challenges also existed in the characterization and measurements of rock properties as a result of heterogeneity and anisotropy as well as the inevitable impact of experimental operation. 3D printing, a cutting-edge technology, was introduced and utilized in the study that is supposed to be capable of controlling the mineralogy, microstructure, physical properties of physical rock replicas and further benefit the petrophysical and geomechanical experimental methodologies. My PhD research project attempted to answer the questions from the standpoint of petrophysicisits and geomechanics scientist: Can 3D printed rocks replicate natural rocks in terms of microstructure, petrophysical and geomechanical properties? If not, by any means can we improve the quality of replicas to mimic the common rock types? Which 3D printing method is best suitable for our research purposes? How could it be applied in the conventional experiments and integrated with theoretical calculation or numerical simulation? Three main types of printing materials and techniques (gypsum, silica sand, resin) were characterized first individually, which demonstrated varying microstructure, anisotropy, petrophysical and geomechanical properties. Post-processing effect was examined on the 3D printed gypsum rocks that show impact differences on nanoscale and microscale pore structures. Through comparison, resin, the material used in stereolithography technology, best suits the reconstruction of intricate pore network that aims to complement digital rock physics and ultimately be applied in petrophysical research. Gypsum material, however, has been proved as the best candidate for geomechanical research spanning from reference samples to upscaling methods validation. Currently, a practical approach of utilizing 3D printing in petroleum geoscience is taking advantages of the characteristics we focus on the research while disregarding the other properties, by which a suitable 3D printing material and technique can emerge

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer