Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

Disability and American Education

Disability and American Education is a 2 credit, in-person, undergraduate course offered at a public liberal arts university. It is designed to introduce students to issues related to disability in the context of American schools, both P-12 and post-secondary. In addition to introducing students to classroom practice, the course focuses on important theory in Disability Studies and the social and political history that has led to our systems of special education

Deep learning applications in non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is a technique for inferring the power consumption of each appliance within a home from one central meter, aiding in energy conservation. In this thesis I present several Deep Learning solutions for NILM, starting with two preliminary works – A proof of concept project for multisensory NILM on a Raspberry Pi; and a fully developed NILM solution named WaveNILM. Despite their success, both methods struggled to generalize outside their training data, a common problem in NILM. To improve generalization, I designed a framework for synthesizing truly novel appliance level power signatures based on generative adversarial networks (GAN) – the main project of this thesis. This generator, named PowerGAN, is trained using a variety of GAN techniques. I present a comparison of PowerGAN to other data synthesis work in the context of NILM and demonstrate that PowerGAN is able to create truly synthetic, realistic, diverse, appliance power signatures

The Value of Conflict and Disagreement in Democratic Teacher Education

Deliberative democracy surfaces disagreements so that people holding conflicting stances understand each other’s reasons for the purpose of decision-making. Democratic education approaches should provide students with the opportunity to learn and practice how to address conflict in the collective decision-making process. In this paper, I examine the Foxfire Course for Teachers, a professional development retreat in which teachers learn to practice democratic teaching by themselves experiencing democratic decision-making. In particular, a series of disagreements among course participants is analyzed in detail to understand the learning that resulted and the conditions that supported that learning. As a result of this experiential learning opportunity, teachers came to realize the importance of allowing students to experience and reason through disagreement although it may cause discomfort. Teachers also came to view democratic participation as a developmental process that requires practice

Report on the Djibouti Refugee Situation

In 1982-83 as a result of a tripartite agreement between the governments of Djibouti and Ethiopiaand the UNHCR, the implementation of a repatriation programme was begun

The Data Gap in Sports Analytics and How to Close It

As the importance and prevalence of sports analytics grows, so does the inequality in sports data. In this paper we examine two main sources of such disparity - the perceived hierarchy of sports and privatization of data. We argue that such inequality hurts the sports analytics community in the short and long terms, and suggest ways for the deep-learning, AI, and sports analytics communities to help mitigate the issue. Keywords: Sports Analytics; AI; Team Sports; Diversit

Locus of Control and Anti‐Immigrant Sentiment in Canada, the United States, and the United Kingdom

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136464/1/pops12338_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136464/2/pops12338.pd