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Full-Text Articles in Engineering
Gold Tree Solar Farm - Machine Learning To Predict Solar Power Generation, Jonathon T. Scott
Gold Tree Solar Farm - Machine Learning To Predict Solar Power Generation, Jonathon T. Scott
Computer Science and Software Engineering
Solar energy causes a strain on the electrical grid because of the uncontrollable nature of the factors that affect power generation. Utilities are often required to balance solar generation facilities to meet consumer demand, which often includes the costly process of activating/deactivating a fossil fuel facility. Therefore, there is considerable interest in increasing the accuracy and the granularity of solar power generation predictions in order to reduce the cost of grid management. This project aims to evaluate how sky imaging technology may contribute to the accuracy of those predictions.
Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm
Master's Theses
Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …
Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman
Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman
Master's Theses
The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this architecture are presented to incorporate direction heading information that …