High Resolution Defect Imaging in Guided Waves Inspections by Dispersion Compensation and Nonlinear Data Fusion

2017 ◽  
Vol 103 (6) ◽  
pp. 941-949 ◽  
Author(s):  
Jochen Moll ◽  
Luca De Marchi ◽  
Christian Kexel ◽  
Alessandro Marzani
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Guided Waves ◽  
Dispersion Compensation ◽  
Defect Imaging

Data fusion in buildings: Synthesis of high-resolution IEQ and occupant tracking data

2021 ◽  
pp. 146047
Author(s):  
Brett Pollard ◽  
Fabian Held ◽  
Lina Engelen ◽  
Lauren Powell ◽  
Richard de Dear
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Tracking Data

A Method of Dispersion Compensation Based on Warped Frequency Transform

2013 ◽  
Vol 718-720 ◽  
pp. 2062-2067 ◽  
Author(s):  
Shang Chen Fu ◽  
Zhen Jian Lv ◽  
Ding Ma ◽  
Li Hua Shi
Keyword(s):  
High Resolution ◽  
Velocity Dispersion ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Dispersion Compensation ◽  
Imaging Method ◽  
Finite Element Software ◽  
Damage Imaging ◽  
Resolution Imaging ◽  
Multi Mode

The use of Lamb waves for structural health monitoring (SHM) has complicated by its multi-mode character and dispersion effect, which impacts the damage positioning and high-resolution imaging. The group velocity dispersion curves of Lamb waves can be employed to warp the frequency axis, and then to establish warped frequency transform (WFT) to process Lamb waves. In this paper, received signals are directly compensated with warped frequency transform to suppress dispersion, and a new imaging method is proposed based on warped frequency transform. The propagation of Lamb waves in damaged aluminum plate is simulated by finite element software ABAQUS, results show that warped frequency transform can effectively compensate dispersive wave-packets, and high-resolution damage imaging can be obtained by the proposed method.

scholarly journals High-resolution gravity and geoid models in Tahiti obtained from new airborne and land gravity observations: data fusion by spectral combination

2015 ◽  
Vol 67 (1) ◽  
Author(s):  
Hsuan-Chang Shih ◽  
Cheinway Hwang ◽  
Jean-Pierre Barriot ◽  
Maxime Mouyen ◽  
Pascal Corréia ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Fusion

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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