Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 35-40.
• Review • Previous Articles Next Articles
WANG Peng-yue1,2, GUO Mao-zu1,2, ZHAO Ling-ling3, ZHANG Yu1,4
CLC Number:
| [1]BOLDO E,MEDINA S,TERTRE A L,et al.Apheis:Health impact assessment of long-term exposure to PM2.5 in 23 European cities[J].European Journal of Epidemiology,2006,21(6):449-458. [2]ZHENGY,CAPRA L,WOLFSON O,et al.Urban Computing:Concepts,Methodologies,and Applications[J].ACM Trans.Intelligent Systems and Technology,2014,5(3):38-55. [3]ZHENG Y,LIU F,HSIEH H-P.U-Air:When urban air quality inference meets big data [C]∥Proceedings of the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining.New York:ACM,2013:1436-1444. [4]MAEKIEWICZ M.A review of mathematical models for the atmospheric dispersion of heavy gases.Part I.A classification of models [J].Ecological Chemistry and Engineering S,2012,19(3):297-314. [5]WANG J F,HU M G,XU C D,et al.Estimation of citywide air pollution in Beijing [J].PLOS ONE,2013,8(1):e53400. [6]VARDOULAKIS S,FISHER B E.A.,PERICLEOUS K A,et al.Modelling air quality in street canyons:A review [J].Atmospheric Environment,2003,37(2):155-182. [7]ZHU J Y,ZHANG C,ZHANG H C,et al.pg-Causality:Identifying spatiotemporal causal pathways for air pollutants with urban big data[J].IEEE Transactions on Big Data,2017,6(6):1. [8]HSIEH H-P,LIU S D,ZHENG Y.Inferring air quality for stationlocation recommendation based on urban big data[C]∥Proceedings of the21th ACM SIGKDD IntConf on Knowledge Discoveryand Data Mining.New York:ACM,2015:437-446. [9]ZHENG Y,YI X W,LI M,et al.Forecasting fine-grained air quality based on bigdata[C]∥Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2015:2267-2276. [10]YI X,ZHANG J,WANG Z,et al.Deep distributed fusion network for air quality prediction[C]∥24th ACM SIGKDD International Conference.2018. [11]LU J,CAO X.PM2.5 pollution in major cities in China:Pollution status,emission sources and control measures[J].Fresenius Environ.Bull,2015,24:1338-1349. [12]ENGEL-C J A,HOLLOMAN C H,COUTANT B W,et al. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality[J].Atmospheric Environment,2004,38(16):2495-2509. [13]WANG J,CHRISTOPHER S A.Intercomparison between sa-tellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies [J].Geophysical Research Letters,2003,30(21). [14]LIU Y,FRANKLIN M,KAHN R,et al.Using aerosol optical thickness to predict ground-level PM2.5concentrations in the St.Louis area:A comparison between MISR and MODIS [J].Remote Sensing of Environment,2007,107(1-2):33-44. [15]VAN D A,MARTIN RV,PARK R J.Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing [J].Journal of Geophysical Research:Atmospheres banner,2006,111(D21):201-210. [16]郑卓云,陈良富,郑君瑜,等.高分辨率气溶胶光学厚度在珠三角及香港地区区域颗粒物监测中的应用研究 [J].中国环境科学,2011,31(6):1154-1161. [17]WANG Z F,CHEN L F,TAN J H,et al.Satellite-based estimation of regional particulate matter(PM) in Beijing using vertical-and-RH correcting method [J].Remote Sensing of Environment,2010,114(1):50-63. [18]WANG Y,LI J Y,HE J,et al.Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement [J].Aerosol Science and Technology,2015,49(11):1063-1077. [19]GAO M,CAO J,SETO E.A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an,China [J].Environmental Pollution,2015,199:56-65. [20]KHADEM M I,SGARCIU V.Dust monitoring systems [C]∥The Sixth International Conference on Systems and Networks Communications(ICSNC 2011).2011:68-71. [21]LIU Y,MAO X,HE Y,et al.CitySee:not only a wireless sensor network [J].IEEE Network,2013,27(5):42-47. [22]CHENG Y,LI X,LI Z,et al.AirCloud:a cloud-based air-quality monitoring system for everyone [C]∥Proceedings ofthe 12th ACM Conf on Embedded Network Sensor Systems.New York:ACM,2014:251-265. [23]TUDOSE D S,PATRASCU T A,VOINESCU A,et al.Mobile sensors in air pollution measurement[C]∥Proceedings of the 8th Positioning Navigation and Communication.Dresden:IEEE,2011:166-170. [24]DEVARAKONDA S,SEVUSU P,LIU H,et al.Real-time air quality monitoring through mobile sensing in metropolitan areas[C]∥Proceedings of the 2nd ACM SIGKDD Intworkshop on urban computing.New York:ACM,2013:15. [25]HEDGECOCK W,VOLGYESI P,LEDECZI A,et al.Dissemi-nation and presentation of high resolution air pollution data from mobile sensor nodes[C]∥Proceedingsof the 48th Annual Southeast Regional Conf.New York:ACM,2010:6. [26]HASENFRATZ D,SAUKH O,WALSER C,et al.Pushing the spatio-temporal resolution limit of urban air pollution maps[C]∥Proceedings of IEEE PerCom’14.Budapest:IEEE,2014:69-77. [27]FIERZ M,HOULE C,STEIGMEIER P,et al.Design,calibration,and field performance of a miniature diffusion size classifier [J].Aerosol Science and Technology,2011,45(1):1-10. [28]DONG W,GUAN G Y,CHEN Y,et al.Mosaic:towards city scale sensing with mobile sensor networks[C]∥Proceedings of IEEE ICPADS’21,Melbourne:IEEE,2015:29-36. [29]GAO Y,DONG W,GUO K,et al.Mosaic:a low-cost mobile sensing system for urban air quality monitoring [C]∥Procee-dings of IEEE INFOCOM’35.San Francisco:IEEE,2016:1-9. [30]MUSTHAG M,GANESAN D.Labor dynamics in a mobile micro-task market[C]∥Proceedings of the SIGCHI Conf on Human Factors in Computing Systems.New York:ACM,2013:641-650. [31]CHEN X,WU X,LI X Y,et al.Privacy-preserving high-quality map generation with participatory sensing[C]∥Proceedings of IEEE INFOCOM’14.Toronto:IEEE,2014:2310-2318. [32]于瑞云,王鹏飞,白志宏,等.参与式感知:以人为中心的智能感知与计算[J].计算机研究与发展,2017,54(3):457-473. [33]BURKE J,ESTRIN D,HANSEN M,et al.Participatory sensing [C]∥Proceedings of ACM SenSys Workshop on World-Sensor-Web,2006. [34]ZHUANG Y,LIN F,YOO E H,et al.AirSense:A portable context-sensing device for personal air quality monitoring [C]∥Proceedings of the 2015 Workshop on Pervasive Wireless Healthcare.New York:ACM,2015:17-22. [35]BUDDE M,BUSSE M,BEIGL M.Investigating the use of commodity dust Sensors for the embedded measurement of particulate matter [C]∥Proceedings of IEEE INSS’9.Antwerp:IEEE,2012:1-4. [36]BUDDE M,MASRI R E,RIEDEL T,et al.Enabling low-cost particulate matter measurement for participatory sensing scenarios [C]∥Proceedings of the 12th IntConf on MUM’13.New York:ACM,2013:19. [37]CHEN L J,HO Y H,LEE H C,et al.An open framework for participatory PM2.5 monitoring in smart cities [J].IEEE Access,2017,5:14441-14454. [38]PODURI S,NIMKAR A,SUKHATME G S.Visibility monitoring using mobile phones[J].Annual Report:Center for Embedded Networked Sensing,2010:125-127. [39]苗启广,李宇楠.图像去雾霾算法的研究现状与展望[J].计算机科学,2017,44(11):1-8. [40]HE K M,SUN J,TANG X O.Single image haze removal using dark channel prior[J].IEEE Computer Society,2011,33(12):2341-2353. [41]BUDDE M,BARBERA P,MASRI RE,et al.Retrofitting smart phones to be used as particulate matter dosimeters [C]∥Proceedings of the 2013 IntSymp on Wearable Computers.New York:ACM,2013:139-140. [42]LI Y,HUANG J,LUO J.Using user generated online photos to estimate and monitor air pollution in major cities[C]∥Procee-dings of the 7th International Conference on Internet Multimedia Computing and Service.ACM,2015:79. [43]LIU X Y,SONG Z,NGAI E,et al.PM2.5 monitoring using images from smartphones in participatory sensing[C]∥Proceedings of IEEE INFOCOM,Hong Kong:IEEE,2015:630-635. |
| [1] | LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214. |
| [2] | NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296. |
| [3] | HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11. |
| [4] | LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266. |
| [5] | ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343. |
| [6] | CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323. |
| [7] | XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406. |
| [8] | YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515. |
| [9] | LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92. |
| [10] | ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99. |
| [11] | WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789. |
| [12] | XU Jie, ZHU Yu-kun, XING Chun-xiao. Application of Machine Learning in Financial Asset Pricing:A Review [J]. Computer Science, 2022, 49(6): 276-286. |
| [13] | LI Ye, CHEN Song-can. Physics-informed Neural Networks:Recent Advances and Prospects [J]. Computer Science, 2022, 49(4): 254-262. |
| [14] | YAO Xiao-ming, DING Shi-chang, ZHAO Tao, HUANG Hong, LUO Jar-der, FU Xiao-ming. Big Data-driven Based Socioeconomic Status Analysis:A Survey [J]. Computer Science, 2022, 49(4): 80-87. |
| [15] | ZHANG Xiao-qing, FANG Jian-sheng, XIAO Zun-jie, CHEN Bang, Risa HIGASHITA, CHEN Wan, YUAN Jin, LIU Jiang. Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image [J]. Computer Science, 2022, 49(3): 204-210. |
|
||
Home