A real-time missing data recovery method using recurrent neural network for multiple transmissions

Bor Shing Lin*, Yu Syuan Lin, I. Jung Lee, Bor-Shyh Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Data loss and recovery is a critical issue in data transmission. Traditional data recovery methods are impractical for use in real-time systems that require multiple transmissions. To solve this problem, this study proposed a recovery method based on a recurrent neural network, which is then used to build a pre-diction model. When a data gap occurs, the missing data can be recovered immediately using the predicted value. This method distributes the calculation and can immediately recover the data gap. Through a series of experiments, this study optimized different parameters in the neural network, thus optimizing the prediction model.

Original languageEnglish
Title of host publicationRecent Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceeding of the Fourteenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
EditorsPei-Wei Tsai, Akinori Ito, Lakhmi C. Jain, Lakhmi C. Jain, Jeng-Shyang Pan, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages99-107
Number of pages9
ISBN (Print)9783030037444
DOIs
StatePublished - 26 Nov 2018
Event14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018 - Sendai, Japan
Duration: 26 Nov 201828 Nov 2018

Publication series

NameSmart Innovation, Systems and Technologies
Volume109
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018
Country/TerritoryJapan
CitySendai
Period26/11/1828/11/18

Keywords

  • Missing data recovery
  • Recurrent neural network
  • Wearable technology

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