Linked Life Data

18441432

Source:http://linkedlifedata.com/resource/pubmed/id/18441432

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2008-4-28
pubmed:abstractText
Important indicators for monitoring and control of wastewater treatment plants (WWTP) often have to be obtained from the processing of on-line signal trajectories. Therefore, the quality of sensor instantaneous measurements can be improved significantly if they are complemented with valuable information about the geometric features of their trajectories. The present paper describes the design and implementation of a Standard Signal Processing Architecture (SSPA) from which enriched sensor information is generated automatically. The SSPA has been made up of three complementary modules: the pre-processing module, the storage module and the post-processing module. Moreover, the SSPA has been parameterised so as to allow its adaptation to the specifications of every signal. By performing basic calculations on pre-processed signal trajectories, the storage module produces enriched vectors which collect information of the first and second time derivatives, average and variance values, peak values, linear regression parameters, curvature, etc. Then, the enriched information vectors can be exploited to implement customised monitoring and control tools. In this respect, the effectiveness of the SSPA has been demonstrated in three different practical cases: (1) OUR and KLa identification algorithms; (2) processing of measurements for real-time controllers; and, (3) detection of bend-points in on-line signals of SBR processes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0273-1223
pubmed:author
pubmed:copyrightInfo
Copyright IWA Publishing 2008.
pubmed:issnType
Print
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1053-60
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Standard signal processing using enriched sensor information for WWTP monitoring and control.
pubmed:affiliation
CEIT and TECNUN, University of Navarra, P de Manuel Lardizabal 15, 20018 San Sebastian, Spain. iirizar@ceit.es
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't