<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>487</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Energiebestimmung an Brettsperrholzbauteilen im Holzmassivbau</title>
    <abstract language="deu">Ein Ziel des DFG-AiF-Clusterforschungsvorhabens Vibroakustik im Planungsprozess für Holzbauten ist die Reduktion des schalltechnischen Planungsaufwandes bei der Holzmassivbauweise. Hierzu soll das Prognoseverfahren der DIN EN 12354 angepasst werden. Darin ist ein vereinfachter SEA-Ansatz zugrunde gelegt, welcher diffuse Körperschallfelder in den Bauteilen sowie deren schwache Kopplung voraussetzt. Tatsächlich trifft die Annahme diffuser Körperschallfelder nur bei Bauteilen mit geringer Ausbreitungsdämpfung zu. Um ein Maß für die Kopplung der Bauteile zu erhalten, wird ein Energieverhältnis aus den Bauteilschnellen gebildet. In der DIN EN ISO 10848-1 wird eine Vorschrift für die Messung der mittleren Bauteilschnelle beschrieben. Daraus wird ein örtlich und zeitlich gemittelter Schnellepegel je Bauteil berechnet, mit dem die Energie des Bauteils bestimmt wird. In diesem Beitrag werden Messergebnisse von Körperschallfeldern an Brettsperrholzelementen im Hinblick auf die Energiebestimmung diskutiert. Bei der statistischen Auswertung werden die Anzahl der Anregepositionen und der Messpositionen auf dem direkt und indirekt angeregten Bauteil analysiert. In einem weiteren Schritt werden die erforderlichen Messpositionen durch eine geeignete Auswahl reduziert und mit den pauschalen, geometrischen Vorgaben der Messvorschrift aus der DIN EN ISO 10848-1 verglichen. Die Erkenntnisse werden zur Bewertung von Messergebnissen und für Empfehlungen an die Durchführung zukünftiger Messungen herangezogen.</abstract>
    <parentTitle language="deu">Deutsche Gesellschaft für Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2017, 43. Deutsche Jahrestagung für Akustik, Kiel, Deutschland</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Simon Mecking</author>
    <author>Markus Scheibengraber</author>
    <author>Tobias Kruse</author>
    <author>Ulrich Schanda</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Holzmassivbau</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Schallmesstechnik</value>
    </subject>
    <collection role="ddc" number="600">Technik, Technologie</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>787</id>
    <completedYear>2016</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Statistische Datenanalyse und Modellierung - Einsatzmöglichkeiten von Machine Learning Verfahren. Forschungsbericht 2016 Hochschule Rosenheim</title>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>V. Sandor</author>
    <author>D. Schalk</author>
    <author>M. Sussmann</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Statistische Datenanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Statistische Modellierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>773</id>
    <completedYear>2016</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>contributiontoperiodical</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Anwendung von maschinellem Lernen in der Tarifierung</title>
    <parentTitle language="deu">Der Aktuar</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>C. Frey</author>
    <author>F. Schönfelder</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Aktuarwissenschaften</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>774</id>
    <completedYear>2011</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An approach  for a statistical model for the user behaviour regarding window ventilation in residential buildings</title>
    <parentTitle language="eng">Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>F. Antretter</author>
    <author>C. Mayer</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Window Ventilation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Residential Buildings</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Building Simulation</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>775</id>
    <completedYear>2010</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>contributiontoperiodical</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Untersuchung der Eignung verschiedener Fassaden für automatisierte Fensterlüftung in Schulen mit einem Entscheidungsbaumverfahren</title>
    <abstract language="deu">Sollen Klassenräume über Fenster belüftet werden, kann dies über Stoßlüftung oder genau dosierte Dauerlüftung erfolgen. Messungen verschiedener Institutionen zeigen, dass die erforderliche regelmäßige Stoßlüftung in Schulen nicht praktiziert wird. &#13;
&#13;
Damit eine Dauerlüftung aber funktionieren kann, muss ermittelt werden, welcher Fenstertyp bei welcher Öffnungsweite bei behaglichen thermischen Verhältnissen im Raum den notwendigen Luftwechsel gewährleistet. Deshalb wurden an einem Freilandversuchsstand des Fraunhofer‐Instituts für Bauphysik Schwingflügel und Kippflügel auf ihre Eignung zur Dauerlüftung in Klassenräumen untersucht. &#13;
&#13;
&#13;
Da sich bei den vielen Einflussfaktoren auf die freie Lüftung die deskriptive Analyse oft als sehr schwierig erweist, wird ein besonderes statistisches Verfahren, ein Entscheidungsbaumverfahren verwendet, das sowohl hinsichtlich der Auswahl der Einflussgrößen und der Modellüberanpassung sehr günstige statistischmethodische Eigenschaften besitzt. &#13;
&#13;
Mit diesem Verfahren können Einflussgrößen mit unterschiedlichen Skalenniveaus adäquat in die Auswertung einbezogen werden. Dabei erhält man ein intuitiv interpretierbares Regelwerk, welches dem Anwender einen erklärenden Einblick in die Zusammenhangsstruktur des untersuchten Prozesses liefert.</abstract>
    <parentTitle language="deu">Bauphysik</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>S. Steiger</author>
    <author>Ulrich Wellisch</author>
    <author>R. T. Hellwig</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fensterlüftung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Schulbelüftung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Raumthermik</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>777</id>
    <completedYear>1997</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Asymptotic behaviour of estimation equations with functional nuisance or working parameter.</title>
    <abstract language="eng">We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing with estimation equations which have a parametric component of interest and a functional (nonparametric) nuisance component. &#13;
We give sufficient conditions for the existence and the asymptotic normality of a consistent estimation equation estimator for the parameter of interest. &#13;
&#13;
These conditions concern the asymptotic distribution of the estimation function and of its derivative as well as the effect of the functional nuisance part in the estimation equation. In order to treat the nonparametric component we introduce a general differential calculus and a general mean value theorem. For the nonparametric part in the estimation equation we distinguish two cases: the situation of a (classical) nuisance parameter and the case of a so called working parameter. &#13;
&#13;
As a special case we get regularity conditions for estimation equations with finite dimensional nuisance or working parameter. As an example we present the semiparametric linear regression model.</abstract>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>H. Pruschka</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Estimating equations</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Asymptotic distribution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>semiparametric linear regression model</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>778</id>
    <completedYear>1997</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Asymptotics for generalized linear segmented regression models with an unknown breakpoint</title>
    <abstract language="eng">We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given. &#13;
&#13;
The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods. &#13;
&#13;
An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results.</abstract>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>H. Küchenhoff</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Asymptotic theory</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear segmentet regression models</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>781</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>43</pageFirst>
    <pageLast>50</pageLast>
    <pageNumber/>
    <edition>23</edition>
    <issue>1</issue>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Erfolg der Privatisierung von Krankenhäusern – Eine Einschätzung aus der Sicht ehemaliger öffentlicher Krankenhausträger</title>
    <title language="eng">Success of hospital privatisations – an assessment from the point of view of former public hospital owners</title>
    <abstract language="deu">Zielsetzung &#13;
In den letzten Jahren konnte eine Steigerung der Anzahl privater Krankenhäuser und Betten festgestellt werden. Die Probleme der Investitionsfinanzierung deutscher Krankenhäuser in Kombination mit verstärkenden Faktoren wie der Einführung der DRGs, aber auch die Erwartung einer höheren Effizienz nach der Privatisierung einer Klinik können als Hauptgründe bei Privatisierungsentscheidungen genannt werden. Es soll untersucht werden, wie ehemalige öffentliche Krankenhausträger den Erfolg der Privatisierung einschätzen.&#13;
&#13;
Methodik &#13;
Alle 99 identifizierten ehemaligen öffentlichen Träger, die ihr Krankenhaus in den Jahren 2003–2013 an einen privaten Eigentümer verkauft hatten, wurden hinsichtlich ihrer Einschätzung von Zielen sowie Befürchtungen vor und nach der Privatisierung anonym und schriftlich befragt.&#13;
&#13;
Ergebnisse und Schlussfolgerung &#13;
Zusammenfassend lässt sich feststellen, dass bei einer ausreichenden Finanzierung der Krankenhäuser aus Krankenkassenbeiträgen (Betriebsmittel) und Ländermittel (Investitionskosten) der Privatisierungsdruck rapide sinken würde. Wenn jedoch privatisiert wurde, hatte dies aus Sicht der ehemaligen Träger weder einen negativen Einfluss auf die medizinische Versorgung in der Region, noch auf die Qualität der medizinischen Behandlung und auf den Umgang mit den Mitarbeitern. &#13;
Die Einschätzungen der ehemaligen Träger weisen eine statistisch überprüfte Übereinstimmung auf. Die mit einer Privatisierung verbundenen Erwartungen und Ziele werden prinzipiell als erfüllt wahrgenommen</abstract>
    <abstract language="eng">Aim&#13;
In recent years, an increase in the number of private hospitals and beds could be determined. The problems of financing investments of German hospitals in combination with other reinforcing factors such as the introduction of DRGs, but also assumptions to higher efficiency after privatisation of a hospital can be identified as the main reasons for privatisation decisions. It should be examined how former public hospital operators assess the success of the privatisation.&#13;
&#13;
Method&#13;
All 99 identified former public owners who had sold their hospitals during the years 2003 to 2013 to a private owner were interviewed anonymously and in written form with regard to their assessment of goals and fears before and after privatisation.&#13;
&#13;
Results and Conclusion&#13;
In summary it can be said that with sufficient funding of hospitals from health insurance contributions (resources) and state funds (investment costs) privatisation pressure would fall sharply. But if privatized once, there was - from the view of the former public owners - neither a negative impact on medical care in the region, nor on the quality of medical treatment and the treatment of employees. The former public owners show a fair agreement on their assesments. The privatisations answer the expectations and the goals of the former public owners.</abstract>
    <parentTitle language="deu">Gesundheitsökonomie &amp; Qualitätsmanagement</parentTitle>
    <identifier type="url">https://doi.org/10.1055/s-0043-110761</identifier>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <author>Daniel Negele</author>
    <author>Robert Ott</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Privatisierung von Krankenhäusern</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Gesundheitspolitik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Krankenhausfinanzierung</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hospital privatisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>health policy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hospital financing</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Gesundheits- und Sozialwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>783</id>
    <completedYear>2016</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-05-08</completedDate>
    <publishedDate>2018-06-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Erarbeitung von Prüfverfahren zur Vereinheitlichung der energetischen Bewertung von großformatigen, transparenten Bausystemen unter in situ Randbedingungen. Teilprojekt Hochschule Rosenheim: Analyseverfahren und Mitarbeit im IEA ECBCS Annex 58 „Reliable building performande characterisation and prediction based on full scale dynamic measurements“, Hochschule Rosenheim, 2016</title>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bausysteme</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Energetische Bewertung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>in situ Randbedingungen</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>784</id>
    <completedYear>2014</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">In situ Prüfverfahren für transparente Bauteile</title>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>L. Bauer</author>
    <author>F. Feldmeier</author>
    <author>F. Paulus</author>
    <author>M. Schreyer</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>transparente Bauteile</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>in situ Prüfverfahren</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>785</id>
    <completedYear>2015</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Statistische Datenanalyse und Modellierung - Risikomodellierung in der Krafthaftpflichtversicherung. Forschungsbericht Hochschule Rosenheim 2015</title>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>D. Sebald</author>
    <author>M. Sussmann</author>
    <author>V. Sandor</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Statistische Datenanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Risikomodellierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kfz- Haftpflichtversicherung</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>786</id>
    <completedYear>2016</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Statistische Datenanalyse und Modellierung -  Machine Learning in der Versicherung, Risikomodelle auf dem Prüfstand. Forschungsbericht 2016 Hochschule Rosenheim</title>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>V. Sandor</author>
    <author>D. Schalk</author>
    <author>M. Sussmann</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Statistische Datenanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Risikomodellierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1675</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>22005</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>172</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions</title>
    <abstract language="eng">Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory.</abstract>
    <parentTitle language="eng">E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)</parentTitle>
    <identifier type="url">https://doi.org/10.1051/e3sconf/202017222005</identifier>
    <enrichment key="RS_Acronym">In Situ Nachweisverfahren</enrichment>
    <enrichment key="RS_ProjectTitle">Montag, 12. April 2021 12:14  ENOB: InsituNachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex 71</enrichment>
    <enrichment key="RS_FundingAgency">Bundesministerium für Wirtschaft und Energie</enrichment>
    <enrichment key="RS_GrantNumber">03ET1509C</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Lucia Hanfstaengl</author>
    <author>Michael Parzinger</author>
    <author>Uli Spindler</author>
    <author>Ulrich Wellisch</author>
    <author>Markus Wirnsberger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>occupancy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>maschine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>random forest</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="ddc" number="53">Physik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1673</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6758</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>17</issue>
    <volume>12</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems</title>
    <abstract language="eng">Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.</abstract>
    <parentTitle language="eng">Sustainability</parentTitle>
    <identifier type="url">https://doi.org/10.3390/su12176758</identifier>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <enrichment key="RS_Acronym">In Situ Nachweisverfahren</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="RS_ProjectTitle">ENOB: In Situ Nachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex71</enrichment>
    <enrichment key="RS_FundingAgency">Bundesministerium für Wirtschaft und Energie</enrichment>
    <enrichment key="RS_GrantNumber">03ET1509C</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Michael Parzinger</author>
    <author>Lucia Hanfstaengl</author>
    <author>Ferdinand Sigg</author>
    <author>Uli Spindler</author>
    <author>Ulrich Wellisch</author>
    <author>Markus Wirnsberger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fault detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HVAC</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>residual analysis</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="ddc" number="53">Physik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1674</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>22001</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>172</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identifying faults in the building system based on model prediction and residuum analysis</title>
    <abstract language="eng">The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.</abstract>
    <parentTitle language="eng">E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)</parentTitle>
    <identifier type="url">https://doi.org/10.1051/e3sconf/202017222001</identifier>
    <enrichment key="RS_Acronym">Insitu Nachweisverfahren</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="RS_ProjectTitle">ENOB: InsituNachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex 71</enrichment>
    <enrichment key="RS_FundingAgency">Bundesministerium für Wirtschaft und Energie</enrichment>
    <enrichment key="RS_GrantNumber">03ET1509C</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Michael Parzinger</author>
    <author>Ulrich Wellisch</author>
    <author>Lucia Hanfstaengl</author>
    <author>Ferdinand Sigg</author>
    <author>Markus Wirnsberger</author>
    <author>Uli Spindler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fault detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HVAC</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>residuum analysis</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>2064</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>109313</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>223</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO</title>
    <abstract language="eng">The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 % and a specificity of at least 97 % with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable</abstract>
    <parentTitle language="eng">Building and Environment</parentTitle>
    <identifier type="doi">10.1016/j.buildenv.2022.109313</identifier>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <enrichment key="RS_Acronym">In Situ Nachweisverfahren</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="RS_ProjectTitle">ENOB: In Situ Nachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex71</enrichment>
    <enrichment key="RS_FundingAgency">Bundesministerium für Wirtschaft und Energie</enrichment>
    <enrichment key="RS_GrantNumber">03ET1509C</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Michael Parzinger</author>
    <author>Lucia Hanfstaengl</author>
    <author>Ferdinand Sigg</author>
    <author>Uli Spindler</author>
    <author>Ulrich Wellisch</author>
    <author>Markus Wirnsberger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Simulated learning data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Occupancy detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random Forest</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LASSO</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>488</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencepaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Experimentally based statistical analysis of the vibrational energy of CLT building elements</title>
    <abstract language="eng">The method is based on a simpliﬁed Statistical Energy Analysis(SEA) approach. The energy ratios of various subsystems are the main quantities to predict sound transmission. The method has proven to work sufﬁciently accurate for masonry and concrete buildings, where the building components like walls, ﬂoors etc. can be regarded as rather homogeneous structures. To adapt this method for solid timber constructions it is necessary to prove that basic SEA requirements are fullﬁlled by orthotropic materials and heterogeneous structures that occur in these building types. In a case study, an isolated T-junction formed by Cross Laminated Timber(CLT) elements is experimentally investigated. The buildings elements are subdivided in segments with typical screwed connections. In the experiments the diffusivity of the vibration ﬁeld is investigated, using point excitation with a shaker at several positions and many, randomly chosen response positions. MONTE-CARLO simulations are conducted for random but ﬁx-sized subsets of the measured response positions to ﬁnd the necessary number of response positions for an accurate determination of the vibrational energy. As a result the distribution of the mean velocity levels can be approximated. In a second approach a multiple linear regression model based on the least absolute shrinkage and selection operator(LASSO)is applied, because for lower frequency bands multicollinearity is expected. In the context of linear regression modelling, data-driven methods are used to select optimal subsets of response positions, to get an estimating equation.</abstract>
    <parentTitle language="eng">24th International Congress on Sound and Vibration, ICSV24, London, United Kingdom, July 2017</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:861-opus4-4883</identifier>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Simon Mecking</author>
    <author>Markus Scheibengraber</author>
    <author>Tobias Kruse</author>
    <author>Ulrich Schanda</author>
    <author>Ulrich Wellisch</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Schallmesstechnik</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CLT building</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>statistical energy analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cross-laminated timber</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vibrational energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>least absolute shrinkage and selection operator</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monte-Carlo</value>
    </subject>
    <collection role="ddc" number="600">Technik, Technologie</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-rosenheim/files/488/Mecking2017ICSV.pdf</file>
  </doc>
</export-example>
