Neural Network Classification and Quantification of Organic Vapors Based on Fluorescence Data from a Fiber-Optic Sensor Array

1997 ◽  
Vol 69 (5) ◽  
pp. 856-862 ◽  
Author(s):  
Jon M. Sutter ◽  
Peter C. Jurs
Keyword(s):  
Neural Network ◽  
Sensor Array ◽  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Organic Vapors ◽  
Neural Network Classification ◽  
Optic Sensor

scholarly journals High-quality hybrid TDM/DWDM-based fiber optic sensor array with extremely low crosstalk based on wavelength-cross-combination method

2017 ◽  
Vol 25 (23) ◽  
pp. 28870 ◽  
Author(s):  
Zhongjie Ren ◽  
Ke Cui ◽  
Jianxin Li ◽  
Rihong Zhu ◽  
Qing He ◽  
...  
Keyword(s):  
Sensor Array ◽  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Combination Method ◽  
Cross Combination ◽  
High Quality ◽  
Optic Sensor

scholarly journals Computational neuroscience applied in surface roughness fiber optic sensor

2019 ◽  
Vol 10 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Computational Neuroscience ◽  
Fiber Optic ◽  
Rbf Neural Network ◽  
Fiber Optic Sensor ◽  
Bottom Surface ◽  
Optic Sensor ◽  
Arbitrary Precision ◽  
The Stability

Abstract Computational neuroscience has been widely used in fiber optic sensor signal output. This paper introduces a method for processing the Surface Roughness Fiber Optic Sensor output signals with a radial basis function neural network. The output signal of the sensor and the laser intensity signal as the light source are added to the input of the RBF neural network at the same time, and with the ability of the RBF neural network to approach the non-linear function with arbitrary precision, to achieve the nonlinear compensation of the sensor and reduction of the effect of changes in laser output light intensity at the same time. The Surface Roughness Fiber Optic Sensor adopting this method has low requirements on the stability of the output power of laser, featuring large measuring range, high accuracy, good repeatability, measuring of special surfaces such as minor area, and the bottom surface of holed etc. The measurements were given and various factors that affect the measurement were analyzed and discussed.

Ocean wave seismic and acoustic noise detected with distributed fiber optic sensor array

2013 ◽  
Vol 134 (5) ◽  
pp. 4180-4180
Author(s):  
Kent K. Hathaway ◽  
Richard D. Costley ◽  
Eric Smith ◽  
Troy Milburn ◽  
Jennifer R. Picucci
Keyword(s):  
Sensor Array ◽  
Fiber Optic ◽  
Acoustic Noise ◽  
Ocean Wave ◽  
Fiber Optic Sensor ◽  
Optic Sensor ◽  
Distributed Fiber Optic Sensor

Simultaneous Determination of Br− and I− with a Multiple Fiber-Optic Fluorescence Sensor

1990 ◽  
Vol 44 (1) ◽  
pp. 59-63 ◽  
Author(s):  
Chu Zhu ◽  
Frank V. Bright ◽  
Gary M. Hieftje
Keyword(s):  
Response Time ◽  
Simultaneous Determination ◽  
Sensor Array ◽  
Fiber Optic ◽  
Point Of View ◽  
Fiber Optic Sensor ◽  
Practical Application ◽  
Optic Sensor ◽  
New Sensors

A dual fiber-optic sensor for the simultaneous determination of bromide and iodide is successfully demonstrated. The device is based upon the dependence of quenching effects on individual fluorophores (quinine and harmane) and quenchers (bromide and iodide). From the point of view of practical application, n unknown analyte species should be able to be determined with a sensor array which contains n sensor elements on which n fluorophores are immobilized. The relative error of the new sensors for the determination of bromide and iodide is less than 5%. The sensor combination is reversible and exhibits an overall response time of 2 minutes. The interference of several ions on the sensors has been studied.

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