The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the curre...The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the current contribution, the latest releases of GOCE-based GGMs are evaluated on the regional scale using the available terrestrial GPS/Levelling and gravity data collected over Egypt. To overcome the spectral inconsistency between the GOCE-based GGMs and the ground-based data, the spectral enhancement method (SEM) is applied. Five of GOCE-based GGMs have been used, namely GOSG01S, IGGT_R1, IfE_GOCE05s_ GO_CONS_GCF_2_SPW_R5 (SPW_R5 in the following) and NULP-02. The evaluation process of GOCE-based GGMs with the available ground data over Egypt considering the SEM method shows remarkable improvements obtained from the SPW_R5 model. The model provides lower differences of the standard deviations with respect to the EGM2008 and the other applied geopotential gravity models as well as the applied ground-based gravity and GPS/Levelling data. The findings regarding the ground-based data show obvious reductions of about 15.16% and 32.22% achieved by the GOCE-based model in term of standard deviations differences of gravity anomalies and geoid heights, respectively. Therefore, the SPW_R5 model is recommended to be applied as a reference model for compensating the long-to-short wavelength (up to spherical harmonics degree/order 280) components when modelling the gravimetric geoid over Egypt.展开更多
函数加密是一种新型原语,通过函数密钥解密可以得到关于消息的函数值,而不会泄露消息的其他信息.通用群模型(generic group model,GGM)是一种可以用来分析方案构造安全性的理想化模型.由于目前还没有针对GGM的有效攻击,且能在GGM下证明...函数加密是一种新型原语,通过函数密钥解密可以得到关于消息的函数值,而不会泄露消息的其他信息.通用群模型(generic group model,GGM)是一种可以用来分析方案构造安全性的理想化模型.由于目前还没有针对GGM的有效攻击,且能在GGM下证明安全的方案都比较高效,越来越多的函数加密方案在GGM下证明安全性.2017年,Baltico等人提出了第一个公钥二次函数加密方案,并在GGM下证明了自适应的不可区分安全性.然而,目前无论是在标准假设还是GGM下,没有能达到自适应可模拟安全性的公钥二次函数加密方案.本文根据公钥二次函数加密可模拟安全的定义,证明了Baltico等人提出的方案在GGM下具有更强的自适应可模拟安全性.由于证明过程繁琐,本文借助计算机辅助,设计了在GGM下证明公钥二次函数加密方案具有可模拟安全性的自动证明工具.该工具是第一个针对函数加密方案设计的自动证明工具,且该工具不仅能快速测试在GGM下构造的函数方案是否具有可模拟安全性,还可以作为标准假设下构造安全函数加密方案的初步验证.展开更多
The availability of many high-degree Global Geopotential Models(GGMs), namely EGM2008, EIGEN-6C4,GECO, SGG-UGM-1, SGG-UGM-2, XGM2019e_2159, and GGMPlus, challenges users regarding which model is best for Vietnam. This...The availability of many high-degree Global Geopotential Models(GGMs), namely EGM2008, EIGEN-6C4,GECO, SGG-UGM-1, SGG-UGM-2, XGM2019e_2159, and GGMPlus, challenges users regarding which model is best for Vietnam. This study, therefore, evaluates their performance by comparing them with GNSS/leveling data over Vietnam. Results show that their absolute and relative performances are largely independent of topographic conditions and geographical location and can be ranked into three classes:(1)XGM2019e_2159 has the highest accuracy,(2) the models EIGEN-6C4, GECO, SGG-UGM-1, SGG-UGM-2, and GGMPlus, have a very similar level of medium accuracy, while(3) EGM2008 is found to be the least accurate. In an absolute sense, the differences between GNSS/leveling and EGM2008-based height anomalies have a standard deviation(STD) of 0.290 ± 0.010 m, whereas, for XGM2019e_2159, this is 0.156 ± 0.006 m.All other models have STDs of(0.18-0.19) ± 0.007 m. Regarding relative performance without fitting, all GGMs have comparable accuracies for baseline length of 5-20 km, while for baselines longer than 20 km,the STD of XGM2019e_2159 is 1.5 ppm-0.5 ppm(approximately 19%-40%) lower compared with EGM2008, and 0.5 ppm-0.25 ppm(approximately 7%-36%) lower compared with EIGEN6C4, GECO,SGG-UGM-1, SGG-UGM-2, and GGMPlus. In addition, the STDs decrease significantly from 20 to 12 ppm in the range of 5-10 km, slightly from 12 to 6 ppm for 10-35 km, very slightly from 6 to 2.5 ppm for35-200 km, and then remain almost unchanged for longer baselines. After fitting, the relative accuracies of all GGMs are at the same level with negligible STD/RMSE values. Furthermore, only EGM2008 experiences significant regional differences, while other GGMs show more homogeneous spatial variation of absolute accuracy over Vietnam. These findings can contribute to the development of local quasigeoid models in Vietnam and may be helpful with the improvement of GGMs in the future.展开更多
广义最小二乘估计(Generalized least squares estimation,GLSE)是最佳线性无偏估计,却有计算复杂高和依赖未知信息的局限性,使得普通最小二乘估计(Ordinary least squares estimation,OLSE)经常成为应用的无奈之选。本文探讨该现象背...广义最小二乘估计(Generalized least squares estimation,GLSE)是最佳线性无偏估计,却有计算复杂高和依赖未知信息的局限性,使得普通最小二乘估计(Ordinary least squares estimation,OLSE)经常成为应用的无奈之选。本文探讨该现象背后的三个循序渐进的理论问题:第一,GLSE的退化问题,给出GLSE完全退化为OLSE的充要条件;第二,退化的分类问题,依据设计矩阵和误差协方差阵的结构把退化现象分为三类,并给出典型的退化特例;第三,不完全退化问题,研讨导致效率退化的因素,刻画效率曲线和效率曲面,最后给出效率不低于95%的退化边界。效率退化和边界分析的潜在应用价值主要包括两方面:第一,为进一步优化试验方案提供效率视角和反馈信息;第二,为设计更简洁更可靠的算法提供理论依据。展开更多
Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study th...Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM （1,1）, which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM （1,1）. Finally, the paper proposes a generalization grey model GGM （1,1）, which is a extended form of GM （1,1） and also a unified form of model GM （1,1）, model GM （1,1,α）, stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.展开更多
文摘The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the current contribution, the latest releases of GOCE-based GGMs are evaluated on the regional scale using the available terrestrial GPS/Levelling and gravity data collected over Egypt. To overcome the spectral inconsistency between the GOCE-based GGMs and the ground-based data, the spectral enhancement method (SEM) is applied. Five of GOCE-based GGMs have been used, namely GOSG01S, IGGT_R1, IfE_GOCE05s_ GO_CONS_GCF_2_SPW_R5 (SPW_R5 in the following) and NULP-02. The evaluation process of GOCE-based GGMs with the available ground data over Egypt considering the SEM method shows remarkable improvements obtained from the SPW_R5 model. The model provides lower differences of the standard deviations with respect to the EGM2008 and the other applied geopotential gravity models as well as the applied ground-based gravity and GPS/Levelling data. The findings regarding the ground-based data show obvious reductions of about 15.16% and 32.22% achieved by the GOCE-based model in term of standard deviations differences of gravity anomalies and geoid heights, respectively. Therefore, the SPW_R5 model is recommended to be applied as a reference model for compensating the long-to-short wavelength (up to spherical harmonics degree/order 280) components when modelling the gravimetric geoid over Egypt.
文摘函数加密是一种新型原语,通过函数密钥解密可以得到关于消息的函数值,而不会泄露消息的其他信息.通用群模型(generic group model,GGM)是一种可以用来分析方案构造安全性的理想化模型.由于目前还没有针对GGM的有效攻击,且能在GGM下证明安全的方案都比较高效,越来越多的函数加密方案在GGM下证明安全性.2017年,Baltico等人提出了第一个公钥二次函数加密方案,并在GGM下证明了自适应的不可区分安全性.然而,目前无论是在标准假设还是GGM下,没有能达到自适应可模拟安全性的公钥二次函数加密方案.本文根据公钥二次函数加密可模拟安全的定义,证明了Baltico等人提出的方案在GGM下具有更强的自适应可模拟安全性.由于证明过程繁琐,本文借助计算机辅助,设计了在GGM下证明公钥二次函数加密方案具有可模拟安全性的自动证明工具.该工具是第一个针对函数加密方案设计的自动证明工具,且该工具不仅能快速测试在GGM下构造的函数方案是否具有可模拟安全性,还可以作为标准假设下构造安全函数加密方案的初步验证.
文摘The availability of many high-degree Global Geopotential Models(GGMs), namely EGM2008, EIGEN-6C4,GECO, SGG-UGM-1, SGG-UGM-2, XGM2019e_2159, and GGMPlus, challenges users regarding which model is best for Vietnam. This study, therefore, evaluates their performance by comparing them with GNSS/leveling data over Vietnam. Results show that their absolute and relative performances are largely independent of topographic conditions and geographical location and can be ranked into three classes:(1)XGM2019e_2159 has the highest accuracy,(2) the models EIGEN-6C4, GECO, SGG-UGM-1, SGG-UGM-2, and GGMPlus, have a very similar level of medium accuracy, while(3) EGM2008 is found to be the least accurate. In an absolute sense, the differences between GNSS/leveling and EGM2008-based height anomalies have a standard deviation(STD) of 0.290 ± 0.010 m, whereas, for XGM2019e_2159, this is 0.156 ± 0.006 m.All other models have STDs of(0.18-0.19) ± 0.007 m. Regarding relative performance without fitting, all GGMs have comparable accuracies for baseline length of 5-20 km, while for baselines longer than 20 km,the STD of XGM2019e_2159 is 1.5 ppm-0.5 ppm(approximately 19%-40%) lower compared with EGM2008, and 0.5 ppm-0.25 ppm(approximately 7%-36%) lower compared with EIGEN6C4, GECO,SGG-UGM-1, SGG-UGM-2, and GGMPlus. In addition, the STDs decrease significantly from 20 to 12 ppm in the range of 5-10 km, slightly from 12 to 6 ppm for 10-35 km, very slightly from 6 to 2.5 ppm for35-200 km, and then remain almost unchanged for longer baselines. After fitting, the relative accuracies of all GGMs are at the same level with negligible STD/RMSE values. Furthermore, only EGM2008 experiences significant regional differences, while other GGMs show more homogeneous spatial variation of absolute accuracy over Vietnam. These findings can contribute to the development of local quasigeoid models in Vietnam and may be helpful with the improvement of GGMs in the future.
文摘广义最小二乘估计(Generalized least squares estimation,GLSE)是最佳线性无偏估计,却有计算复杂高和依赖未知信息的局限性,使得普通最小二乘估计(Ordinary least squares estimation,OLSE)经常成为应用的无奈之选。本文探讨该现象背后的三个循序渐进的理论问题:第一,GLSE的退化问题,给出GLSE完全退化为OLSE的充要条件;第二,退化的分类问题,依据设计矩阵和误差协方差阵的结构把退化现象分为三类,并给出典型的退化特例;第三,不完全退化问题,研讨导致效率退化的因素,刻画效率曲线和效率曲面,最后给出效率不低于95%的退化边界。效率退化和边界分析的潜在应用价值主要包括两方面:第一,为进一步优化试验方案提供效率视角和反馈信息;第二,为设计更简洁更可靠的算法提供理论依据。
基金supported by the National Natural Science Foundation of China(70971103)the Specialized Research Fund for the Doctora Program of Higher Education(20120143110001)
文摘Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM （1,1）, which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM （1,1）. Finally, the paper proposes a generalization grey model GGM （1,1）, which is a extended form of GM （1,1） and also a unified form of model GM （1,1）, model GM （1,1,α）, stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.
