为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。...为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。展开更多
Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base ...Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.展开更多
基金教育部新世纪优秀人才支持计划(the Program for New Century Excellent Talents in University No.NCET-06-0298)辽宁省高等学校优秀人才支持计划(the Program for Liaoning Excellent Talents in University No.RC-05-07,No.2006R06)+2 种基金辽宁省教育厅科学研究计划(the Program for Study of Science of the Educational Department of Liaoning Province No.05L020)大连市科学技术计划(the Programfor Dalian Science and Technology No.2005A10GX106)大连大学辽宁省智能信息处理重点实验室开放课题(the Open Fund of LiaoningKey Lab of Intelligent Information Processing,Dalian University No.2005-9)
文摘为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。
基金Under the National Key R&D Program Key Project(No.2021YFC3201201)National Natural Science Foundation of China(No.52360032)+2 种基金Basic Scientific Research Business Fee Project of Colleges And Universities Directly Under the Inner Mongolia Autonomous Region(No.JBYYWF2022001)Development Plan of Innovation Team of Colleges And Universities in Inner Mongolia Autonomous Region(No.NMGIRT2313)the Innovation Team of‘Grassland Talents’。
文摘Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.
