scholarly journals Fast 3D particle reconstruction using a convolutional neural network: application to dusty plasmas

2021 ◽  
Vol 2 (4) ◽  
pp. 045019
Author(s):  
Michael Himpel ◽  
André Melzer
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Dusty Plasmas ◽  
Network Application ◽  
Particle Reconstruction

scholarly journals Localization of Immersed Sources by Modified Convolutional Neural Network: Application to a Deep-Sea Experiment

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3109
Author(s):  
Xu Xiao ◽  
Wenbo Wang ◽  
Lin Su ◽  
Xinyi Guo ◽  
Li Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Sea ◽  
Vertical Array ◽  
Distribution Form ◽  
Gaussian Probability Distribution ◽  
The Mean ◽  
Network Application ◽  
Single Regression ◽  
Better Than

A modified convolutional neural network (CNN) is proposed to enhance the reliability of source ranging based on acoustic field data received by a vertical array. Compared to the traditional method, the output layer is modified by outputting Gauss regression sequences, expressed using a Gaussian probability distribution form centered on the actual distance. The processed results of deep-sea experimental data confirmed that the ranging performance of the CNN with a Gauss regression output was better than that using single regression and classification outputs. The mean relative error between the predicted distance and the actual value was ~2.77%, and the positioning accuracy with 10% and 5% error was 99.56% and 90.14%, respectively.

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