Data-Driven Nonparametric Structural Nonlinearity Identification Under Unknown Excitation with Limited Data Fusion

AbstractThe WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries, and has been declared as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.

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Geomorphological hazards assessment using machine learning and data fusion

10.5194/egusphere-egu2020-20770 ◽

2020 ◽

Author(s):

Mohammad Ahmed ◽

Hamed Farhadi ◽

Panagiotis Michalis ◽

Manousos Valyrakis

Keyword(s):

Machine Learning ◽

Data Fusion ◽

Turbulent Flow ◽

Turbulent Flows ◽

Fuzzy Inference ◽

Data Driven ◽

Sensor Data ◽

Machine Learning Techniques ◽

Flume Experiments ◽

Motion Data

Turbulent flows may destabilise riverbeds and banks, transporting sediment or underscouring hydraulic infrastructure built near water bodies. For example, scour is a significant challenge that can affect the stability of bridge foundations as the transport of sediment around a bridge pier may cause structural instabilities and catastrophic failures. The aim of this study is to use machine learning techniques & data driven algorithms to predict how energetic turbulent flow events can result in the removal of individual sediment grains, resting on the bed surface or on the protective armour layer around built infrastructure.&#160;The flume experiments involve flow and particle motion data gathering campaigns [1]. Turbulent flow data are collected upstream the exposed target particle using acoustic Doppler velocimetry. Particle's motion data are gathered using novel micro-electro-mechanical sensors embedded within its waterproof casing, for a range of flow conditions. The obtained data are fed into neural networks having distinct algorithmic complexity (inputs, levels and neutrons). A comparison of the performance of the various model architectures, as well as with past ones [2], is conducted to identify the optimal predictive algorithm for the configuration tested. Sensor data fusion combined with artificial intelligence techniques are shown to provide a unique tool for live and robust data-driven predictions to help tackle significant engineering problems, such as geomorphological activity and scouring of infrastructure (eg bridge piers and embankments) due to turbulent flows, which become increasingly more challenging, under the scope of climate change and intensifying extreme weather hazards.&#160;References[1]&#160;Valyrakis, M., Farhadi, H. 2017. Investigating coarse sediment particles transport using PTV and &#8220;smart-pebbles&#8221; instrumented with inertial sensors, EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017, id. 9980.[2]&#160;Valyrakis, M., Diplas, P., Dancey, C.L. 2011b. Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.

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A Data Fusion Framework for Fracture Toughness Modeling Using Multiple Sources of Data

2020 International Symposium on Flexible Automation ◽

10.1115/isfa2020-9649 ◽

2020 ◽

Author(s):

Shancong Mou ◽

Jialei Chen ◽

Chuck Zhang ◽

Ben Wang

Keyword(s):

Fracture Toughness ◽

Data Fusion ◽

Adhesive Bonding ◽

Model Performance ◽

Predictive Performance ◽

Standard Test ◽

Training Data ◽

Data Driven ◽

Multiple Sources ◽

Fusion Framework

Abstract The adhesive bonding technology of composite material is widely used in the industry, and the double-cantilever beam (DCB) test is a standard test for measuring the bonding quality. However, adhesive bonding methods may compromise the bonding strength, leading to weak bonds or so-called kissing bonds. In this research, we present a data-driven method to model the relationship between the process parameters and the mode-I fracture toughness. Due to the limited size of the DCB training data, we propose a novel data fusion framework, also incorporating the historical single-lap joint (SLJ) dataset at hand. Though the SLJ test is a less effective method for measuring the fracture toughness, we show it can be used to improve the model performance. We then demonstrate the effectiveness of our data-driven framework in an airplane maintenance application, with two times better predictive performance obtained.

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Reconstruction of Governing Equations from Vibration Measurements for Geometrically Nonlinear Systems

Lubricants ◽

10.3390/lubricants7080064 ◽

2019 ◽

Vol 7 (8) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

Marco Didonna ◽

Merten Stender ◽

Antonio Papangelo ◽

Filipe Fontanela ◽

Michele Ciavarella ◽

...

Keyword(s):

System Identification ◽

Differential Equations ◽

Duffing Oscillator ◽

Vibration Signal ◽

Data Driven ◽

Geometrically Nonlinear ◽

Limited Data ◽

Weakly Coupled ◽

Driven System ◽

Quasi Linear System

Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural dynamics of a geometrically nonlinear system. First, the methodology is validated against the forced Duffing oscillator to evaluate its robustness against noise and limited data. Then, differential equations governing the dynamics of two weakly coupled cantilever beams with base excitation are reconstructed from experimental data. Results indicate the appealing abilities of data-driven system identification: underlying equations are successfully reconstructed and (non-)linear dynamic terms are identified for two experimental setups which are comprised of a quasi-linear system and a system with impacts to replicate a piecewise hardening behavior, as commonly observed in contacts.

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Data fusion via intrinsic dynamic variables: An application of data-driven Koopman spectral analysis

EPL (Europhysics Letters) ◽

10.1209/0295-5075/109/40007 ◽

2015 ◽

Vol 109 (4) ◽

pp. 40007 ◽

Cited By ~ 20

Author(s):

Matthew O. Williams ◽

Clarence W. Rowley ◽

Igor Mezić ◽

Ioannis G. Kevrekidis

Keyword(s):

Spectral Analysis ◽

Data Fusion ◽

Data Driven ◽

Intrinsic Dynamic ◽

Dynamic Variables

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SAR Target Classification with Limited Data via Data Driven Active Learning

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324364 ◽

2020 ◽

Author(s):

Yue Zhou ◽

Xue Jiang ◽

Zhou Li ◽

Xingzhao Liu

Keyword(s):

Active Learning ◽

Data Driven ◽

Limited Data ◽

Target Classification

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The total solar irradiance during the recent solar minimum period measured by SOHO/VIRGO

Scientific Reports ◽

10.1038/s41598-021-87108-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

W. Finsterle ◽

J. P. Montillet ◽

W. Schmutz ◽

R. Šikonja ◽

L. Kolar ◽

...

Keyword(s):

Time Series ◽

Data Fusion ◽

Solar Irradiance ◽

Total Solar Irradiance ◽

Data Driven ◽

Combine Data ◽

Fusion Algorithm ◽

Heliospheric Observatory ◽

Dual Channel ◽

The Individual

AbstractVarious space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010–2014) and the Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission Solar and Heliospheric Observatory, which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a manual process based on assumed knowledge of the sensor hardware. Here we present a new data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e $$-0.17\pm 0.29$$ - 0.17 ± 0.29 W/m$$^2$$ 2 ) between the TSI levels during the two most recent solar minima in 2008/09 and 2019/20. The new algorithm can be applied to any TSI experiment that employs a multi-channel philosophy for degradation tracking. It does not require deep technical knowledge of the individual radiometers.

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