A Novel ANN-Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring
Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ul...
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| Vydáno v: | IEEE transactions on instrumentation and measurement Ročník 69; číslo 6; s. 3359 - 3369 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9456, 1557-9662 |
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| Abstract | Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ultrasonic sensor based distance measurement system. Existing standard techniques assume that the temperature and relative humidity levels remain constant throughout the measurement medium. In our proposed system, we measure water level in storage tanks of different depths, which exhibits a gradient of temperature and relative humidity across the measurement medium. Hence, the standard ultrasonic measurement system (UMS) is not able to estimate distance accurately. In this article, we propose an algorithm based on modified neural network architecture to increase the accuracy of UMS and also to extend the standard operating range of the ultrasonic sensor used. This article presents a novel approach to reduce measurement error using Levenberg-Marquardt backpropagation artificial neural network (LMBP-ANN) architecture. The proposed model is validated by comparing the actual water level at various depths under different environmental conditions with the output of the trained neural network. The measurement error in the proposed model is bounded by ±1 cm in the distance measurements ranging from 2 to 500 cm. This model is able to extend the maximum standard operating range of the ultrasonic sensor (HC-SR04 model) from 400 to 500 cm. The proposed model is evaluated using mean squared error (MSE) and <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula>-values to establish the effectiveness. |
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| AbstractList | Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ultrasonic sensor based distance measurement system. Existing standard techniques assume that the temperature and relative humidity levels remain constant throughout the measurement medium. In our proposed system, we measure water level in storage tanks of different depths, which exhibits a gradient of temperature and relative humidity across the measurement medium. Hence, the standard ultrasonic measurement system (UMS) is not able to estimate distance accurately. In this article, we propose an algorithm based on modified neural network architecture to increase the accuracy of UMS and also to extend the standard operating range of the ultrasonic sensor used. This article presents a novel approach to reduce measurement error using Levenberg-Marquardt backpropagation artificial neural network (LMBP-ANN) architecture. The proposed model is validated by comparing the actual water level at various depths under different environmental conditions with the output of the trained neural network. The measurement error in the proposed model is bounded by ±1 cm in the distance measurements ranging from 2 to 500 cm. This model is able to extend the maximum standard operating range of the ultrasonic sensor (HC-SR04 model) from 400 to 500 cm. The proposed model is evaluated using mean squared error (MSE) and <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula>-values to establish the effectiveness. Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ultrasonic sensor based distance measurement system. Existing standard techniques assume that the temperature and relative humidity levels remain constant throughout the measurement medium. In our proposed system, we measure water level in storage tanks of different depths, which exhibits a gradient of temperature and relative humidity across the measurement medium. Hence, the standard ultrasonic measurement system (UMS) is not able to estimate distance accurately. In this article, we propose an algorithm based on modified neural network architecture to increase the accuracy of UMS and also to extend the standard operating range of the ultrasonic sensor used. This article presents a novel approach to reduce measurement error using Levenberg–Marquardt backpropagation artificial neural network (LMBP-ANN) architecture. The proposed model is validated by comparing the actual water level at various depths under different environmental conditions with the output of the trained neural network. The measurement error in the proposed model is bounded by ±1 cm in the distance measurements ranging from 2 to 500 cm. This model is able to extend the maximum standard operating range of the ultrasonic sensor (HC-SR04 model) from 400 to 500 cm. The proposed model is evaluated using mean squared error (MSE) and [Formula Omitted]-values to establish the effectiveness. |
| Author | Sahoo, Ajit Kumar Udgata, Siba Kumar |
| Author_xml | – sequence: 1 givenname: Ajit Kumar surname: Sahoo fullname: Sahoo, Ajit Kumar email: ajit@uohyd.ac.in organization: School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India – sequence: 2 givenname: Siba Kumar surname: Udgata fullname: Udgata, Siba Kumar email: udgata@uohyd.ac.in organization: School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India |
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| SubjectTerms | Acoustic noise Acoustics Adaptive systems Algorithms Artificial neural network (ANN) Artificial neural networks Back propagation Computer architecture Distance measurement Error analysis Error reduction Humidity Level measurement Neural networks Relative humidity Sensors Storage tanks temperature Temperature measurement Temperature sensors ultrasonic measurement system (UMS) ultrasonic sensor Ultrasonic variables measurement Water levels Water tanks |
| Title | A Novel ANN-Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring |
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