Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency
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 prim...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 37 |
|---|---|
| container_issue | |
| container_start_page | 29 |
| container_title | |
| container_volume | |
| creator | Sweeney, S.P. Sehwan Yoo Chi, A. Lin, F. Taikyeong Jeong Sengphil Hong Fernald, S. |
| description | 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. |
| doi_str_mv | 10.1109/SNPD.2008.132 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4617344</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4617344</ieee_id><sourcerecordid>4617344</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-d488e0ef5c9741b1ab15fd397a7f411e009c9038377d79aa3722b3b15d0231893</originalsourceid><addsrcrecordid>eNpVjMFKw0AURQekoNYsXbmZH0idNy_JzCyltVYIsdiKyzLJvMhIEuskFfL3RnTj3Vwu53AZuwaxABDmdldsVwsphF4AyjMWGaWFykyKMkMzY5c_yMhMQHrOor5_F1PQTExfsOeCTsE2vKCBv9qBAs_pixq-D9Q5vg3kfDX4j47baa7Gzra--ifubHtsfPfG14E-T9RV4xWb1bbpKfrrOXtZ3--Xmzh_enhc3uWxB5UOsUu0JkF1WhmVQAm2hLR2aJRVdQJAQpjKCNSolFPGWlRSljhJTkgEbXDObn5_PREdjsG3NoyHJAOFSYLflOpOtA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Sweeney, S.P. ; Sehwan Yoo ; Chi, A. ; Lin, F. ; Taikyeong Jeong ; Sengphil Hong ; Fernald, S.</creator><creatorcontrib>Sweeney, S.P. ; Sehwan Yoo ; Chi, A. ; Lin, F. ; Taikyeong Jeong ; Sengphil Hong ; Fernald, S.</creatorcontrib><description>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.</description><identifier>ISBN: 9780769532639</identifier><identifier>ISBN: 0769532632</identifier><identifier>DOI: 10.1109/SNPD.2008.132</identifier><identifier>LCCN: 2008926015</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural network ; Artificial neural networks ; Digital filters ; Java ; Sensors ; Time series analysis ; Trend prediction ; Turning ; Water level prediction ; Wireless sensor networks</subject><ispartof>2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008, p.29-37</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4617344$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4617344$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Sweeney, S.P.</creatorcontrib><creatorcontrib>Sehwan Yoo</creatorcontrib><creatorcontrib>Chi, A.</creatorcontrib><creatorcontrib>Lin, F.</creatorcontrib><creatorcontrib>Taikyeong Jeong</creatorcontrib><creatorcontrib>Sengphil Hong</creatorcontrib><creatorcontrib>Fernald, S.</creatorcontrib><title>Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency</title><title>2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing</title><addtitle>SNPD</addtitle><description>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.</description><subject>Artificial neural network</subject><subject>Artificial neural networks</subject><subject>Digital filters</subject><subject>Java</subject><subject>Sensors</subject><subject>Time series analysis</subject><subject>Trend prediction</subject><subject>Turning</subject><subject>Water level prediction</subject><subject>Wireless sensor networks</subject><isbn>9780769532639</isbn><isbn>0769532632</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVjMFKw0AURQekoNYsXbmZH0idNy_JzCyltVYIsdiKyzLJvMhIEuskFfL3RnTj3Vwu53AZuwaxABDmdldsVwsphF4AyjMWGaWFykyKMkMzY5c_yMhMQHrOor5_F1PQTExfsOeCTsE2vKCBv9qBAs_pixq-D9Q5vg3kfDX4j47baa7Gzra--ifubHtsfPfG14E-T9RV4xWb1bbpKfrrOXtZ3--Xmzh_enhc3uWxB5UOsUu0JkF1WhmVQAm2hLR2aJRVdQJAQpjKCNSolFPGWlRSljhJTkgEbXDObn5_PREdjsG3NoyHJAOFSYLflOpOtA</recordid><startdate>200808</startdate><enddate>200808</enddate><creator>Sweeney, S.P.</creator><creator>Sehwan Yoo</creator><creator>Chi, A.</creator><creator>Lin, F.</creator><creator>Taikyeong Jeong</creator><creator>Sengphil Hong</creator><creator>Fernald, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200808</creationdate><title>Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency</title><author>Sweeney, S.P. ; Sehwan Yoo ; Chi, A. ; Lin, F. ; Taikyeong Jeong ; Sengphil Hong ; Fernald, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d488e0ef5c9741b1ab15fd397a7f411e009c9038377d79aa3722b3b15d0231893</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial neural network</topic><topic>Artificial neural networks</topic><topic>Digital filters</topic><topic>Java</topic><topic>Sensors</topic><topic>Time series analysis</topic><topic>Trend prediction</topic><topic>Turning</topic><topic>Water level prediction</topic><topic>Wireless sensor networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Sweeney, S.P.</creatorcontrib><creatorcontrib>Sehwan Yoo</creatorcontrib><creatorcontrib>Chi, A.</creatorcontrib><creatorcontrib>Lin, F.</creatorcontrib><creatorcontrib>Taikyeong Jeong</creatorcontrib><creatorcontrib>Sengphil Hong</creatorcontrib><creatorcontrib>Fernald, S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sweeney, S.P.</au><au>Sehwan Yoo</au><au>Chi, A.</au><au>Lin, F.</au><au>Taikyeong Jeong</au><au>Sengphil Hong</au><au>Fernald, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency</atitle><btitle>2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing</btitle><stitle>SNPD</stitle><date>2008-08</date><risdate>2008</risdate><spage>29</spage><epage>37</epage><pages>29-37</pages><isbn>9780769532639</isbn><isbn>0769532632</isbn><abstract>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.</abstract><pub>IEEE</pub><doi>10.1109/SNPD.2008.132</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9780769532639 |
| ispartof | 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008, p.29-37 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_4617344 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural network Artificial neural networks Digital filters Java Sensors Time series analysis Trend prediction Turning Water level prediction Wireless sensor networks |
| title | Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T06%3A14%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Neural%20Net%20Water%20Level%20Trend%20Prediction%20and%20Dynamic%20Water%20Level%20Sampling%20Frequency&rft.btitle=2008%20Ninth%20ACIS%20International%20Conference%20on%20Software%20Engineering,%20Artificial%20Intelligence,%20Networking,%20and%20Parallel/Distributed%20Computing&rft.au=Sweeney,%20S.P.&rft.date=2008-08&rft.spage=29&rft.epage=37&rft.pages=29-37&rft.isbn=9780769532639&rft.isbn_list=0769532632&rft_id=info:doi/10.1109/SNPD.2008.132&rft_dat=%3Cieee_6IE%3E4617344%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4617344&rfr_iscdi=true |