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Spring 2023
With rising demands in industry for reliable electrical cable distribution networks comes an inherent need for utility providers to know well the condition of the assets in their network. Heightened expectations from regulators and consumers require methods of reliability assessment to improve...
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A Data-Driven Neural Network Model to Correct Derived Features in a RANS-Based Simulation of the Flow Around a Sharp-Edge Bluff Body
DownloadSpring 2023
In this dissertation, a machine-learning method is utilized to enhance the accuracy of wake parameters calculated by Reynolds Averaged Navier Stokes (RANS) k-ω SST model of flow on and around wall-mounted rectangular cylinders. Using high-quality results from Large Eddy Simulation (LES), this...
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Fall 2021
Mohammadhosseinzadeh Golabchi, Hamidreza
Considering the high rates of labor resources in construction projects clearly indicates the importance of appropriate labor resource management methods. Accurate labor resource allocation is a substantial step towards successful labor resource management. With the recent developments in the area...
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A Framework for Associating Mobile Devices to Individuals Based on Identification of Motion Events
DownloadFall 2020
The ubiquity of the Internet-of-Things (IoT) devices in everyday life allows various sensors to be utilized in networked systems for solving a number of real-world problems. Models utilizing specific sensing modalities achieve impressive performance in understanding human activity and are used in...
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Fall 2021
The management of project documentation involves processing a large amount of important information embedded in different contract and project specification documents. Although contract-related documentation is critical for effective information flow and—in turn—successful project management, it...
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Spring 2022
The world offers unprecedented amounts of data in real-world domains, from which we can develop successful decision-making systems. It is possible for reinforcement learning (RL) to learn control policies offline from such data but challenging to deploy an agent during learning in safety-critical...
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A Hierarchical Constrained Reinforcement Learning for Optimization of Bitumen Recovery Rate in a Primary Separation Vessel
Download2020-01-01
Shafi, Hareem, Velswamy, Kirubakaran, Ibrahim, Fadi, Huang,Biao
This work proposes a two-level hierarchical constrained control structure for reinforcement learning (RL) with application in a Primary Separation Vessel (PSV). The lower level is concerned with servo tracking and regulation of the interface level against variances in ore quality by manipulating...
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2022-01-01
Xiunan Wang, Hao Wang, Pouria Ramazi, Kyeongah Nah, Mark Lewis
Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction...
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2022-03-10
Xiunan Wang, Hao Wang, Pouria Ramazi, Kyeongah Nah, Mark Lewis
Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction...
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A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems
Download2017-08-18
Yuan wang, Kirubakaran Velswamy, Biao Huang
Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called...