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Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle

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ABSTRACT

This paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head is the end of a large core polymer optical fiber, which constitutes one arm of an asymmetrical coupler. The head works on the reflection intensity basis. The reflected signal level depends on the Fresnel reflection from the air and from the mixture examined when the head is immersed in it. The sensor head is mounted on a lift. For detection purposes the signal can be measured on head submerging, submersion, emerging and emergence. Therefore, the measured signal depends on the surface tension, viscosity, turbidity and refraction coefficient of the solution. The signal coming from the head is processed electrically in an opto-electronic interface. Then it is fed to a neural network. The novelty of the proposed sensor lies in that it contains an asymmetrical coupler and a neural network that works in the generalization mode. The sensor resolution depends on the efficiency of the asymmetrical coupler, the precision of the opto-electronic signal conversion and the learning accuracy of the neural network. Therefore, the number and quality of the points used for the learning process is very important. By way of example, the paper describes a sensor intended for examining the concentration of liquid soap in water.

No MeSH data available.


The neural network output signal versus inputs levels.
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f16-sensors-07-00384: The neural network output signal versus inputs levels.

Mentions: The proposed network has two data inputs: submersion and emergence. The almost stable signals received at 12s and 35s are assumed to be the data pattern. The network has been trained to recognize the solutions with the concentrations: 0, 12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5 and 100%. The network answer is assumed to correspond to the soap concentration. Five independent data patterns determined for each solution concentration were used for the training process. The RMS error at the output, incurred in the training patterns, versus the tested data is 0.056. The correlation factor for all the network transfer functions is 0.93. The first hidden layer process gave 62.5% of the signal and the output layer gave 37.5%, so that additional layers are not necessary. The network and the trained weight factors wij and wwk are shown in Figure 15. The variation of the network output signal versus the inputs is shown in Figure 16. The average effect of inputs upon the output can be estimated to be 25% in the emergence and 75% in the submersion.


Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle
The neural network output signal versus inputs levels.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756727&req=5

f16-sensors-07-00384: The neural network output signal versus inputs levels.
Mentions: The proposed network has two data inputs: submersion and emergence. The almost stable signals received at 12s and 35s are assumed to be the data pattern. The network has been trained to recognize the solutions with the concentrations: 0, 12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5 and 100%. The network answer is assumed to correspond to the soap concentration. Five independent data patterns determined for each solution concentration were used for the training process. The RMS error at the output, incurred in the training patterns, versus the tested data is 0.056. The correlation factor for all the network transfer functions is 0.93. The first hidden layer process gave 62.5% of the signal and the output layer gave 37.5%, so that additional layers are not necessary. The network and the trained weight factors wij and wwk are shown in Figure 15. The variation of the network output signal versus the inputs is shown in Figure 16. The average effect of inputs upon the output can be estimated to be 25% in the emergence and 75% in the submersion.

View Article: PubMed Central

ABSTRACT

This paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head is the end of a large core polymer optical fiber, which constitutes one arm of an asymmetrical coupler. The head works on the reflection intensity basis. The reflected signal level depends on the Fresnel reflection from the air and from the mixture examined when the head is immersed in it. The sensor head is mounted on a lift. For detection purposes the signal can be measured on head submerging, submersion, emerging and emergence. Therefore, the measured signal depends on the surface tension, viscosity, turbidity and refraction coefficient of the solution. The signal coming from the head is processed electrically in an opto-electronic interface. Then it is fed to a neural network. The novelty of the proposed sensor lies in that it contains an asymmetrical coupler and a neural network that works in the generalization mode. The sensor resolution depends on the efficiency of the asymmetrical coupler, the precision of the opto-electronic signal conversion and the learning accuracy of the neural network. Therefore, the number and quality of the points used for the learning process is very important. By way of example, the paper describes a sensor intended for examining the concentration of liquid soap in water.

No MeSH data available.