Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking: = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Würzburg
2020
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Online-Ressource |
| DOI: | 10.25972/OPUS-21595 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047120079 | ||
| 003 | DE-604 | ||
| 005 | 20210226 | ||
| 006 | a m||| 00||| | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 210202s2020 gw |||| o||u| ||||||eng d | ||
| 015 | |a 20,O12 |2 dnb | ||
| 016 | 7 | |a 1221527975 |2 DE-101 | |
| 024 | 7 | |a 10.25972/OPUS-21595 |2 doi | |
| 024 | 7 | |a urn:nbn:de:bvb:20-opus-215953 |2 urn | |
| 035 | |a (OCoLC)1237585484 | ||
| 035 | |a (DE-599)DNB1221527975 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BY | ||
| 049 | |a DE-20 | ||
| 084 | |a ST 302 |0 (DE-625)143652: |2 rvk | ||
| 084 | |8 2\p |a 004.6 |2 23ksdnb | ||
| 084 | |8 1\p |a 004 |2 23sdnb | ||
| 100 | 1 | |a Hofmann, Jan |e Verfasser |0 (DE-588)1226183840 |4 aut | |
| 245 | 1 | 0 | |a Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking |b = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |c vorgelegt von Jan Hofmann |
| 246 | 1 | 1 | |a Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| 264 | 1 | |a Würzburg |c 2020 | |
| 300 | |a Online-Ressource | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 502 | |b Bachelorarbeit |c Julius-Maximilians-Universität Würzburg |d 2020 | ||
| 583 | 1 | |a Archivierung/Langzeitarchivierung gewährleistet |5 DE-101 |2 pdager | |
| 650 | 0 | 7 | |a Echtzeitsystem |0 (DE-588)4131397-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bestärkendes Lernen |g Künstliche Intelligenz |0 (DE-588)4825546-4 |2 gnd |9 rswk-swf |
| 653 | |a Deep Reinforcement Learning | ||
| 653 | |a Time-Sensitive-Networking | ||
| 653 | |a Real-Time-Networks | ||
| 653 | |a Bestärkendes Lernen | ||
| 653 | |a Echtzeit-Netzwerke | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | 1 | |a Bestärkendes Lernen |g Künstliche Intelligenz |0 (DE-588)4825546-4 |D s |
| 689 | 0 | 2 | |a Echtzeitsystem |0 (DE-588)4131397-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 0 | |u https://doi.org/10.25972/OPUS-21595 |x Resolving-System |z kostenfrei |3 Volltext |
| 912 | |a ebook | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032526406 | ||
| 883 | 0 | |8 1\p |a aepsg |c 0,99756 |d 20201117 |q DE-101 |u https://d-nb.info/provenance/plan#aepsg | |
| 883 | 0 | |8 2\p |a aepkn |c 0,97399 |d 20201117 |q DE-101 |u https://d-nb.info/provenance/plan#aepkn | |
Datensatz im Suchindex
| _version_ | 1804182163681181696 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Hofmann, Jan |
| author_GND | (DE-588)1226183840 |
| author_facet | Hofmann, Jan |
| author_role | aut |
| author_sort | Hofmann, Jan |
| author_variant | j h jh |
| building | Verbundindex |
| bvnumber | BV047120079 |
| classification_rvk | ST 302 |
| collection | ebook |
| ctrlnum | (OCoLC)1237585484 (DE-599)DNB1221527975 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.25972/OPUS-21595 |
| format | Thesis Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02379nmm a2200589zc 4500</leader><controlfield tag="001">BV047120079</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20210226 </controlfield><controlfield tag="006">a m||| 00||| </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">210202s2020 gw |||| o||u| ||||||eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">20,O12</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1221527975</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.25972/OPUS-21595</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:bvb:20-opus-215953</subfield><subfield code="2">urn</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1237585484</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1221527975</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="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BY</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-20</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 302</subfield><subfield code="0">(DE-625)143652:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="8">2\p</subfield><subfield code="a">004.6</subfield><subfield code="2">23ksdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="8">1\p</subfield><subfield code="a">004</subfield><subfield code="2">23sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hofmann, Jan</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1226183840</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking</subfield><subfield code="b">= Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking</subfield><subfield code="c">vorgelegt von Jan Hofmann</subfield></datafield><datafield tag="246" ind1="1" ind2="1"><subfield code="a">Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Würzburg</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</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="502" ind1=" " ind2=" "><subfield code="b">Bachelorarbeit</subfield><subfield code="c">Julius-Maximilians-Universität Würzburg</subfield><subfield code="d">2020</subfield></datafield><datafield tag="583" ind1="1" ind2=" "><subfield code="a">Archivierung/Langzeitarchivierung gewährleistet</subfield><subfield code="5">DE-101</subfield><subfield code="2">pdager</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Echtzeitsystem</subfield><subfield code="0">(DE-588)4131397-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bestärkendes Lernen</subfield><subfield code="g">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4825546-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Deep Reinforcement Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Time-Sensitive-Networking</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Real-Time-Networks</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bestärkendes Lernen</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Echtzeit-Netzwerke</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4113937-9</subfield><subfield code="a">Hochschulschrift</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Bestärkendes Lernen</subfield><subfield code="g">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4825546-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Echtzeitsystem</subfield><subfield code="0">(DE-588)4131397-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.25972/OPUS-21595</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ebook</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032526406</subfield></datafield><datafield tag="883" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">aepsg</subfield><subfield code="c">0,99756</subfield><subfield code="d">20201117</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#aepsg</subfield></datafield><datafield tag="883" ind1="0" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">aepkn</subfield><subfield code="c">0,97399</subfield><subfield code="d">20201117</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#aepkn</subfield></datafield></record></collection> |
| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047120079 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:29:07Z |
| indexdate | 2024-07-10T09:03:11Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032526406 |
| oclc_num | 1237585484 |
| open_access_boolean | 1 |
| owner | DE-20 |
| owner_facet | DE-20 |
| physical | Online-Ressource |
| psigel | ebook |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| record_format | marc |
| spelling | Hofmann, Jan Verfasser (DE-588)1226183840 aut Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking vorgelegt von Jan Hofmann Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking Würzburg 2020 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Bachelorarbeit Julius-Maximilians-Universität Würzburg 2020 Archivierung/Langzeitarchivierung gewährleistet DE-101 pdager Echtzeitsystem (DE-588)4131397-5 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Deep Reinforcement Learning Time-Sensitive-Networking Real-Time-Networks Bestärkendes Lernen Echtzeit-Netzwerke (DE-588)4113937-9 Hochschulschrift gnd-content Deep learning (DE-588)1135597375 s Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s Echtzeitsystem (DE-588)4131397-5 s DE-604 https://doi.org/10.25972/OPUS-21595 Resolving-System kostenfrei Volltext 1\p aepsg 0,99756 20201117 DE-101 https://d-nb.info/provenance/plan#aepsg 2\p aepkn 0,97399 20201117 DE-101 https://d-nb.info/provenance/plan#aepkn |
| spellingShingle | Hofmann, Jan Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking Echtzeitsystem (DE-588)4131397-5 gnd Deep learning (DE-588)1135597375 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| subject_GND | (DE-588)4131397-5 (DE-588)1135597375 (DE-588)4825546-4 (DE-588)4113937-9 |
| title | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| title_alt | Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| title_auth | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| title_exact_search | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| title_exact_search_txtP | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| title_full | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking vorgelegt von Jan Hofmann |
| title_fullStr | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking vorgelegt von Jan Hofmann |
| title_full_unstemmed | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking vorgelegt von Jan Hofmann |
| title_short | Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking |
| title_sort | deep reinforcement learning for configuration of time sensitive networking deep reinforcement learning zur konfiguration von time sensitive networking |
| title_sub | = Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking |
| topic | Echtzeitsystem (DE-588)4131397-5 gnd Deep learning (DE-588)1135597375 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| topic_facet | Echtzeitsystem Deep learning Bestärkendes Lernen Künstliche Intelligenz Hochschulschrift |
| url | https://doi.org/10.25972/OPUS-21595 |
| work_keys_str_mv | AT hofmannjan deepreinforcementlearningforconfigurationoftimesensitivenetworkingdeepreinforcementlearningzurkonfigurationvontimesensitivenetworking AT hofmannjan deepreinforcementlearningzurkonfigurationvontimesensitivenetworking |