This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverag...This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.展开更多
为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰...为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。展开更多
为探究突发公共卫生事件应急能力提升路径,基于应急管理全过程理论,遵循“分析-评价-提升”的逻辑,首先,分析突发公共卫生事件特点与发展变化规律,构建包含预防与准备、监测与预警、处置与救援、恢复与重建4项一级指标,以及23项二级指...为探究突发公共卫生事件应急能力提升路径,基于应急管理全过程理论,遵循“分析-评价-提升”的逻辑,首先,分析突发公共卫生事件特点与发展变化规律,构建包含预防与准备、监测与预警、处置与救援、恢复与重建4项一级指标,以及23项二级指标的评价指标体系;然后结合主成分分析,运用熵权TOPSIS法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)和秩和比法(Rank-Sum Ratio,RSR)构建应急能力评价模型;最后,分析陕西省10个地级市2018-2022年数据,得到各地级市公共卫生事件应急能力评价等级。结果表明:陕西省应急能力分布总体呈现中部高而四周低,北部较高、东南部较低的空间格局;研究期内西安市的应急能力总体表现最优,评分均在0.65以上,其次为榆林、咸阳、铜川和宝鸡;西安市应急能力较为均衡,部分地级市恢复重建能力占比仍有待提升;卫生机构覆盖率、社区卫生服务中心覆盖率和疾病预防控制中心覆盖率为影响突发公共卫生事件管控的最有效因素。研究结果为提升突发公共卫生事件的应对能力提供了一定的实践借鉴。展开更多
How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.展开更多
基金Funded by 973 Program of Ministry of National Defense of China(Grant No.613237)
文摘This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.
文摘为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。
文摘为探究突发公共卫生事件应急能力提升路径,基于应急管理全过程理论,遵循“分析-评价-提升”的逻辑,首先,分析突发公共卫生事件特点与发展变化规律,构建包含预防与准备、监测与预警、处置与救援、恢复与重建4项一级指标,以及23项二级指标的评价指标体系;然后结合主成分分析,运用熵权TOPSIS法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)和秩和比法(Rank-Sum Ratio,RSR)构建应急能力评价模型;最后,分析陕西省10个地级市2018-2022年数据,得到各地级市公共卫生事件应急能力评价等级。结果表明:陕西省应急能力分布总体呈现中部高而四周低,北部较高、东南部较低的空间格局;研究期内西安市的应急能力总体表现最优,评分均在0.65以上,其次为榆林、咸阳、铜川和宝鸡;西安市应急能力较为均衡,部分地级市恢复重建能力占比仍有待提升;卫生机构覆盖率、社区卫生服务中心覆盖率和疾病预防控制中心覆盖率为影响突发公共卫生事件管控的最有效因素。研究结果为提升突发公共卫生事件的应对能力提供了一定的实践借鉴。
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.
