Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency

August 06-August 08

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http://doi.ieeecomputersociety.org/10.1109/SNPD.2008.132

2008 Ninth ACIS International Confere ...

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	ASCII Text	x
	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	x
	@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	x
	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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