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Method for High-Efficiency Data Compression and Transmission of Vehicle Measurement Data Through Mobile Internet

Bag om Method for High-Efficiency Data Compression and Transmission of Vehicle Measurement Data Through Mobile Internet

Lorenz Georg Görne presents a method (PrOComp) for optimal usage of the transmission path between the vehicle and the data backend. The compression ratio of vehicle measurement data could be improved from roughly a factor of ten in conventional methods, to up to 27. The method allows vehicle measurement data to be transmitted optimally in terms of data volume via the mobile internet and via traditional transmission routes. Through the PrOComp method, real-time data analysis over the mobile internet is feasible, as well as the collection of big data in the field. This enables key features like predictive maintenance, reactive event evaluation (for example crash events) or fast generation of AI training data. Through the usage of standardized interfaces and data formats, PrOComp can be adapted to the needs of many industry branches that feature field data collection.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783658437985
  • Indbinding:
  • Paperback
  • Sideantal:
  • 168
  • Udgivet:
  • 30. december 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 148x10x210 mm.
  • Vægt:
  • 226 g.
  • 8-11 hverdage.
  • 15. januar 2025
På lager
Forlænget returret til d. 31. januar 2025
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Beskrivelse af Method for High-Efficiency Data Compression and Transmission of Vehicle Measurement Data Through Mobile Internet

Lorenz Georg Görne presents a method (PrOComp) for optimal usage of the transmission path between the vehicle and the data backend. The compression ratio of vehicle measurement data could be improved from roughly a factor of ten in conventional methods, to up to 27. The method allows vehicle measurement data to be transmitted optimally in terms of data volume via the mobile internet and via traditional transmission routes.

Through the PrOComp method, real-time data analysis over the mobile internet is feasible, as well as the collection of big data in the field. This enables key features like predictive maintenance, reactive event evaluation (for example crash events) or fast generation of AI training data. Through the usage of standardized interfaces and data formats, PrOComp can be adapted to the needs of many industry branches that feature field data collection.

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