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Full-Text Articles in Physical Sciences and Mathematics
Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller
Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller
The Summer Undergraduate Research Fellowship (SURF) Symposium
Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …
A Framework For The Statistical Analysis Of Mass Spectrometry Imaging Experiments, Kyle Bemis
A Framework For The Statistical Analysis Of Mass Spectrometry Imaging Experiments, Kyle Bemis
Open Access Dissertations
Mass spectrometry (MS) imaging is a powerful investigation technique for a wide range of biological applications such as molecular histology of tissue, whole body sections, and bacterial films , and biomedical applications such as cancer diagnosis. MS imaging visualizes the spatial distribution of molecular ions in a sample by repeatedly collecting mass spectra across its surface, resulting in complex, high-dimensional imaging datasets. Two of the primary goals of statistical analysis of MS imaging experiments are classification (for supervised experiments), i.e. assigning pixels to pre-defined classes based on their spectral profiles, and segmentation (for unsupervised experiments), i.e. assigning pixels to newly …
Differentially Private Data Publishing For Data Analysis, Dong Su
Differentially Private Data Publishing For Data Analysis, Dong Su
Open Access Dissertations
In the information age, vast amounts of sensitive personal information are collected by companies, institutions and governments. A key technological challenge is how to design mechanisms for effectively extracting knowledge from data while preserving the privacy of the individuals involved. In this dissertation, we address this challenge from the perspective of differentially private data publishing. Firstly, we propose PrivPfC, a differentially private method for releasing data for classification. The key idea underlying PrivPfC is to privately select, in a single step, a grid, which partitions the data domain into a number of cells. This selection is done using the exponential …