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Full-Text Articles in Engineering
Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind
Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind
Industrial Engineering Undergraduate Honors Theses
Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration …
Nonconvex Optimization For Statistical Learning With Structured Sparsity, Chengyu Ke
Nonconvex Optimization For Statistical Learning With Structured Sparsity, Chengyu Ke
Operations Research and Engineering Management Theses and Dissertations
Sparse learning problems, known as feature selection problems or variable selection problems, are a popular branch in the field of statistical learning. When faced with a dataset with only a few observations but a large number of features, we are interested in extracting the most useful features automatically by solving an optimization problem. In this dissertation, we start by introducing a novel penalty function as well as an iterative reweighted algorithm to solve the group sparsity problem, a special type of feature selection problems. The penalty function, named group LOG, shows a better ability to recover the ground-truth compared to …
Statistical Analysis And Degradation Pathway Modeling Of Photovoltaic Minimodules With Varied Packaging Strategies, Sameera Nalin Venkat, Xuanji Yu, Jiqi Liu, Jakob Wegmueller, Jayvic Cristian Jimenez, Erika I. Barcelos, Hein Htet Aung, Roger H. French, Laura S. Bruckman
Statistical Analysis And Degradation Pathway Modeling Of Photovoltaic Minimodules With Varied Packaging Strategies, Sameera Nalin Venkat, Xuanji Yu, Jiqi Liu, Jakob Wegmueller, Jayvic Cristian Jimenez, Erika I. Barcelos, Hein Htet Aung, Roger H. French, Laura S. Bruckman
Faculty Scholarship
Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power …
Digital Learning Resources, Hybrid Teaching And Remote Students - Are Our Students Actively Engaged?, Thea Bjørnland
Digital Learning Resources, Hybrid Teaching And Remote Students - Are Our Students Actively Engaged?, Thea Bjørnland
Practice Papers
At the Norwegian University of Science and Technology, a new cross-campus statistics course for approximately 1000 engineering students was planned for the fall of 2020. Due to the pandemic, digital learning resources were developed to allow students to work from home or campus, individually or collaboratively. These resources include short learning videos, automatically graded exercise sets, and Jupyter Notebooks for Python coding. Since 2020, digital learning resources have been essential for teaching statistics to engineering students across three campuses, and remotely. To help students navigate digital resources, on-campus activities, and assessments, each week of the semester was structured according to …
A Machine Learning Framework For Hypersonic Vehicle Design Exploration, Atticus Beachy
A Machine Learning Framework For Hypersonic Vehicle Design Exploration, Atticus Beachy
Browse all Theses and Dissertations
The design of Hypersonic Vehicles (HVs) requires meeting multiple unconventional and often conflicting design requirements in a hostile, high-energy environment. The most fundamental difference between ordinary aerospace design and hypersonic flight is that the extreme conditions of hypersonic flight require parts to perform multiple functions and be tightly integrated, resulting in significant coupled effects. Critical couplings among the disciplines of aerodynamics, structures, propulsion, and thermodynamics must be investigated in the early stages of design exploration to reduce the risk of requiring major design changes and cost overruns later. In addition, due to a lack of validated test data within the …