Development of an ANN-Based Pressure Transducer

Kumar, Vaegae Naveen; Narayana, Komanapalli Venkata Lakshmi

doi:10.1109/jsen.2015.2477458

Cited by 17 publications

(4 citation statements)

References 21 publications

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“…The linearisation of the bridge output voltage can be done by using the ANN technique. The modelling of ANN consists of a multiple layer perceptron neural networks [24]. It consists of multiinput multi-output or single input single output.…”

Section: Methods Of Approachmentioning

confidence: 99%

Neural network‐based target‐type flowmeter for high‐velocity liquid flow measurement

Ali

Maurya

Mandal

2020

IET Science, Measurement & Technology

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Section: Methods Of Approachmentioning

confidence: 99%

Neural network‐based target‐type flowmeter for high‐velocity liquid flow measurement

Ali

Maurya

Mandal

2020

IET Science, Measurement & Technology

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“…The details of ANN with back propagation algorithm are listed in our previous work at [32] and the models used for coding are considered from [33] and [34]. Further we also study the usefulness of multilayer feed forward ANN's with more than 5 layers along with their performance analysis on sign classifications.…”

Section: Ann Classifiermentioning

confidence: 99%

Flower image classification with basket of features and multi layered artificial neural networks

Inthiyaz¹,

Madhav²,

Prasad³

2017

IJET

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Artificial intelligence is penetrating most of the classification and recognition tasks performed by a computer. This work proposes to classify flower images based on features extracted during segmentation and after segmentation using multiple layered neural networks. The segmentation models used are watershed, wavelet, wavelet fusion model, supervised active contours based on shape, color and Local binary pattern textures and color, fused textures based active contours. Multi-dimension feature vectors are constructed from these segmented results for each indexed flower image labelled with their name. Each feature becomes input to a neuron in various feature layers and error back propagation algorithm with convex optimization structure trains these multiple feature layers. Testing with different flower images sets from multiple sources resulted in average classification accuracy of 92% for shape, color and texture supervised active contour segmented flower images.

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“…The authors used Laguerre neural network to linearise the sensor characteristics, with a maximum full-scale error of ±1.0% over a temperature range of −50-200°C. Kumar and Narayana [24] have proposed the ANN-based inductive signal conditioning network for the pressure measurement system. Here bellow has been used as a pressure sensor; one end of the bellow is attached with the ferromagnetic wire.…”

Section: Introductionmentioning

confidence: 99%

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Lata

Mandal

2020

IET Science, Measurement &Amp; Technology

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The proposed liquid level transmitter using a force resistive sensor as a secondary sensor is simple, cost-effective, and applicable for all types of liquid. The force resistive sensor is located at the bottom of a liquid measuring tank. The pressure exerted by the liquid height on the measuring tank changes the force resistive sensor's resistance. In the long term application, due to the creeping behaviour of force resistive sensor, the proposed level transmitter has low repeatability, hysteresis, and nonlinearity. In this study, the artificial neural network (ANN) has been used to eliminate the hysteresis and non-linearity present in the proposed liquid level transmitter. The performance of the proposed technique has been experimentally verified. The hysteresis and non-linearity error has been decreased from 7 to 0.89% of full-scale observation (FSO) and 9 to 0.14%, respectively.

show abstract

Development of an ANN-Based Pressure Transducer

Cited by 17 publications

References 21 publications

Neural network‐based target‐type flowmeter for high‐velocity liquid flow measurement

Neural network‐based target‐type flowmeter for high‐velocity liquid flow measurement

Flower image classification with basket of features and multi layered artificial neural networks

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

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