Linear regression with Laplace measurement error

Master of ScienceStatisticsWeixing SongIn this report, an improved estimation procedure for the regression parameter in simple linear regression models with the Laplace measurement error is proposed. The estimation procedure is made feasible by a Tweedie type equality established for E(X|Z), where Z = X + U, X and U are independent, and U follows a Laplace distribution. When the density function of X is unknown, a kernel estimator for E(X|Z) is constructed in the estimation procedure. A leave-one-out cross validation bandwidth selection method is designed. The finite sample performance of the proposed estimation procedure is evaluated by simulation studies. Comparison study is also conducted to show the superiority of the proposed estimation procedure over some existing estimation methods

Computer vision frameworks for physics-based simulation of liquids and solids

Doctor of PhilosophyDepartment of Computer ScienceMitchell L. NeilsenSimulating and visualizing fluid and solid materials in agricultural domains is an important and challenging problem in scientific computing and computer vision. Modern seed breeding programs require the ability to analyze seeds efficiently to be useful. Even simple measures such as volume and density can be challenging to compute efficiently with modest equipment. The dynamics of liquid and soil materials involve significant deformation during storm flows and require sophisticated numerical algorithms to achieve sufficient accuracy and visual realism. This dissertation focuses on extending volume carving techniques to measure seed volume and to create a new Material Point Method (MPM) models and finite volume models to simulate solids and fluids for dam safety analysis and visualization. This dissertation makes the following major contributions: The first is to create a novel framework for the design and analysis of computer experiments. The framework is applied to perform efficient dam breach and internal erosion analysis on a large number of structures. Given historical dam breach or design data input, the modeling framework can also be used to conduct sensitivity analysis to determine which parameters make the most impact on the resulting dam erosion. The second contribution is to develop new models for numerical simulation of dam erosion by combining fluid flow models developed using Computational Fluid Dynamics (CFD) with new dam erosion models using the Finite Element Method (FEM). A new model that combines fluid flow and erosion simulation into a single model is also developed using the Material Point Method (MPM). The third contribution is to build a comprehensive image capture and processing framework for seed property analysis. Rather than having a human manually measure seed properties such as length, width, thickness, and volume, the framework can automatically analyze a set of images from multiple angles and calculate the physical measurements for single seed samples. Finally, image analysis is extended using deep learning to increase the accuracy of rice image classification. The proposed frameworks are suitable for larger scale and more dynamic data in both dam safety and agricultural domains. They are also useful for computer animation in developing physics-based special effects for the animation of dam erosion. Previous work on MPM has resulted in models used in animation for Disney Studios, and the new models proposed could be used for accurate animation of fluid flows and dam erosion. Finally, the combination of image analysis algorithms and deep learning has many applications in the biomedical domain as well as the agricultural domain

Dam Breach Simulation with the Material Point Method

Dam embankment breaches caused by overtopping or internal erosion can impact both life and property downstream. It is important to accurately predict the amount of erosion, peak discharge, and the resulting downstream flow. This paper presents a new model based on the material point method to simulate soil and water interaction and predict failure rate parameters. The model assumes that the dam consists of a homogeneous embankment constructed with cohesive soil, and water inflow is defined by a hydrograph using other readily available reach routing software. The model uses continuum mixture theory to describe each phase where each species individually obeys the conservation of mass and momentum. A two-grid material point method is used to discretize the governing equations. The Drucker–Prager plastic flow model, combined with a Hencky strain-based hyperelasticity model, is used to compute soil stress. Water is modeled as a weakly compressible fluid. Analysis of the model demonstrates the efficacy of our approach for existing examples of overtopping dam breach, dam failures, and collisions. Simulation results from our model are compared with a physical-based breach model, WinDAM C. The new model can capture water and soil interaction at a finer granularity than WinDAM C. The new model gradually removes the granular material during the breach process. The impact of material properties on the dam breach process is also analyzed

Durability Evaluation of Phosphogypsum-Based Cemented Backfill Through Drying-Wetting Cycles

In this study, the durability of phosphogypsum (PG)-based cemented backfill was investigated by drying-wetting cycles to explore deterioration of its strength and the release of impurities. The leachates in this test were composed of deionized water, 5% Na2SO4 solution, 5% NaCl solution, and a range of sulfuric acid solutions with pH values of 1.5, 3, and 5. After drying-wetting cycles, unconfined compressive strength (UCS), visual deterioration, porosity, microstructure and concentrations of phosphate and fluoride in the leachates were measured. The results showed that both saline and acidic solutions could lead to strength reduction of PG-based cemented backfill under different deterioration mechanisms. The mechanical damage of salinity was caused by micro-cracking and degradation of C−S−H. However, the H+ broke the backfill by dissolving hydration products, leaving the conjunctures between PG particles weakened. Furthermore, the environmental impact was investigated by measuring the concentration of phosphate and fluoride in the leachates. In acidic solutions, the release of phosphate and fluoride was greatly enhanced by H+. Compared to the great strength deterioration in saline leachates, the concentration of phosphate and fluoride were similar to that of deionized water, indicating that saline solutions had little impact on the release of hazardous impurities

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis