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2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency
August 06-August 08
ISBN: 978-0-7695-3263-9
ASCII Text
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Steven P. Sweeney, Sehwan Yoo, Albert Chi, Frank Lin, Taikyeong Jeong, Sengphil Hong, Sam Fernald, "Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, pp. 29-37, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008.
 
 
BibTex
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@article{ 10.1109/SNPD.2008.132,
author = {Steven P. Sweeney and Sehwan Yoo and Albert Chi and Frank Lin and Taikyeong Jeong and Sengphil Hong and Sam Fernald},
title = {Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {0},
year = {2008},
isbn = {978-0-7695-3263-9},
pages = {29-37},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2008.132},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency
SN - 978-0-7695-3263-9
SP29
EP37
A1 - Steven P. Sweeney,
A1 - Sehwan Yoo,
A1 - Albert Chi,
A1 - Frank Lin,
A1 - Taikyeong Jeong,
A1 - Sengphil Hong,
A1 - Sam Fernald,
PY - 2008
KW - Artificial neural network
KW - Trend prediction
KW - Water level prediction
VL - 0
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
We have used Neural Network Water Level Trend Prediction (NNWLTP) in support of a water level sensing project. The NNWLTP approach allows dynamic change in water level sampling frequency, which will reduce power consumption and extend battery life in energy constrained devices.??This paper deals primarily with the NNWLTP, which would allow sampling frequency change commands to be transmitted to the sensors when a transition or turning point was detected.
Index Terms:
Artificial neural network, Trend prediction, Water level prediction
Citation:
Steven P. Sweeney, Sehwan Yoo, Albert Chi, Frank Lin, Taikyeong Jeong, Sengphil Hong, Sam Fernald, "Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency," snpd, pp.29-37, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008
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