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<dc:title xml:lang="pl"><![CDATA[Configuring a sensor network for fault detection in distributed parameter systems]]></dc:title>
<dc:creator><![CDATA[Patan, Maciej (1975- )]]></dc:creator>
<dc:creator><![CDATA[Uciński, Dariusz]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[branch-and-bound]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[constrained experimental design]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[distributed parameter systems]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[fault detection]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[parameter estimation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[sensor location]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset can be selected because of the cost constraints. As a suitable performance measure, the Ds-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. Then, the solution of a resulting combinatorial problem is determined based on the branch-and-bound method. As its essential part, a relaxed problem is discussed in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gauged sites.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The concavity and differentiability properties of the criterion are established and a gradient projection algorithm is proposed to perform the search for the optimal solution. The delineated approach is illustrated by a numerical example on a sensor network design for a two-dimensional convective diffusion process.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Sauter, Dominique - ed.]]></dc:contributor>
<dc:date><![CDATA[2008]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/78738/AMCS_2008_18_4_8.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/88510/edition/78738/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:78738]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 18, number 4 (2008)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=34]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:88510]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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