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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
An Investigation Into Applications Of Canonical Polyadic Decomposition & Ensemble Learning In Forecasting Thermal Data Streams In Direct Laser Deposition Processes, Jonathan Storey
Theses and Dissertations
Additive manufacturing (AM) is a process of creating objects from 3D model data by adding layers of material. AM technologies present several advantages compared to traditional manufacturing technologies, such as producing less material waste and being capable of producing parts with greater geometric complexity. However, deficiencies in the printing process due to high process uncertainty can affect the microstructural properties of a fabricated part leading to defects. In metal AM, previous studies have linked defects in parts with melt pool temperature fluctuations, with the size of the melt pool and the scan pattern being key factors associated with part defects. …
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Theses and Dissertations
Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …
Direct Measurement Of The 114cd(N, Gamma)115cd Cross Section In The 1 Ev To 300 Kev Energy Range, Kofi Tutu Addo Assumin-Gyimah
Direct Measurement Of The 114cd(N, Gamma)115cd Cross Section In The 1 Ev To 300 Kev Energy Range, Kofi Tutu Addo Assumin-Gyimah
Theses and Dissertations
The large thermal cross section of cadmium makes it ideal for many practical applications where screening of thermal neutrons is desired. For example, in non-destructive assay techniques, or for astrophysical studies of the s-process. All such applications require precise knowledge of the neutron-capture cross section on cadmium. Although there are some data on neutron-capture cross sections particularly at thermal energies and at energies relevant for astrophysics, there is very little data at most other energies. Further, the evaluated cross sections from the ENDF and JENDL databases disagree at high energies. Therefore, there is a critical need for precise knowledge of …
Secure And Efficient Federated Learning, Xingyu Li
Secure And Efficient Federated Learning, Xingyu Li
Theses and Dissertations
In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Theses and Dissertations
This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.