A Neural Network Approach to Fluid Quantity Measurement in Dynamic Environments

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Music CDs Collectables Everything Else 4. Format see all Format. A neural network based classification technique has been applied to predict the actual quantity of the fluid contained in a tank under sloshing conditions. To examine the performance of the classification system, many field trials were carried out on a running vehicle at various tank volume levels that range from 5 L to 50 L.

The effectiveness of signal enhancement on the neural network based signal classification system is also investigated. Results obtained from the investigation are compared with traditionally used statistical averaging methods, and proves that the neural network based measurement system can produce highly accurate fluid quantity measurements in a dynamic environment.

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Although in this case a capacitive sensor was used to demonstrate measurement system this methodology is valid for all types of electronic sensors. The approach demonstrated in A neural network approach to fluid quantity measurement in dynamic environments can be applied to a wide range of fluid quantity measurement applications in the automotive, naval and aviation industries to produce accurate fluid level readings.

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Students, lecturers, and experts will find the description of current research about accurate fluid level measurement in dynamic environments using neural network approach useful. Subject Fluid dynamic measurements. Fluid dynamic measurements. Neural networks Computer science Engineering.

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Hydraulic engineering. Computational Intelligence. Engineering Fluid Dynamics. Implementing the HIL requires three phases.

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The tank must be calibrated to obtain a linear relationship between the voltage and the water level. Afterward, the open-loop Ziegler Nichols is exploited to tune the parameters of three term Proportional-Integral-Derivative controller. Capacitive sensor-based fluid level measurement in a dynamic environment using neural network, Engineering Applications of Artificial Intelligence, Vol.


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Michael J. McGrath, James F. Mostafa A. Fellani, Aboubaker M. Rahmawati, N.