The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Muhammad Alamgir - One of the best experts on this subject based on the ideXlab platform.
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brief paper capacitive sensor based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and support vector machine svm
Sensors and Actuators A-physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and Support Vector Machine (SVM)
Sensors and Actuators A: Physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement. © 2010 Elsevier B.V.
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Capacitive sensor-based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network. © 2009 Elsevier Ltd. All rights reserved.
C R Nagarajah - One of the best experts on this subject based on the ideXlab platform.
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brief paper capacitive sensor based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and support vector machine svm
Sensors and Actuators A-physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and Support Vector Machine (SVM)
Sensors and Actuators A: Physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement. © 2010 Elsevier B.V.
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Capacitive sensor-based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network. © 2009 Elsevier Ltd. All rights reserved.
Jenny Terzic - One of the best experts on this subject based on the ideXlab platform.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and support vector machine svm
Sensors and Actuators A-physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement.
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Fluid Level measurement in dynamic environments using a single ultrasonic sensor and Support Vector Machine (SVM)
Sensors and Actuators A: Physical, 2010Co-Authors: Jenny Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A Fluid Level measurement system to accurately determine Fluid Levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted Fluid Level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of Fluid Level measurement. © 2010 Elsevier B.V.
Edin Terzic - One of the best experts on this subject based on the ideXlab platform.
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brief paper capacitive sensor based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network.
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Capacitive sensor-based Fluid Level measurement in a dynamic environment using neural network
Engineering Applications of Artificial Intelligence, 2010Co-Authors: Edin Terzic, C R Nagarajah, Muhammad AlamgirAbstract:A measurement system has been developed using a single tube capacitive sensor to accurately determine the Fluid Level in non-stationary tanks, namely automotive fuel tanks. The system determines the Fluid Level in the presence of dynamic slosh. A neural network-based approach is used to process the sensor signal and achieve substantial accuracy compared with the averaging method, which is normally used under such conditions. The sensor readings were obtained by experimentation carried out under various dynamic conditions. The sensor response was recorded at various slosh frequencies and fuel volumes; which was then used to train three different neural network topologies. Field trials were carried out to obtain the actual driving data for the purpose of testing the neural networks using MATLAB software. One static neural network topology, namely Feed-forward Backpropagation Neural Network, and two dynamic neural network topologies, namely Distributed Time Delay Neural Network and NARX Neural Network, have been investigated in this work. The developed Fluid Level measurement system is capable of determining the Fluid Level in a dynamic environment with a maximum error of 8.7% by using the two dynamic neural networks, and 0.11% using the static feed-forward backpropagation neural network. © 2009 Elsevier Ltd. All rights reserved.
Bryant D Taylor - One of the best experts on this subject based on the ideXlab platform.
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a wireless Fluid Level measurement technique
Sensors and Actuators A-physical, 2007Co-Authors: Stanley E Woodard, Bryant D TaylorAbstract:Abstract This paper presents the application of a recently developed wireless measurement acquisition system to Fluid-Level measurement that alleviates many shortcomings of Fluid-Level measurement methods currently being used, including limited applicability of any one Fluid-Level sensor design; necessity for power to be supplied to each sensor and for the measurement to be extracted from each sensor via a physical connection to the sensor and needing a data channel and signal conditioning electronics be dedicated to each sensor. Use of wires results in other shortcomings such as logistics needed to add or replace sensors, weight, potential for electrical arcing and wire degradations. The Fluid-Level sensor design is a simple passive inductor–capacitor circuit that is not subject to mechanical failure that is possible when float and lever-arm systems are used. Oscillating magnetic fields are used to power the sensor. Once electrically excited, the sensor produces a magnetic field response. The response frequency corresponds to the amount of Fluid within the capacitor's electric field. The sensor design can be modified for measuring the Level of any Fluid or non-gaseous Fluid substance that can be stored in a non-conductive reservoir. Methods are presented for using the sensor in caustic, acidic or cryogenic Fluids. A method is also presented for calibrating the sensor response with respect to fractional Fluid Levels for different Fluids using only the response when sensor is completely immersed in Fluid and with it is not immersed in Fluid. Results are presented for measuring the Levels of hydrochloric acid, liquid nitrogen, sugar, ground corn, ammonia, bleach, water, salt water, oil, and transmission Fluid.