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<export-example>
  <doc>
    <id>1664</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>797</pageFirst>
    <pageLast>806</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-04-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures</title>
    <abstract language="eng">This paper presents a convolutional neural network (CNN)which can be used for forecasting electricity load profiles 36 hours intothe future. In contrast to well established CNN architectures, the inputdata is one-dimensional. A parameter scanning of network parameters isconducted in order to gain information about the influence of the kernelsize,  number  of  filters,  and  dense  size.  The  results  show  that  a  goodforecast quality can already be achieved with basic CNN architectures.The  method  works  not  only  for  smooth  sum  loads  of  many  hundredconsumers, but also for the load of apartment buildings</abstract>
    <parentTitle language="eng">International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain)</parentTitle>
    <identifier type="url">https://arxiv.org/abs/1911.11536</identifier>
    <identifier type="url">http://itise.ugr.es/ITISE2019_vol2.pdf</identifier>
    <identifier type="isbn">978-84-17970-78-9</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-16649</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Christian Lang</author>
    <author>Florian Steinborn</author>
    <author>Oliver Steffens</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy load forecasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>STLF</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>con-volutional networks</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
    <collection role="institutes" number="">Labor Bauphysik</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/1664/Electricity_Load_Forecasting.pdf</file>
  </doc>
  <doc>
    <id>2472</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>194</pageFirst>
    <pageLast>197</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Hybridizing EMD with cICA for fMRI Analysis of Patient Groups</title>
    <abstract language="eng">Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.</abstract>
    <parentTitle language="eng">2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany</parentTitle>
    <identifier type="doi">10.1109/EMBC.2019.8856355</identifier>
    <identifier type="pmid">31945876</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>S. Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>biomedical MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>independent component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>medical image processing</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5356</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Academic Press</publisherName>
    <publisherPlace>San Diego, CA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early meta-level: deeper understanding of connectivity-states and consequences for state definition</title>
    <parentTitle language="eng">21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii</parentTitle>
    <identifier type="doi">10.13140/RG.2.1.2561.3929</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Ana Maria Tomé</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
</export-example>
