Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of cli...Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.展开更多
In this paper, an artificial neural network method that can predict the chemical composition of deposited weld metal by CO 2 Shielded Flux Cored Wire Surfacing was studied. It is found that artificial neural networ...In this paper, an artificial neural network method that can predict the chemical composition of deposited weld metal by CO 2 Shielded Flux Cored Wire Surfacing was studied. It is found that artificial neural network is a good approach on studying welding metallurgy processes that cannot be described by conventional mathematical methods. In the same time we explored a new way to study the no equilibrium welding metallurgy processes.展开更多
近年来,速变域名(Fast⁃Flux)技术已成为在速变服务网络(Fast⁃Flux Service Network,FFSN)中组建僵尸网络的常见做法,这些FFSN能够以非常高的可用性维持非法在线服务。文中基于FFSN工作原理以及速变域名技术特点,提出了一系列检测特征,...近年来,速变域名(Fast⁃Flux)技术已成为在速变服务网络(Fast⁃Flux Service Network,FFSN)中组建僵尸网络的常见做法,这些FFSN能够以非常高的可用性维持非法在线服务。文中基于FFSN工作原理以及速变域名技术特点,提出了一系列检测特征,设计了一种基于被动DNS流量的Fast⁃Flux域名检测方法。利用DNS协议、黑白名单、DNS流量实时特征对流量数据进行过滤,采用基于信息增益率和基尼系数线性组合的随机森林算法作为模型训练算法,然后用实验数据集和现网真实数据集对所提的方法进行验证。实验结果证明,该方法能够有效识别出Fast⁃Flux域名,并且具有较高的精确率。展开更多
Based on simplex algorithm of optimal design, the multicomponent mixture regression model was used to investigate physical properties of submerged arc welding flux. The effect of complex interaction of seven component...Based on simplex algorithm of optimal design, the multicomponent mixture regression model was used to investigate physical properties of submerged arc welding flux. The effect of complex interaction of seven components in agglomerated flux on softening temperature was analyzed. The results indicate that the interaction of MgO-TiO2-CaCOa-AI20a increases the softening temperature of flux, but the additions of CaF2 and ZrO2 can decrease the softening temperature.展开更多
永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(re...永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(recursive least squares with forgetting factor,FRLS)法避免数据饱和但存在参数估计误差与动态跟踪性能矛盾的问题,文章提出一种基于折息最小二乘(recursive least squares with discount factor,DRLS)法的磁链辨识方法。该算法在FRLS法中引入加权因子构成折息因子,采用递推方法进行磁链辨识,减小参数估计误差,提高磁链辨识精度及动态跟踪能力。通过MATLAB仿真及半实物仿真试验,验证所提磁链识别方法的有效性。展开更多
基金funded by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(Grant No.XDA05040200)the National Key Research and Development Program of China(Grant No.2016YFA0600203)+1 种基金the National Natural Science Foundation of China(Grant Nos.41375035 and 31500402)the Chinese Academy of Sciences Strategic Priority Program on Space Science(Grant No.XDA04077300)
文摘Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.
文摘In this paper, an artificial neural network method that can predict the chemical composition of deposited weld metal by CO 2 Shielded Flux Cored Wire Surfacing was studied. It is found that artificial neural network is a good approach on studying welding metallurgy processes that cannot be described by conventional mathematical methods. In the same time we explored a new way to study the no equilibrium welding metallurgy processes.
文摘近年来,速变域名(Fast⁃Flux)技术已成为在速变服务网络(Fast⁃Flux Service Network,FFSN)中组建僵尸网络的常见做法,这些FFSN能够以非常高的可用性维持非法在线服务。文中基于FFSN工作原理以及速变域名技术特点,提出了一系列检测特征,设计了一种基于被动DNS流量的Fast⁃Flux域名检测方法。利用DNS协议、黑白名单、DNS流量实时特征对流量数据进行过滤,采用基于信息增益率和基尼系数线性组合的随机森林算法作为模型训练算法,然后用实验数据集和现网真实数据集对所提的方法进行验证。实验结果证明,该方法能够有效识别出Fast⁃Flux域名,并且具有较高的精确率。
文摘Based on simplex algorithm of optimal design, the multicomponent mixture regression model was used to investigate physical properties of submerged arc welding flux. The effect of complex interaction of seven components in agglomerated flux on softening temperature was analyzed. The results indicate that the interaction of MgO-TiO2-CaCOa-AI20a increases the softening temperature of flux, but the additions of CaF2 and ZrO2 can decrease the softening temperature.
文摘永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(recursive least squares with forgetting factor,FRLS)法避免数据饱和但存在参数估计误差与动态跟踪性能矛盾的问题,文章提出一种基于折息最小二乘(recursive least squares with discount factor,DRLS)法的磁链辨识方法。该算法在FRLS法中引入加权因子构成折息因子,采用递推方法进行磁链辨识,减小参数估计误差,提高磁链辨识精度及动态跟踪能力。通过MATLAB仿真及半实物仿真试验,验证所提磁链识别方法的有效性。
