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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 …
Study Of Stochastic Market Clearing Problems In Power Systems With High Renewable Integration, Saumya Sakitha Sashrika Ariyarathne
Study Of Stochastic Market Clearing Problems In Power Systems With High Renewable Integration, Saumya Sakitha Sashrika Ariyarathne
Operations Research and Engineering Management Theses and Dissertations
Integrating large-scale renewable energy resources into the power grid poses several operational and economic problems due to their inherently stochastic nature. The lack of predictability of renewable outputs deteriorates the power grid’s reliability. The power system operators have recognized this need to account for uncertainty in making operational decisions and forming electricity pricing. In this regard, this dissertation studies three aspects that aid large-scale renewable integration into power systems. 1. We develop a nonparametric change point-based statistical model to generate scenarios that accurately capture the renewable generation stochastic processes; 2. We design new pricing mechanisms derived from alternative stochastic programming …
Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu
Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu
Open Access Theses & Dissertations
Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …
Improving The Polymer Injection Manufacturing Process By Using Quality Engineering Methodology, Dotan I. Shvorin
Improving The Polymer Injection Manufacturing Process By Using Quality Engineering Methodology, Dotan I. Shvorin
Dr. Dotan Shvorin
Using quality engineering tools and methodologies are proven to be the back bone of a successful organization. Yet, they present challenges to engineers because in order to use them correctly the engineer needs to rely on his own analysis. From his findings, the engineer would be able to determine the location and the purpose for each unique quality tool or methodology. This paper presents a quality engineered methodology usage in the polymer injection manufacturing process in order to minimize the manufacturing process defects.
How Do You Interpret A Confidence Interval?, Paul Savory
How Do You Interpret A Confidence Interval?, Paul Savory
Industrial and Management Systems Engineering: Instructional Materials
A confidence interval (CI) is an interval estimate of a population parameter. Instead of estimating the parameter by a single value, a point estimate, an interval likely to cover the parameter is developed. Many student incorrectly interpret the meaning of a confidence interval. This paper offers a quick overview of how to correctly interpret a confidence interval.
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Industrial and Management Systems Engineering: Instructional Materials
In statistics, the sample standard deviation is a widely used measure of the variability or dispersion of a data set. The standard deviation of a data set is the square root of its variance. In calculating the sample standard deviation, the divisor is the number of samples in the data set minus one (n-1) rather than n. This often confuses students. This paper offers a quick overview of why the divisor is (n-1) for calculating the sample standard deviation.