Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 4 of 4
Full-Text Articles in Entire DC Network
Improving Manufacturing Data Quality With Data Fusion And Advanced Algorithms For Improved Total Data Quality Management, David Juriga
Improving Manufacturing Data Quality With Data Fusion And Advanced Algorithms For Improved Total Data Quality Management, David Juriga
Masters Theses
Data mining and predictive analytics in the sustainable-biomaterials industries is currently not feasible given the lack of organization and management of the database structures. The advent of artificial intelligence, data mining, robotics, etc., has become a standard for successful business endeavors and is known as the ‘Fourth Industrial Revolution’ or ‘Industry 4.0’ in Europe. Data quality improvement through real-time multi-layer data fusion across interconnected networks and statistical quality assessment may improve the usefulness of databases maintained by these industries. Relational databases with a high degree of quality may be the gateway for predictive modeling and enhanced business analytics. Data quality …
Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee
Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee
Masters Theses
The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. The HVAC load associated with heating and cooling is an ideal candidate for price optimization through automation for two reasons: Its power footprint in a typical home is sizeable, and the required level of participation from an inhabitant is passive. HVAC is difficult to accurately model and unique for every home, so online machine learning is used to allow for real-time readjustment in performance. Energy cost for the cooling unit shown in this work is …
Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke
Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke
Masters Theses
Demand on earth orbiting surveillance systems in increasing as more equipment is put into orbit. These systems rely on predictive techniques to periodically track objects. The demand on these systems may be reduced if object trajectory data to develop scalable training sets used for training artificial neural networks (ANNs) to predict trajectories of a dynamic system. These methods use multi-variable statistics to analyze data energy content to provide the ANN with low density, feature-rich, training data. The developed techniques have been shown to increase ANN prediction accuracy while reducing the size of the training set when applied to a linear …
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Masters Theses
"Multiple recurrent reinforcement learners were implemented to make trading decisions based on real and freely available macro-economic data. The learning algorithm and different reinforcement functions (the Differential Sharpe Ratio, Differential Downside Deviation Ratio and Returns) were revised and the performances were compared while transaction costs were taken into account. (This is important for practical implementations even though many publications ignore this consideration.) It was assumed that the traders make long-short decisions in the S&P500 with complementary 3-month treasury bill investments. Leveraged positions in the S&P500 were disallowed. Notably, the Differential Sharpe Ratio and the Differential Downside Deviation Ratio are risk …