计算机科学 ›› 2024, Vol. 51 ›› Issue (7): 116-123.doi: 10.11896/jsjkx.230400111
黄伟杰1, 郭贤伟1, 於志勇1,2, 黄昉菀1,2
HUANG Weijie1, GUO Xianwei1, YU Zhiyong1,2, HUANG Fangwan1,2
摘要: 随着城市化进程的不断加快,工业发展、人口聚集使得空气质量问题日益严峻。出于对采集成本的考虑,对空气质量的主动采样正受到越来越多的关注。但现有模型要么只能迭代选择采样位置,要么难以实时更新采样算法。基于此,提出了一种基于压缩感知自适应测量矩阵的空气质量主动采样方法,将采样位置的选择问题转化为矩阵的列子集选择问题。该方法首先利用历史完整数据进行字典学习,然后将学习后的字典经过列子集选择后得到能够指导批量采样的自适应测量矩阵,最后结合利用空气质量数据特性构建的稀疏基矩阵恢复出未采样的数据。该方法使用压缩感知模型一体化实现采样和推断,避免了使用多个模型的不足。此外,考虑到空气质量的时序变动问题,在每一次的主动采样后,字典还会利用最新数据进行在线更新以指导下一次的采样。两个真实数据集上的实验结果表明,经过字典学习后得到的自适应测量矩阵在低于20%的多个采样率下,恢复性能优于所有基线。
中图分类号:
[1]SHADDICK G,THOMAS M L,MUDU P,et al.Half theworld's population are exposed to increasing air pollution[J].NPJ Climate and Atmospheric Science,2020,3(1):1-5. [2]SOKHI R S,MOUSSIOPOULOS N,BAKLANOV A,et al.Advances in air quality research-current and emerging challenges[J].Atmospheric Chemistry and Physics,2022,22(7):4615-4703. [3]YANG X,ZHANG Z.An attention-based domain spatial-temporal meta-learning(ADST-ML) approach for PM2.5 concentration dynamics prediction[J].Urban Climate,2023,47:101363. [4]GUO B,WANG Z,YU Z,et al.Mobile crowd sensing and computing:The review of an emerging human-powered sensing para-digm[J].ACM Computing Surveys(CSUR),2015,48(1):1-31. [5]JEZDOVIĆ I,POPOVIĆ S,RADENKOVIĆ M,et al.A crowd-sensing platform forreal-time monitoring and analysis of noise pollution in smart cities[J].Sustainable Computing:Informatics and Systems,2021,31:100588. [6]BALLATORE A,VERHAGEN T J,LI Z,et al.This city is not a bin:crowd mapping the distribution of urban litter[J].Journal of Industrial Ecology,2022,26(1):197-212. [7]SHENG X,TANG J,ZHANG W.Energy-efficient collaborative sensing with mobile phones[C]//Proceedings of IEEE Confe-rence on Computer Communications(INFOCOM).IEEE,2012:1916-1924. [8]WANG L,ZHANG D,PATHAK A,et al.CCS-TA:Quality-guaranteed online task allocation in compressive crowdsensing[C]//Proceedings of the 2015 ACM International Joint Confe-rence on Pervasive and Ubiquitous Computing.2015:683-694. [9]SONG X,GUO Y,LI N,et al.A novel approach for missing data prediction in coevolving time series[J].Computing,2019,101(11):1565-1584. [10]BUDD S,ROBINSON E C,KAINZ B.A survey on active lear-ning and human-in-the-loop deep learning for medical image ana-lysis[J].Medical Image Analysis,2021,71:102062. [11]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306. [12]TSAIG Y,DONOHO D L.Extensions of compressed sensing[J].Signal Processing,2006,86(3):549-571. [13]CAI C,BAI E,JIANG X Q,et al.Simultaneous Audio Encryption and Compression Using Parallel Compressive Sensing and Modified Toeplitz Measurement Matrix[J].Electronics,2021,10(23):2902. [14]MENDELSON S,PAJOR A,TOMCZAK-JAEGERMANN N.Uniform uncertainty principle for Bernoulli and subgaussian ensembles[J].Constructive Approximation,2008,28(3):277-289. [15]HEGDE C,SANKARANARAYANAN A C,YIN W,et al.Numax:A convex approach for learning near-isometric linear embeddings[J].IEEE Transactions on Signal Processing,2015,63(22):6109-6121. [16]TROPP J A.A mathematical introduction to compressive sen-sing [J].Bulletin of the American Mathematical Society,2017,54(1):151-165. [17]XU K,LI Y,REN F.A data-driven compressive sensing framework tailored for energy-efficient wearable sensing[C]//Proceedings of 2017 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2017:861-865. [18]LI S,ZHANG W,CUI Y,et al.Joint design of measurement matrix and sparse support recovery method via deep auto-encoder[J].IEEE Signal Processing Letters,2019,26(12):1778-1782. [19]HUIJBEN I A M,VEELING B S,VAN SLOUN R J G.Deep probabilistic subsampling for task-adaptive compressed sensing[C]//Proceedings of 8th International Conference on Learning Representations.ICLR,2020. [20]SHAHRASBI B,RAHNAVARD N.Model-based nonuniformcompressive sampling and recovery of natural images utilizing a wavelet-domain universal hidden Markov model[J].IEEE Transactions on Signal Processing,2016,65(1):95-104. [21]MALLOY M L,NOWAK R D.Near-optimal adaptive com-pressed sensing[J].IEEE Transactions on Information Theory,2014,60(7):4001-4012. [22]XIE K,LI X,WANG X,et al.Active sparse mobile crowd sen-sing based on matrix completion[C]//Proceedings of the 2019 International Conference on Management of Data.2019:195-210. [23]TANG G.Seismic data reconstruction and denoising based oncompressive sensing and sparse representation[D].Beijing:Tsinghua University,2010. [24]CAO J J,XIAO J M,ZHU Y F,et al.Efficient shallow seismicacquisition method based on compressed sensing theory[J].Progress in Geophysics,2022,37(5):1920-1932. [25]LIU W,WANG L,WANG E,et al.Reinforcement learning-based cell selection in sparse mobile crowdsensing[J].Computer Networks,2019,161:102-114. [26]WANG L,LIU W,ZHANG D,et al.Cell selection with deep reinforcement learning in sparse mobile crowdsensing[C]//Proceedings of 2018 IEEE 38th International Conference on Distri-buted Computing Systems(ICDCS).IEEE,2018:1543-1546. [27]ZHANG Z,XU Y,YANG J,et al.A survey of sparse representation:algorithms and applications[J].IEEE Access,2015,3:490-530.. [28]PATI Y C,REZAIIFAR R,KRISHNAPRASAD P S.Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C]//Proceedings of 27th Asilomar Conference on Signals,Systems and Computers.IEEE,1993:40-44. [29]CHEN S S,DONOHO D L,SAUNDERS M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159. [30]BLUMENSATH T,DAVIES M E.Iterative hard thresholding for compressed sensing[J].Applied and Computational Harmonic Analysis,2009,27(3):265-274. [31]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:An algo-rithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322. [32]BOUTSIDIS C,MAHONEY M W,DRINEAS P.An improved approximation algorithm for the column subset selection pro-blem[C]//Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete algorithms.Society for Industrial and Applied Mathematics,2009:968-977. [33]PAN C T.On the existence and computation of rank-revealing LU factorizations[J].Linear Algebra and Its Applications,2000,316(1/2/3):199-222. [34]BOUTSIDIS C,DRINEAS P,MAGDON-ISMAIL M.Near-optimal column-based matrix reconstruction[J].SIAM Journal on Computing,2014,43(2):687-717. [35]LIANG X,LI S,ZHANG S,et al.PM2.5 data reliability,consis-tency,and air quality assessment in five Chinesecities[J].Journal of Geophysical Research:Atmospheres,2016,121(17):10220-10236. [36]BURBIDGE R,ROWLAND J J,KING R D.Active learning for regression based on query by committee[J].Lecture Notes in Computer Science,2007,4881:209-218. |
|