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工程科学学报 Chinese Journal of Engineering 基于图像混合核的列生成PM25预测 李晓理张博杨旭 Column-generation PM2s prediction based on image mixture kernel LI Xiao-li.ZHANG Bo.YANG Xu 引用本文: 李晓理,张博,杨旭.基于图像混合核的列生成PM2s预测[.工程科学学报,2020,42(7)922-929.doi:10.13374.iss2095- 9389.2019.07.15.002 LI Xiao-li,ZHANG Bo,YANG Xu.Column-generation PM2s prediction based on image mixture kernel[J]Chinese Journal of Engineering,2020,42(7):922-929.doi:10.13374j.issn2095-9389.2019.07.15.002 在线阅读View online:htps:/ldoi.org/10.13374j.issn2095-9389.2019.07.15.002 您可能感兴趣的其他文章 Articles you may be interested in 基于机器学习的北京市PM2.5浓度预测模型及模拟分析 Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing 工程科学学报.2019,41(3:401 https:1doi.org/10.13374.issn2095-9389.2019.03.014 基于IPSO-RELM转炉冶炼终点锰含量预测模型 Improved prediction model for BOF end-point manganese content based on IPSO-RELM method 工程科学学报.2019,41(8):1052htps:1doi.org10.13374斩.issn2095-9389.2019.08.011 磁场形式及参数对单纤维捕集钢铁行业粉尘中PM,性能影响 Performance of single fibercollection PMunder different magnetic field forms in the irn and stee industry 工程科学学报.2020,42(2:154 https:/1doi.org/10.13374.issn2095-9389.2019.02.24.004 新型硬质合金微坑车刀切削能对比研究与预测 Performance comparison and prediction of cutting energy of new cemented carbide micro-pit turning tool 工程科学学报.2017,398):1207 https:/1doi.org/10.13374.issn2095-9389.2017.08.010 无钟高炉炉料分布预测模型 Burden distribution prediction model in a blast furnace with bell-less top 工程科学学报.2017,392:276 https:oi.org/10.13374.issn2095-9389.2017.02.016 BP神经网络F钢铝耗的预测模型 Prediction model of aluminum consumption with BP neural networks in IF steel production 工程科学学报.2017,394:511 https::/1doi.org10.13374.issn2095-9389.2017.04.005基于图像混合核的列生成PM2.5预测 李晓理 张博 杨旭 Column-generation PM2.5 prediction based on image mixture kernel LI Xiao-li, ZHANG Bo, YANG Xu 引用本文: 李晓理, 张博, 杨旭. 基于图像混合核的列生成PM2.5预测[J]. 工程科学学报, 2020, 42(7): 922-929. doi: 10.13374/j.issn2095- 9389.2019.07.15.002 LI Xiao-li, ZHANG Bo, YANG Xu. Column-generation PM2.5 prediction based on image mixture kernel[J]. Chinese Journal of Engineering, 2020, 42(7): 922-929. doi: 10.13374/j.issn2095-9389.2019.07.15.002 在线阅读 View online: https://doi.org/10.13374/j.issn2095-9389.2019.07.15.002 您可能感兴趣的其他文章 Articles you may be interested in 基于机器学习的北京市PM2.5浓度预测模型及模拟分析 Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing 工程科学学报. 2019, 41(3): 401 https://doi.org/10.13374/j.issn2095-9389.2019.03.014 基于IPSO-RELM转炉冶炼终点锰含量预测模型 Improved prediction model for BOF end-point manganese content based on IPSO-RELM method 工程科学学报. 2019, 41(8): 1052 https://doi.org/10.13374/j.issn2095-9389.2019.08.011 磁场形式及参数对单纤维捕集钢铁行业粉尘中PM2.5性能影响 Performance of single fiber collection PM2.5 under different magnetic field forms in the iron and steel industry 工程科学学报. 2020, 42(2): 154 https://doi.org/10.13374/j.issn2095-9389.2019.02.24.004 新型硬质合金微坑车刀切削能对比研究与预测 Performance comparison and prediction of cutting energy of new cemented carbide micro-pit turning tool 工程科学学报. 2017, 39(8): 1207 https://doi.org/10.13374/j.issn2095-9389.2017.08.010 无钟高炉炉料分布预测模型 Burden distribution prediction model in a blast furnace with bell-less top 工程科学学报. 2017, 39(2): 276 https://doi.org/10.13374/j.issn2095-9389.2017.02.016 BP神经网络IF钢铝耗的预测模型 Prediction model of aluminum consumption with BP neural networks in IF steel production 工程科学学报. 2017, 39(4): 511 https://doi.org/10.13374/j.issn2095-9389.2017.04.005
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