A Width-Variable Window Attention Model for Environmental Sensors

  • Cuiqin Hou
  • Yingju Xia
  • Jun Sun
  • Jing Shang
  • Ryozo Takasu
  • Masao Kondo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


Air pollution is a major problem in modern cities and developing countries. Fine particulate matter (PM2.5) is a growing public health concern and become the most serious air pollution. In this study, we formulate the PM2.5 inference problem in conventional environmental sensors as a sequence-to-sequence problem. We adopt the encoder-decoder LSTM (Long short term memory) framework to solve the PM2.5 inference problem. A novel width-variable window attention mechanism is proposed for the encoder-decoder LSTM system. The proposed method learn the position and width of the attention window simultaneously. The proposed method is evaluated on large scale data and the experimental results show that it achieves better performance on two datasets with different concentration of PM2.5.


LSTM Attention model RNN 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cuiqin Hou
    • 1
  • Yingju Xia
    • 1
  • Jun Sun
    • 1
  • Jing Shang
    • 2
  • Ryozo Takasu
    • 3
  • Masao Kondo
    • 1
  1. 1.Fujitsu Research and Development Center Co., LTD.BeijingChina
  2. 2.College of Environmental Sciences and EngineeringPeking UniversityBeijingChina
  3. 3.Fujitsu Laboratories LTD.KawasakiJapan

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