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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 …
On The Robustness Of Object Detection Based Deep Learning Models, Matthew Seals
On The Robustness Of Object Detection Based Deep Learning Models, Matthew Seals
Masters Theses
Object detection is one of the most popular areas in the field of computer vision and deep learning. Several advances have been reported in the literature showing promising object detection results. However, most of these results use databases of images that have been collected under almost ideal conditions and tested with input images mostly not representative of real life imagery. When tested with challenging data, most of these object detection models break down.The objective of this work is to quantify the performance of the most recent object detection models in the presence of realistic degradation in the form of differing …
A Cnn-Lstm For Predicting Mortality In The Icu, Mohammad Hashir Khan
A Cnn-Lstm For Predicting Mortality In The Icu, Mohammad Hashir Khan
Masters Theses
An accurate predicted mortality is crucial to healthcare as it provides an empirical risk estimate for prognostic decision making, patient stratification and hospital benchmarking. Current prediction methods in practice are severity of disease scoring systems that usually involve a fixed set of admission attributes and summarized physiological data. These systems are prone to bias and require substantial manual effort which necessitates an updated approach which can account for most shortcomings. Clinical observation notes allow for recording highly subjective data on the patient that can possibly facilitate higher discrimination. Moreover, deep learning models can automatically extract and select features without human …