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Search For Triple-Proton Decay Using Machine Learning With Cuore, Douglas Adams
Search For Triple-Proton Decay Using Machine Learning With Cuore, Douglas Adams
Theses and Dissertations
A framework to search for a triple-proton decay of 130Te in the CUORE detector against a background of muons is presented. We use machine learning to classify different kinds of energy depositing events. We use the classification information to improve our detection or non-detection limits of a triple-proton decay process. We derive and use a methodology of combining Poisson counting statistics with supervised classification machine learning tools. Additionally, a sensitivity calculation is provided which uses the classification counting likelihood. Using our analysis technique, we achieve an lower 2σ half-life bound of 7.43×1024yrs for triple-proton decay of …
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Theses and Dissertations
Dendrite core is the center point of the dendrite. The information of dendrite core is very helpful for material scientists to analyze the properties of materials. Therefore, detecting the dendrite core is a very important task in the material science field. Meanwhile, because of some special properties of the dendrites, this task is also very challenging. Different from the typical detection problems in the computer vision field, detecting the dendrite core aims to detect a single point location instead of the bounding-box. As a result, the existing regressing bounding-box based detection methods can not work well on this task because …
Image-Based Crack Detection By Extracting Depth Of The Crack Using Machine Learning, Nishat Tabassum
Image-Based Crack Detection By Extracting Depth Of The Crack Using Machine Learning, Nishat Tabassum
Theses and Dissertations
Concrete structures have been a major aspect of social infrastructure since the ancient Roman times, so they have been used for many centuries. Concrete is used for the durability and support it provides to buildings and bridges. Assessing the state of these structures is important in preserving the longevity of structures and the safety of the public. Detecting cracks in their early stage allows repairs to be made without the need to replace the whole structure, so it reduces the cost. Traditional methods are slowly falling behind as technology advances and an increase in demand for a practical method of …
Regression Methods For Group Testing Data, Michael Stutz
Regression Methods For Group Testing Data, Michael Stutz
Theses and Dissertations
Group testing is an efficient method of disease screening, whereby individual specimens (e.g., blood, urine, etc.) are pooled together and tested as a whole for the presence of disease. A common goal is to use data arising from these testing protocols to better understand the relationship between disease status and potential risk factors (e.g., age, symptom status, etc.). Numerous statistical methodologies have been developed for this purpose, most of which are built within the framework of a generalized linear model. Recent authors have suggested the inadequacy of such regression methods to capture the true functional relationships when nonlinear effects are …
A Spatial Risk Prediction Model For Drug Overdose, Parisa Bozorgi
A Spatial Risk Prediction Model For Drug Overdose, Parisa Bozorgi
Theses and Dissertations
Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy from 2015 to 2018. Overdose deaths, especially from opioids, have also been recognized in recent years as a significant public health issue. To address this public health problem, this study sought to identify neighborhood-level (e.g., block group) factors associated with drug overdose and develop a spatial model using machine learning (ML) algorithms to predict the likelihood or risk of drug overdoses across South Carolina. This study included block group level socio-demographic factors and drug use variables which …
Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh
Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh
Theses and Dissertations
Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultra- high carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work …