The mean sputter depth depends on the surface composition gradient during ion implantation.For the high fluence ion implantation into a Pt-Cu alloy, the surface composition gradient of Cu is so large that the differen...The mean sputter depth depends on the surface composition gradient during ion implantation.For the high fluence ion implantation into a Pt-Cu alloy, the surface composition gradient of Cu is so large that the difference in mean sputter depth between Pt and Cu, is significant. However, for the high fluence ion implantation into 10B-11B isotope mixture, the surface composition gradient of 10B is so small that the difference in mean sputter depth between 10B and 11B is insignificant.展开更多
The formulae for parameters of a negative electron affinity semiconductor(NEAS)with large mean escape depth of secondary electrons A(NEASLD)are deduced.The methods for obtaining parameters such asλ,B,E_(pom)and the m...The formulae for parameters of a negative electron affinity semiconductor(NEAS)with large mean escape depth of secondary electrons A(NEASLD)are deduced.The methods for obtaining parameters such asλ,B,E_(pom)and the maximumδandδat 100.0 keV≥E_(po)≥1.0 keV of a NEASLD with the deduced formulae are presented(B is the probability that an internal secondary electron escapes into the vacuum upon reaching the emission surface of the emitter,δis the secondary electron yield,E_(po)is the incident energy of primary electrons and E_(pom)is the E_(po)corresponding to the maximumδ).The parameters obtained here are analyzed,and it can be concluded that several parameters of NEASLDs obtained by the methods presented here agree with those obtained by other authors.The relation between the secondary electron emission and photoemission from a NEAS with large mean escape depth of excited electrons is investigated,and it is concluded that the presented method of obtaining A is more accurate than that of obtaining the corresponding parameter for a NEAS with largeλ_(ph)(λ_(ph)being the mean escape depth of photoelectrons),and that the presented method of calculating B at E_(po)>10.0 keV is more widely applicable for obtaining the corresponding parameters for a NEAS with largeλ_(ph).展开更多
The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 10...The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 1000 （2000） db in the tropical Pacific derived from the ARGO float position information during the period November 2001 to October 2004 are used to evaluate the intermediate currents of the National Centers for Environmental Prediction reanalysis. To derive reliable velocity information from ARGO float trajectory points, a rigorous quality control scheme is applied, and by virtue of a correction method for reducing the drift error on the surface in obtaining the velocity vectors, their relative errors are less than 25%. Based on the comparisons from the quantitative velocity vectors and from the space-time average currents, some substantial discrepancies are revealed. The first is that the velocities of the reanalysis at mid-depths except near the equator are underestimated relative to the observed velocities by the floats. The average speed difference between NCEP and ARGO values ranges from about -2.3cm s^-1 to -1.8 cm s^-1. The second is that the velocity difference between the ocean model and the observations at 2000 dB seems smaller than that at 1000 dB. The third is that the zonal flow in the reanalysis is too dominant so that some eddies could not be simulated, such as the cyclonic eddy to the east of 160°E between 20°N and 30°N at 2000 dB. In addition, it is noticeable that many floats parking at 1000 dB cannot acquire credible mid-depth velocities due to the time information of their end of ascent （start of descent） on the surface in the trajectory files. Thus, relying on default times of parking, descent and ascent in the metadata files gravely confines their application to measuring mid-depth currents.展开更多
为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserst...为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserstein距离算法与MMD(Maximun Mean Discrepancy)距离算法,在深度生成模型中,对医院网络数据展开降维处理。向异常检测模型中输入降维后网络正常运行数据样本,并提取样本特征。利用深度学习策略中的Adam算法,生成异常信息判别函数,通过待测网络运行数据与正常网络运行数据的特征对比,实现医院网络异常信息入侵检测。实验结果表明,算法能实现对医院网络异常信息入侵的高效检测,精准检测多类型网络入侵行为,为医疗机构网络运行提供安全保障。展开更多
文摘The mean sputter depth depends on the surface composition gradient during ion implantation.For the high fluence ion implantation into a Pt-Cu alloy, the surface composition gradient of Cu is so large that the difference in mean sputter depth between Pt and Cu, is significant. However, for the high fluence ion implantation into 10B-11B isotope mixture, the surface composition gradient of 10B is so small that the difference in mean sputter depth between 10B and 11B is insignificant.
基金Project supported by the National Natural Science Foundation of China(Grant No.11873013)。
文摘The formulae for parameters of a negative electron affinity semiconductor(NEAS)with large mean escape depth of secondary electrons A(NEASLD)are deduced.The methods for obtaining parameters such asλ,B,E_(pom)and the maximumδandδat 100.0 keV≥E_(po)≥1.0 keV of a NEASLD with the deduced formulae are presented(B is the probability that an internal secondary electron escapes into the vacuum upon reaching the emission surface of the emitter,δis the secondary electron yield,E_(po)is the incident energy of primary electrons and E_(pom)is the E_(po)corresponding to the maximumδ).The parameters obtained here are analyzed,and it can be concluded that several parameters of NEASLDs obtained by the methods presented here agree with those obtained by other authors.The relation between the secondary electron emission and photoemission from a NEAS with large mean escape depth of excited electrons is investigated,and it is concluded that the presented method of obtaining A is more accurate than that of obtaining the corresponding parameter for a NEAS with largeλ_(ph)(λ_(ph)being the mean escape depth of photoelectrons),and that the presented method of calculating B at E_(po)>10.0 keV is more widely applicable for obtaining the corresponding parameters for a NEAS with largeλ_(ph).
基金This research is supported by Natural Science Foundation of China(Contract No.40437017 and 40225015).
文摘The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 1000 （2000） db in the tropical Pacific derived from the ARGO float position information during the period November 2001 to October 2004 are used to evaluate the intermediate currents of the National Centers for Environmental Prediction reanalysis. To derive reliable velocity information from ARGO float trajectory points, a rigorous quality control scheme is applied, and by virtue of a correction method for reducing the drift error on the surface in obtaining the velocity vectors, their relative errors are less than 25%. Based on the comparisons from the quantitative velocity vectors and from the space-time average currents, some substantial discrepancies are revealed. The first is that the velocities of the reanalysis at mid-depths except near the equator are underestimated relative to the observed velocities by the floats. The average speed difference between NCEP and ARGO values ranges from about -2.3cm s^-1 to -1.8 cm s^-1. The second is that the velocity difference between the ocean model and the observations at 2000 dB seems smaller than that at 1000 dB. The third is that the zonal flow in the reanalysis is too dominant so that some eddies could not be simulated, such as the cyclonic eddy to the east of 160°E between 20°N and 30°N at 2000 dB. In addition, it is noticeable that many floats parking at 1000 dB cannot acquire credible mid-depth velocities due to the time information of their end of ascent （start of descent） on the surface in the trajectory files. Thus, relying on default times of parking, descent and ascent in the metadata files gravely confines their application to measuring mid-depth currents.
文摘为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserstein距离算法与MMD(Maximun Mean Discrepancy)距离算法,在深度生成模型中,对医院网络数据展开降维处理。向异常检测模型中输入降维后网络正常运行数据样本,并提取样本特征。利用深度学习策略中的Adam算法,生成异常信息判别函数,通过待测网络运行数据与正常网络运行数据的特征对比,实现医院网络异常信息入侵检测。实验结果表明,算法能实现对医院网络异常信息入侵的高效检测,精准检测多类型网络入侵行为,为医疗机构网络运行提供安全保障。
