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  <doc>
    <id>6724</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-02-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimal Design of Experiments for Estimating the Time of Death in Forensic Medicine</title>
    <abstract language="eng">Estimation of time of death based on a single measurement of body&#13;
 core temperature is a standard procedure in forensic medicine. &#13;
Mechanistic models using simulation of heat transport promise &#13;
higher accuracy than established phenomenological models in &#13;
particular in nonstandard situations,  but involve many not exactly &#13;
known physical parameters. Identifying both time of death and &#13;
physical parameters from multiple temperature measurements is &#13;
one possibility to reduce the uncertainty significantly. &#13;
&#13;
In this paper, we consider the inverse problem in a Bayesian setting &#13;
and perform both local and sampling-based uncertainty &#13;
quantification, where proper orthogonal decomposition is used as &#13;
model reduction for fast solution of the forward model. Based on &#13;
the local uncertainty quantification, optimal  design of experiments &#13;
is performed in order to minimize the uncertainty in the time of &#13;
death estimate for a given number of measurements. For  reasons &#13;
of practicability, temperature acquisition points are selected from &#13;
a set of candidates  in different spatial and temporal locations. &#13;
Applied to a real corpse model, a significant accuracy improvement &#13;
is obtained already with a small number of measurements.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-67247</identifier>
    <enrichment key="SourceTitle">Inverse Problems 34, 125005, 2018.  DOI 10.1088/1361-6420/aae7a5</enrichment>
    <author>Martin Weiser</author>
    <submitter>Bodo Erdmann</submitter>
    <author>Yvonne Freytag</author>
    <author>Bodo Erdmann</author>
    <author>Michael Hubig</author>
    <author>Gita Mall</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-08</number>
    </series>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="projects" number="UJena-Forensic">UJena-Forensic</collection>
    <collection role="projects" number="ZIB-Kaskade7">ZIB-Kaskade7</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6724/ZR-18-08.pdf</file>
  </doc>
</export-example>
