An Assessment of Neural Networks in Processing Optical Sensor Measurements for Advanced Multi-Analyte Fluid Characterization

2020 ◽  
Author(s):  
Dingding Chen ◽  
Christopher Jones ◽  
Bin Dai ◽  
Anthony VanZuilekom
Keyword(s):  
Neural Networks ◽  
Optical Sensor ◽  
Fluid Characterization

Downhole Fluid Characterization with Validated Mapping from Optical Sensor Measurements

2019 ◽  
Author(s):  
Dingding Chen ◽  
Christopher Jones ◽  
Bin Dai ◽  
Anthony VanZuilekom
Keyword(s):  
Optical Sensor ◽  
Fluid Characterization

scholarly journals An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Optik ◽  
2019 ◽  
Vol 185 ◽  
pp. 543-557 ◽  
Author(s):  
Cunlei Wang ◽  
Donghui Li ◽  
Zairan Li ◽  
Dan Wang ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Optical Sensor ◽  
Plantar Pressure ◽  
Local Binary Pattern

Identification of Multiple Analytes Using an Optical Sensor Array and Pattern Recognition Neural Networks

1997 ◽  
Vol 69 (22) ◽  
pp. 4641-4648 ◽  
Author(s):  
Stephen R. Johnson ◽  
Jon M. Sutter ◽  
Heidi L. Engelhardt ◽  
Peter C. Jurs ◽  
Joel White ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Optical Sensor ◽  
Sensor Array ◽  
Multiple Analytes

Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks

2018 ◽  
Vol 18 (13) ◽  
pp. 5429-5438 ◽  
Author(s):  
Krzysztof Czuszynski ◽  
Jacek Ruminski ◽  
Alicja Kwasniewska
Keyword(s):  
Neural Networks ◽  
Gesture Recognition ◽  
Optical Sensor ◽  
Recurrent Neural Networks ◽  
Linear Optical

Virtual Sensing in High-Performance Vehicles With Artificial Intelligence

2019 ◽  
Author(s):  
Angelo Bonfitto ◽  
Stefano Feraco ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Neural Networks ◽  
Optical Sensor ◽  
High Performance ◽  
Mean Value ◽  
Sideslip Angle ◽  
The Road ◽  
Wheel Drive ◽  
Virtual Sensing ◽  
Instrumented Vehicle ◽  
Cascaded Regression

Abstract This paper presents a technique for the estimation of the sideslip angle and longitudinal speed of a high-performance four-wheel drive vehicle by means of Artificial Neural Networks. The proposed architecture relies on the combination of a pattern recognition neural classifier with two stages of cascaded regression neural networks. The classifier allows identifying the road condition and the regression stages perform the two estimation tasks. The networks are trained by means of datasets recorded on an instrumented vehicle. The strategy is tested in different road adherence conditions, namely dry, wet and icy, and in a variety of driving maneuvers. The effectiveness of the technique is demonstrated experimentally by deploying the algorithm on the vehicle and comparing the sideslip angle estimation with the measurement computed by an optical sensor and the longitudinal speed estimation with both the estimation already present on the vehicle and obtained as the mean value of the wheels’ velocity and with the optical sensor measurement.

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