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Full-Text Articles in Physical Sciences and Mathematics
Topology Identification In Distribution System Via Machine Learning Algorithms, Peyman Razmi, Mahdi Ghaemi Asl, Giorgio Canarella, Afsaneh Sadat Emami
Topology Identification In Distribution System Via Machine Learning Algorithms, Peyman Razmi, Mahdi Ghaemi Asl, Giorgio Canarella, Afsaneh Sadat Emami
Economics Faculty Publications
This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices' status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder's voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile's behavior in each feeder, detect the status of …
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Economics Faculty Publications
The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella, 1983; Pascarell and Ternzini, 1978; St. John and Cabrera, 2000; Tinto, 1975) This body of research provides a starting point for the consideration of which explanatory variables to include in any model specification, as well as identifying possible data sources. The literature separates itself into two major camps including research related to the hypothesis testing …