Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 116-123.doi: 10.11896/jsjkx.230400111

• Database & Big Data & Data Science • Previous Articles     Next Articles

Active Sampling of Air Quality Based on Compressed Sensing Adaptive Measurement Matrix

HUANG Weijie1, GUO Xianwei1, YU Zhiyong1,2, HUANG Fangwan1,2   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University),Fuzhou 350108,China
  • Received:2023-04-16 Revised:2023-09-08 Online:2024-07-15 Published:2024-07-10
  • About author:HUANG Weijie,born in 1999,postgra-duate.His main research interests include machine learning and so on.
    HUANG Fangwan,born in 1980,Ph.D,senior lecturer,is a member of CCF(No.D3015M).Her main research interests include computational intelligence,machine learning and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(61772136),Fujian Provincial Guiding Project(2020H0008) and Educational Research Project for Young and Middle-aged Teachers in Fujian Province(JAT210007).

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Abstract: With the continuous acceleration of urbanization,industrial development and population agglomeration make the pro-blem of air quality increasingly serious.Due to the cost of sampling,more and more attention is paid to active sampling of air qua-lity.However,the existing models can either only select the sampling location iteratively or hardly update the sampling algorithm in real time.Motivated by this,an active sampling method of air quality based on compressed sensing adaptive measurement matrix is proposed in this paper.The problem of sampling location selection is transformed into the column subset selection problem of the matrix.Firstly,the historical complete data is used for dictionary learning.After column subset selection of the learned dictionary,an adaptive measurement matrix that can guide batch sampling is obtained.Finally,the unsampled data is recovered by using the sparse basis matrix constructed by the data characteristics of air quality.This method uses a compressed sensing model to realize sampling and inference integrally,which avoids the shortcoming of using multiple models.In addition,considering the ti-ming variation of air quality,after each active sampling,the dictionary is updated online with the latest data to guide the next sampling.Experimental results on two real datasets show that the adaptive measurement matrix obtained after dictionary learning has better recovery performance than all baselines at multiple sampling rates less than 20%.

Key words: Crowd sensing, Compressed sensing, Adaptive measurement matrix, Dictionary learning, Active sampling

CLC Number: 

  • TP391
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