<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1550</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>000199</pageFirst>
    <pageLast>000206</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic Data</title>
    <parentTitle language="eng">2018 IEEE International Conference on Intelligent Engineering Systems (INES)</parentTitle>
    <identifier type="isbn">978-1-5386-1122-7</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/INES.2018.8523987</enrichment>
    <enrichment key="THI_conferenceName">2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), Las Palmas de Gran Canaria (Spain), 21.-23.06.2018</enrichment>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Ziegmann</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Driver Energy Prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental Feature Selection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensitivity Analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feature Transformation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Filter and Wrapper Methods</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>1559</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>517</pageFirst>
    <pageLast>522</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of individual driver velocity prediction using data-driven driver models with environmental features</title>
    <parentTitle language="eng">2017 IEEE Intelligent Vehicles Symposium</parentTitle>
    <identifier type="isbn">978-1-5090-4804-5</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach (USA), 11.-14.06.2017</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IVS.2017.7995770</enrichment>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Ziegmann</last_name>
    </author>
    <author>
      <first_name>Jieqing</first_name>
      <last_name>Shi</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Schnörer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>predictive models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>training</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>prediction algorithms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>kernel</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>navigation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>biological system modeling</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>1561</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Driver adaptive predictive velocity control</title>
    <parentTitle language="eng">2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">978-1-5386-1526-3</identifier>
    <identifier type="issn">2153-0017</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama (Japan), 16.-19.10.2017</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC.2017.8317668</enrichment>
    <author>
      <first_name>Ulrich</first_name>
      <last_name>Vögele</last_name>
    </author>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Ziegmann</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Predictive Velocity Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Longitudinal Vehicle Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Driver Adpative</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-Criteria Optimization</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>1631</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue>42</issue>
    <volume>2016</volume>
    <articleNumber>01002</articleNumber>
    <type>article</type>
    <publisherName>EDP Sciences</publisherName>
    <publisherPlace>Les Ulis</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimization of inverse model identification for multi-axial test rig control</title>
    <abstract language="deu">Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO) model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.</abstract>
    <parentTitle language="eng">MATEC Web of Conferences</parentTitle>
    <identifier type="issn">2261-236X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-16314</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1051/matecconf/20164201002</enrichment>
    <enrichment key="THI_conferenceName">2015 The 3rd International Conference on Control, Mechatronics and Automation (ICCMA 2015), Barcelona (Spain), 21.-22.12.2015</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Tino</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Ziegmann</last_name>
    </author>
    <author>
      <first_name>Simon</first_name>
      <last_name>Krüner</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1631/matecconf_iccma2016_01002.pdf</file>
  </doc>
  <doc>
    <id>1545</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1807</pageFirst>
    <pageLast>1814</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Stochastic Driver Velocity Prediction with Environmental Features on Naturalistic Driving Data</title>
    <parentTitle language="eng">2018 IEEE Intelligent Transportation Systems Conference</parentTitle>
    <identifier type="isbn">978-1-7281-0323-5</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC.2018.8569767</enrichment>
    <enrichment key="THI_conferenceName">2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui (USA), 04.-07.11.2018</enrichment>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Ziegmann</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Denk</last_name>
    </author>
    <author>
      <first_name>Ulrich</first_name>
      <last_name>Vögele</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Velocity prediction</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>driver behavior modeling</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>energy prediction</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kalman filter</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Particle filter</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>switching hidden Markov model</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
</export-example>
