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8. Workshop report

Author

Chesney, R., Smeets, M., and Kaminska, M.

Published
2019

12. Vitamin D in Renal Diseases

Author

Greer, F. R., Balsan, S., Chesney, R. W., DeLuca, H. F., Dabbagh, S., Mahan, J., Kim, Y. K., Brodehl, Johannes, editor, and Ehrich, Jochen H. H., editor

Published
1984
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13. Capacitive sensor-based fluid level measurement in a dynamic environment using neural network

Author

Edin Terzic, Muhammad Alamgir, and Chesney R. Nagarajah

Subjects
Quantitative Biology::Neurons and Cognition, Artificial neural network, Artificial Intelligence, Control and Systems Engineering, Computer science, Time delay neural network, Capacitive sensing, Computer Science::Neural and Evolutionary Computation, Electrical and Electronic Engineering, Network topology, Simulation, Backpropagation
Abstract

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.

Published
2010
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16. The RIVUR Trial: Profile and Baseline Clinical Associations of Children With Vesicoureteral Reflux

Author

Greenfield, S. P., Chesney, R. W., Moxey-Mims, M., Keren, R., Hoberman, A., Mattoo, T. K., Mathews, R., and Carpenter, M. A.

Subjects
urologic and male genital diseases, female genital diseases and pregnancy complications
Abstract

Vesicoureteral reflux (VUR) is diagnosed in ∼30% to 40% of children who have imaging studies after urinary tract infections (UTIs). Our goal is to characterize children enrolled in the Randomized Intervention for Children with Vesicoureteral Reflux (RIVUR) trial and to compare our study cohort with those from previously published studies.

Published
2013
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26. The FOPE II Commentary

Author

Chesney, R. W., primary, Simon, J., additional, Alden, E., additional, Gruskin, A., additional, and Williams, R., additional

Published
2001
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