Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Wiesbaden
Springer Fachmedien Wiesbaden
2018
|
| Schriftenreihe: | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart
|
| Schlagworte: | |
| Online-Zugang: | BTU01 FAW01 FHA01 FHI01 FHM01 FHN01 FHR01 FKE01 FLA01 FRO01 FWS01 FWS02 HTW01 TUM01 UBY01 Volltext |
| Beschreibung: | 1 Online-Ressource (XXXII, 166 p. 34 illus) |
| ISBN: | 9783658203672 |
| DOI: | 10.1007/978-3-658-20367-2 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV044702639 | ||
| 003 | DE-604 | ||
| 005 | 20180423 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180108s2018 |||| o||u| ||||||eng d | ||
| 020 | |a 9783658203672 |c Online |9 978-3-658-20367-2 | ||
| 024 | 7 | |a 10.1007/978-3-658-20367-2 |2 doi | |
| 035 | |a (ZDB-2-ENG)9783658203672 | ||
| 035 | |a (OCoLC)1018471067 | ||
| 035 | |a (DE-599)BVBBV044702639 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-861 |a DE-1046 |a DE-860 |a DE-91 |a DE-Aug4 |a DE-898 |a DE-M347 |a DE-573 |a DE-523 |a DE-859 |a DE-863 |a DE-706 |a DE-634 |a DE-862 |a DE-92 | ||
| 082 | 0 | |a 629.2 |2 23 | |
| 084 | |a MAS 000 |2 stub | ||
| 084 | |a ELT 000 |2 stub | ||
| 100 | 1 | |a Bergmeir, Philipp |e Verfasser |0 (DE-588)1152097113 |4 aut | |
| 245 | 1 | 0 | |a Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |c by Philipp Bergmeir |
| 264 | 1 | |a Wiesbaden |b Springer Fachmedien Wiesbaden |c 2018 | |
| 300 | |a 1 Online-Ressource (XXXII, 166 p. 34 illus) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Pattern recognition | |
| 650 | 4 | |a Automotive engineering | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Automotive Engineering | |
| 650 | 4 | |a Data Mining and Knowledge Discovery | |
| 650 | 4 | |a Pattern Recognition | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-3-658-20366-5 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-658-20367-2 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-ENG | ||
| 940 | 1 | |q ZDB-2-ENG_2018 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030099313 | ||
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l BTU01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FAW01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FHA01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FHI01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FHM01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FHN01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FHR01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FKE01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FLA01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FRO01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FWS01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l FWS02 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l HTW01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l TUM01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-658-20367-2 |l UBY01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
Datensatz im Suchindex
| DE-BY-FWS_katkey | 676445 |
|---|---|
| _version_ | 1824554648926683136 |
| any_adam_object | |
| author | Bergmeir, Philipp |
| author_GND | (DE-588)1152097113 |
| author_facet | Bergmeir, Philipp |
| author_role | aut |
| author_sort | Bergmeir, Philipp |
| author_variant | p b pb |
| building | Verbundindex |
| bvnumber | BV044702639 |
| classification_tum | MAS 000 ELT 000 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)9783658203672 (OCoLC)1018471067 (DE-599)BVBBV044702639 |
| dewey-full | 629.2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.2 |
| dewey-search | 629.2 |
| dewey-sort | 3629.2 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik Verkehr / Transport Maschinenbau |
| doi_str_mv | 10.1007/978-3-658-20367-2 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03182nmm a2200649zc 4500</leader><controlfield tag="001">BV044702639</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20180423 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180108s2018 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783658203672</subfield><subfield code="c">Online</subfield><subfield code="9">978-3-658-20367-2</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-3-658-20367-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-ENG)9783658203672</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1018471067</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV044702639</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-861</subfield><subfield code="a">DE-1046</subfield><subfield code="a">DE-860</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-859</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-634</subfield><subfield code="a">DE-862</subfield><subfield code="a">DE-92</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">629.2</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAS 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ELT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bergmeir, Philipp</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1152097113</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data</subfield><subfield code="c">by Philipp Bergmeir</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Wiesbaden</subfield><subfield code="b">Springer Fachmedien Wiesbaden</subfield><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XXXII, 166 p. 34 illus)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automotive engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automotive Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data Mining and Knowledge Discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern Recognition</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-3-658-20366-5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-ENG_2018</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030099313</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FAW01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FHA01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FHI01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FHM01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FHR01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FKE01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FLA01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FRO01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FWS01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">FWS02</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">HTW01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-658-20367-2</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV044702639 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-20T06:55:07Z |
| institution | BVB |
| isbn | 9783658203672 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030099313 |
| oclc_num | 1018471067 |
| open_access_boolean | |
| owner | DE-861 DE-1046 DE-860 DE-91 DE-BY-TUM DE-Aug4 DE-898 DE-BY-UBR DE-M347 DE-573 DE-523 DE-859 DE-863 DE-BY-FWS DE-706 DE-634 DE-862 DE-BY-FWS DE-92 |
| owner_facet | DE-861 DE-1046 DE-860 DE-91 DE-BY-TUM DE-Aug4 DE-898 DE-BY-UBR DE-M347 DE-573 DE-523 DE-859 DE-863 DE-BY-FWS DE-706 DE-634 DE-862 DE-BY-FWS DE-92 |
| physical | 1 Online-Ressource (XXXII, 166 p. 34 illus) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2018 |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Springer Fachmedien Wiesbaden |
| record_format | marc |
| series2 | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart |
| spellingShingle | Bergmeir, Philipp Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data Engineering Data mining Pattern recognition Automotive engineering Automotive Engineering Data Mining and Knowledge Discovery Pattern Recognition |
| title | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |
| title_auth | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |
| title_exact_search | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |
| title_full | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data by Philipp Bergmeir |
| title_fullStr | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data by Philipp Bergmeir |
| title_full_unstemmed | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data by Philipp Bergmeir |
| title_short | Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |
| title_sort | enhanced machine learning and data mining methods for analysing large hybrid electric vehicle fleets based on load spectrum data |
| topic | Engineering Data mining Pattern recognition Automotive engineering Automotive Engineering Data Mining and Knowledge Discovery Pattern Recognition |
| topic_facet | Engineering Data mining Pattern recognition Automotive engineering Automotive Engineering Data Mining and Knowledge Discovery Pattern Recognition |
| url | https://doi.org/10.1007/978-3-658-20367-2 |
| work_keys_str_mv | AT bergmeirphilipp enhancedmachinelearninganddataminingmethodsforanalysinglargehybridelectricvehiclefleetsbasedonloadspectrumdata |