Building Structure-Property Predictive Models Using Data Assimilation

2006 ◽  
pp. 64-73
Author(s):  
Hamse Y. Mussa ◽  
David J. Lary ◽  
Robert C. Glen
Keyword(s):  
Data Assimilation ◽  
Predictive Models ◽  
Building Structure ◽  
Structure Property ◽  
Using Data

scholarly journals A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation

2021 ◽  
Author(s):  
Umberto Emil Morelli ◽  
Patricia Barral ◽  
Peregrina Quintela ◽  
Gianluigi Rozza ◽  
Giovanni Stabile
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Continuous Casting ◽  
Numerical Approach ◽  
Heat Flux Estimation ◽  
Flux Estimation ◽  
Using Data

scholarly journals Enhancing CFD-LES air pollution prediction accuracy using data assimilation

2019 ◽  
Vol 165 ◽  
pp. 106383 ◽  
Author(s):  
Elsa Aristodemou ◽  
Rossella Arcucci ◽  
Laetitia Mottet ◽  
Alan Robins ◽  
Christopher Pain ◽  
...  
Keyword(s):  
Air Pollution ◽  
Data Assimilation ◽  
Prediction Accuracy ◽  
Using Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Study on the transport of COD in the sea area around Maidao off Qingdao coast using data assimilation

2007 ◽  
Vol 6 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Qiang Zhao ◽  
Xiaomin Hu ◽  
Xianqing Lü ◽  
Xuejun Xiong ◽  
Bo Yang
Keyword(s):  
Data Assimilation ◽  
Qingdao Coast ◽  
Using Data ◽  
Sea Area

Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011

2018 ◽  
Vol 36 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoli Xia ◽  
Jinzhong Min ◽  
Feifei Shen ◽  
Yuanbing Wang ◽  
Chun Yang
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Dust Storm ◽  
Using Data

scholarly journals Assessment of building structure using data from dynamic monitoring

2018 ◽  
Vol 365 ◽  
pp. 052031
Author(s):  
Alexandr Lyapin ◽  
Yurij Shatilov
Keyword(s):  
Dynamic Monitoring ◽  
Building Structure ◽  
Using Data

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

Development of the Forecasting System of the Current from Sacrificial Anodes of Marine Structures using Data Assimilation

2014 ◽  
Vol 63 (2) ◽  
pp. 43-49
Author(s):  
Naoki Yoneya ◽  
Yoshikazu Akira ◽  
Kenkichi Tashiro ◽  
Tomohiro Iida ◽  
Toru Yamaji ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Marine Structures ◽  
Sacrificial Anodes ◽  
Forecasting System ◽  
Using Data

Prediction of nontidal sea level variations in the Persian Gulf using data assimilation techniques

2018 ◽  
Vol 60 (3) ◽  
pp. 340-355 ◽  
Author(s):  
Naghmeh Afshar-Kaveh ◽  
Abbas Ghaheri ◽  
Vahid Chegini ◽  
Mostafa Nazarali
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Persian Gulf ◽  
Sea Level Variations ◽  
Using Data ◽  
The Persian Gulf

scholarly journals An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

2021 ◽  
Author(s):  
Hossein Estiri ◽  
Zachary Strasser ◽  
Sina Rashidian ◽  
Jeffrey Klann ◽  
Kavishwar Wagholikar ◽  
...  
Keyword(s):  
Predictive Models ◽  
Medical Records ◽  
Prediction Models ◽  
Evaluation Studies ◽  
Protected Group ◽  
Increased Risk ◽  
Using Data ◽  
Bias Evaluation ◽  
Algorithmic Bias

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

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