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Full-Text Articles in Engineering

Dice Testing With The Running Chi-Square Distribution, Warren Campbell, Hunter Wimsatt Jul 2022

Dice Testing With The Running Chi-Square Distribution, Warren Campbell, Hunter Wimsatt

SEAS Faculty Publications

Dice are not fair. Producing a geometrically precise, uniform die is not possible. Casino dice come as close as possible to perfectly random dice because they are machined to an accuracy of a few ten-thousandths of an inch and the putty used for the pips (dots) is the same density as the plastic die body. Polyhedral dice used in games like Dungeons and Dragons are far more difficult to manufacture to high tolerances. Some dice are fairer than others. Since 20-sided dice (D20s) are very difficult to manufacture precisely, they were the focus of this study. The running chi-square distribution …


Robust Sensor Design For The Novel Reduced Models Of The Mead-Marcus Sandwich Beam Equation, Ahmet Aydin Jul 2022

Robust Sensor Design For The Novel Reduced Models Of The Mead-Marcus Sandwich Beam Equation, Ahmet Aydin

Masters Theses & Specialist Projects

Novel space-discretized Finite Differences-based model reductions are proposed for the partial differential equations (PDE) model of a multi-layer Mead-Marcus-type beam with (i) hinged-hinged and (ii) clamped-free boundary conditions. The PDE model describes transverse vibrations for a sandwich beam whose alternating outer elastic layers constrain viscoelastic core layers, which allow transverse shear. The major goal of this project is to design a single boundary sensor, placed at the tip of the beam, to control the overall dynamics on the beam.

For (i), it is first shown that the PDE model is exactly observable by the so-called nonharmonic Fourier series approach. However, …


Western Kentucky University Stormwater Utility Survey 2022, Warren Campbell Jun 2022

Western Kentucky University Stormwater Utility Survey 2022, Warren Campbell

SEAS Faculty Publications

The main goal of this survey is to identify as many U.S. Stormwater Utilities (SWUs) as possible. Because many stormwater professionals do not have the time to respond to questionnaires, our primary method of identification was Internet searches. We searched on key terms such as “stormwater utility”, “stormwater fee”, and “drainage fee”. We scoured on-line municipal codes such as Municode, AmLegal, Sterling, LexisNexis, General Code, and others. We searched through many city web sites trying to find utilities. We have also had many people contact me to update fees and identify new utilities. However, the data primarily comes from Internet …


A Low-Cost And Low-Tech Solution To Test For Variations Between Multiple Offline Programming Software Packages., Steffen Wendell Bolz Apr 2022

A Low-Cost And Low-Tech Solution To Test For Variations Between Multiple Offline Programming Software Packages., Steffen Wendell Bolz

Masters Theses & Specialist Projects

This research paper chronicles the attempt to bring forth a low-cost and low-tech testing methodology whereby multiple offline programming (OLP) software packages’ generated programs may be compared when run on industrial robots. This research was initiated by the discovery that no real research exists to test between iterations of OLP software packages and that most research for positional accuracy and/or repeatability on industrial robots is expensive and technologically intensive. Despite this, many countries’ leaders are pushing for intensive digitalization of manufacturing and Small and Mediumsized Enterprises (SMEs) are noted to be lagging in adoption of such technologies. The research consisted …


K-Means Clustering Using Gravity Distance, Ajinkya Vishwas Indulkar Apr 2022

K-Means Clustering Using Gravity Distance, Ajinkya Vishwas Indulkar

Masters Theses & Specialist Projects

Clustering is an important topic in data modeling. K-means Clustering is a well-known partitional clustering algorithm, where a dataset is separated into groups sharing similar properties. Clustering an unbalanced dataset is a challenging problem in data modeling, where some group has a much larger number of data points than others. When a K-means clustering algorithm with Euclidean distance is applied to such data, the algorithm fails to form good clusters. The standard K-means tends to split data into smaller clusters during a clustering process evenly.

We propose a new K-means clustering algorithm to overcome the disadvantage by introducing a different …