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Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Electronic Theses and Dissertations
Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Inanc Senocak
Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor’s specified limit of detection. The inference is based on the Bayesian paradigm …
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor’s specified limit of detection. The inference is based on the Bayesian paradigm …