Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams

2009 ◽  
Vol 96 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Alfredo Goñi ◽  
Alfredo Burgos ◽  
Lacramioara Dranca ◽  
Jimena Rodríguez ◽  
Arantza Illarramendi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Cost Model ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Real Time Monitoring ◽  
Biological Sensor

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals A computational simulation platform for designing real-time monitoring systems with application to COVID-19

2021 ◽  
Vol 171 ◽  
pp. 112716 ◽  
Author(s):  
Fatemeh Shahbazi ◽  
Masoud Jabbari ◽  
Mohammad Nasr Esfahani ◽  
Amir Keshmiri
Keyword(s):  
Real Time ◽  
Computational Simulation ◽  
Monitoring Systems ◽  
Simulation Platform ◽  
Real Time Monitoring
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