A Machine Learning-Based Data Fusion Approach for Improved Corrosion Testing

2019 ◽  
Vol 41 (3) ◽  
pp. 531-548 ◽  
Author(s):  
Christoph Völker ◽  
Sabine Kruschwitz ◽  
Gino Ebell
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Corrosion Testing ◽  
Fusion Approach

Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach

2020 ◽  
Vol 14 (11) ◽  
pp. 1410-1417 ◽  
Author(s):  
Alfred Daniel ◽  
Karthik Subburathinam ◽  
Bala Anand Muthu ◽  
Newlin Rajkumar ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Data Fusion ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Fusion Approach

A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece

2019 ◽  
Vol 684 ◽  
pp. 155-163 ◽  
Author(s):  
Evangelos Anastasiou ◽  
Annamaria Castrignanò ◽  
Konstantinos Arvanitis ◽  
Spyros Fountas
Keyword(s):  
Data Fusion ◽  
Temporal Variability ◽  
Table Grape ◽  
Use Case ◽  
Source Data ◽  
Fusion Approach

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

scholarly journals A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

2021 ◽  
Vol 115 ◽  
pp. 103693
Author(s):  
Jun Li ◽  
Pei Yuan ◽  
Xiaojuan Hu ◽  
Jingbin Huang ◽  
Longtao Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Features Fusion ◽  
Fusion Approach

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

2018 ◽  
Vol 40 ◽  
pp. 34-44 ◽  
Author(s):  
Mingquan Wu ◽  
Wenjiang Huang ◽  
Zheng Niu ◽  
Changyao Wang ◽  
Wang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Data ◽  
Ndvi Time Series ◽  
Fusion Approach
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