In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The re...In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The results show that the model nowcasting is improved when only the cloud water and rain water are adjusted or all of the cloud water, rain water and vapor are adjusted in the initial field. The forecasting of the former(latter) approach during 0-3(0-6) hours is significantly improved. Furthermore, for the forecast for 0-3 hours, the latter approach is better than the former. Compared with the forecasting results for which the vapor of the model initial field is adjusted by the background vapor with those by the revised vapor, the nowcasting of the revised vapor is much better than that of background vapor. Analysis of the reasons indicated that when the vapor is adjusted in the model initial field, especially when the saturated vapor is considered, the forecasting of the vapor field is significantly affected. The changed vapor field influences the circulation, which in turn improves the model forecasting of radar reflectivity and rainfall.展开更多
The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parame...The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.展开更多
Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materia...Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.展开更多
In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenario...In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.展开更多
Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest i...Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.展开更多
Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong ...Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.展开更多
Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, bu...Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.展开更多
To analyze the effects of gas cannons on clouds and precipitation,multisource observational data,including those from National Centers for Environmental Prediction(NCEP)reanalysis,Hangzhou and Huzhou new-generation we...To analyze the effects of gas cannons on clouds and precipitation,multisource observational data,including those from National Centers for Environmental Prediction(NCEP)reanalysis,Hangzhou and Huzhou new-generation weather radars,laser disdrometer,ground-based automatic weather station,wind profiler radar,and Lin'an C-band dualpolarization radar,were adopted in this study.Based on the variational dual-Doppler wind retrieval method and the polarimetric variables obtained by the dual-polarization radar,we analyzed the microphysical processes and the variations in the macro-and microphysical quantities in clouds from the perspective of the synoptic background before precipitation enhancement,the polarization echo characteristics before,during and after enhancement,and the evolution of the fine three-dimensional kinematic structure and the microphysical structure.The results show that the precipitation enhancement operation promoted the development of radar echoes and prolonged their duration,and both the horizontal and vertical wind speeds increased.The dual-polarization radar echo showed that the diameter of the precipitation particles increased,and the concentration of raindrops increased after precipitation enhancement.The raindrops were lifted to a height corresponding to 0 to-20℃due to vertical updrafts.Based on the disdrometer data during precipitation enhancement,the concentration of small raindrops(lgN_(w))showed a significant increase,and the mass-weighted diameter D_(m)value decreased,indicating that the precipitation enhancement operation played a certain“lubricating”effect.After the precipitation enhancement,the concentration of raindrops did not change much compared with that during the enhancement process,while the Dm increased,corresponding to an increase in rain intensity.The results suggest the positive effect of gas cannons on precipitation enhancement.展开更多
Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production.This paper develops a second-order ...Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production.This paper develops a second-order fulldiscretization method(2ndFDM)-based 3-D stability prediction model for simultaneous optimization of spindle speed,axial cutting depth and radial cutting depth.The optimal machining parameters in each pass are obtained to achieve the minimum production time comprehensive considering constraints of 3-D stability,machine tool performance,tool life and machining requirements.A cloud drop-enabled particle swarm optimization(CDPSO)algorithm is proposed to solve the developed machining parameter optimization,and 13 benchmark problems are used to evaluate CDPSO algorithm.Numerical results show that CDPSO algorithm has a certain advantage in computational cost as well as comparable search quality and robustness.A demonstrative example is provided.展开更多
A set of microphysics equations is scaled based on the convective length and velocity scales. Comparisons are made among the dynamical transport and various microphysical processes. From the scaling analysis, it becom...A set of microphysics equations is scaled based on the convective length and velocity scales. Comparisons are made among the dynamical transport and various microphysical processes. From the scaling analysis, it becomes apparent which parameterized microphysical processes present off-scaled influences in the integration of the set of microphysics equations. The variabilities of the parameterized microphysical processes are also studied using the approach of a controlled parameter space. Given macroscopic dynamic and thermodynamic conditions in different regions of convective storms, it is possible to analyze and compare vertical profiles of these processes. Bulk diabatic heating profiles for a cumulus convective updraft and downdraft are also derived from this analysis. From the two different angles, the scale analysis and the controlled-parameter space approach can both provide an insight into and an understanding of microphysics parameterizations.展开更多
Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems ...Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection methods.Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical systems.Malicious software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the information.The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)model.The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform.To attain this,Zscore data normalization is utilized to scale the data into a uniform format.In addition,BDDBN model is also exploited for recognition and categorization of malware.To effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is applied.This scenario enhances the classification results and also shows the novelty of current study.The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.展开更多
LiDAR(Light Detection and Ranging)technology is now commonly used in geospatial technology when it is an active remote sensing technology and capable of collecting data on large areas.However,with a large dataset of m...LiDAR(Light Detection and Ranging)technology is now commonly used in geospatial technology when it is an active remote sensing technology and capable of collecting data on large areas.However,with a large dataset of measurement areas,selecting and using the data in accordance with the research purpose takes a lot of time to conduct the classification of points.The algorithm selection in LiDAR data processing problem is important in the use of lidar data.EM(Expectation Maximization)algorithm is a typical algorithm of data mining,with the advantage of being easy to install and understand the algorithm used in classification problems.In this study,the author improved the EM algorithm in classification of lidar point cloud by using scheduling parameters to reduce the convergence time of the algorithm.展开更多
针对当前电离层现报和预报数据精度不足、多源异构数据融合困难等问题,本文基于全球卫星导航系统(Global Navigation Satellite System,GNSS)和垂测观测数据,设计了一套电离层融合处理和预报系统。采用限带卡尔曼滤波模型,基于银河麒麟...针对当前电离层现报和预报数据精度不足、多源异构数据融合困难等问题,本文基于全球卫星导航系统(Global Navigation Satellite System,GNSS)和垂测观测数据,设计了一套电离层融合处理和预报系统。采用限带卡尔曼滤波模型,基于银河麒麟操作系统和云计算平台,利用容器云、高可用、分布式架构实现全球及中国周边区域电离层总电子含量(Total Electron Content,TEC)、F2层临界频率(Critical Frequency of the F2 Layer,foF2)及电子密度的高精度现报和预报。实验测试表明,该系统现报延迟约5 min、空间时间分辨率达到5°×2.5°×15 min,较传统数据处理方法有较大提升。系统支持三维电子密度可视化,为电离层研究、卫星导航修正、短波通信及地基雷达等应用提供可靠数据支撑,为无线电系统应用提供高精度、高时效的电离层环境信息服务。展开更多
针对菇房内杏鲍菇表型参数测量任务中,由于扫描设备视角受限,扫描的杏鲍菇点云出现残缺问题,基于AdaPoinTr(Adaptive geometry-aware point transformers)提出了改进的SwinPoinTr模型,实现了对残缺杏鲍菇点云的准确补全和杏鲍菇表型参...针对菇房内杏鲍菇表型参数测量任务中,由于扫描设备视角受限,扫描的杏鲍菇点云出现残缺问题,基于AdaPoinTr(Adaptive geometry-aware point transformers)提出了改进的SwinPoinTr模型,实现了对残缺杏鲍菇点云的准确补全和杏鲍菇表型参数的测量。该方法在使用提出的特征重塑模块的基础上,构建具有几何感知能力的层次化Transformer编码模块,提高了模型对输入点云的利用率和模型捕捉点云细节特征的能力。然后基于泊松重建方法完成了补全点云表面重建,并测量到杏鲍菇表型参数。实验结果表明,本文所提算法在残缺杏鲍菇点云补全任务中,模型倒角距离为1.316×10^(-4),地球移动距离为21.3282,F1分数为87.87%。在表型参数估测任务中,模型对杏鲍菇菌高、体积、表面积估测结果的决定系数分别为0.9582、0.9596、0.9605,均方根误差分别为4.4213 mm、10.8185 cm^(3)、7.5778 cm^(2)。结果证实了该研究方法可以有效地补全残缺的杏鲍菇点云,可以为菇房内杏鲍菇表型参数测量提供基础。展开更多
基金National Natural Science Foundation of China(41075083)On the Techniques of 0-6h Quantitative Forecast of Rain(Snow)(GYHY201006001)Science and Technology Planning Project for Guangdong Province(2011A032100006,2012A061400012)
文摘In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The results show that the model nowcasting is improved when only the cloud water and rain water are adjusted or all of the cloud water, rain water and vapor are adjusted in the initial field. The forecasting of the former(latter) approach during 0-3(0-6) hours is significantly improved. Furthermore, for the forecast for 0-3 hours, the latter approach is better than the former. Compared with the forecasting results for which the vapor of the model initial field is adjusted by the background vapor with those by the revised vapor, the nowcasting of the revised vapor is much better than that of background vapor. Analysis of the reasons indicated that when the vapor is adjusted in the model initial field, especially when the saturated vapor is considered, the forecasting of the vapor field is significantly affected. The changed vapor field influences the circulation, which in turn improves the model forecasting of radar reflectivity and rainfall.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41275147 and 41875173)the STS Program of Inner Mongolia Meteorological Service, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences and Institute of Atmospheric Physics, Chinese Academy of Sciences (Grant No. 2021CG0047)
文摘The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.
基金funded by the National Natural Science Foundation of China(42071014).
文摘Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.
基金supported by the National Natural Science Foundation of China under Grant No.62471381the ZTE Industry-University-Institute Cooperation Funds.
文摘In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.
基金supported by the National Natural Science Foundation of China(Project No.42171361)the Research Grants Council of the Hong Kong Special Administrative Region,China,under Project PolyU 25211819the Hong Kong Polytechnic University under Projects 1-ZE8E and 1-ZVN6.
文摘Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.
文摘Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.
基金Speical Scientific Research Project for Public Welfare (Meteorological) Industry (GYHY200906002)Project of National Natural Science Foundation (41075083)
文摘Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.
基金National Natural Science Foundation of China(41675029)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0998)+1 种基金Science and Technology Program of Huzhou(2021GZ14,2020GZ31)Science and Technology(Key)Program of Zhejiang Meteorological Service(2021ZD27)。
文摘To analyze the effects of gas cannons on clouds and precipitation,multisource observational data,including those from National Centers for Environmental Prediction(NCEP)reanalysis,Hangzhou and Huzhou new-generation weather radars,laser disdrometer,ground-based automatic weather station,wind profiler radar,and Lin'an C-band dualpolarization radar,were adopted in this study.Based on the variational dual-Doppler wind retrieval method and the polarimetric variables obtained by the dual-polarization radar,we analyzed the microphysical processes and the variations in the macro-and microphysical quantities in clouds from the perspective of the synoptic background before precipitation enhancement,the polarization echo characteristics before,during and after enhancement,and the evolution of the fine three-dimensional kinematic structure and the microphysical structure.The results show that the precipitation enhancement operation promoted the development of radar echoes and prolonged their duration,and both the horizontal and vertical wind speeds increased.The dual-polarization radar echo showed that the diameter of the precipitation particles increased,and the concentration of raindrops increased after precipitation enhancement.The raindrops were lifted to a height corresponding to 0 to-20℃due to vertical updrafts.Based on the disdrometer data during precipitation enhancement,the concentration of small raindrops(lgN_(w))showed a significant increase,and the mass-weighted diameter D_(m)value decreased,indicating that the precipitation enhancement operation played a certain“lubricating”effect.After the precipitation enhancement,the concentration of raindrops did not change much compared with that during the enhancement process,while the Dm increased,corresponding to an increase in rain intensity.The results suggest the positive effect of gas cannons on precipitation enhancement.
基金supported partially by the National Science Foundation of China(No.51775279)National Defense Basic Scientific Research Program of China(No. JCKY201605B006)+1 种基金Fundamental Research Funds for the Central Universities(No. NT2021019)Jiangsu Industry Foresight and Common Key Technology (No. BE2018127)
文摘Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production.This paper develops a second-order fulldiscretization method(2ndFDM)-based 3-D stability prediction model for simultaneous optimization of spindle speed,axial cutting depth and radial cutting depth.The optimal machining parameters in each pass are obtained to achieve the minimum production time comprehensive considering constraints of 3-D stability,machine tool performance,tool life and machining requirements.A cloud drop-enabled particle swarm optimization(CDPSO)algorithm is proposed to solve the developed machining parameter optimization,and 13 benchmark problems are used to evaluate CDPSO algorithm.Numerical results show that CDPSO algorithm has a certain advantage in computational cost as well as comparable search quality and robustness.A demonstrative example is provided.
基金Acknowledgments. Thanks to Dr. Alexander MacDonald of NOAA/FSL for his support throughout this study, and to Professors William Cotton. Roger Pielke. Wayne Schubert of Colorado State University, and to Dr. Fanyou Kong of University of Oklahoma and Mr. Hu
文摘A set of microphysics equations is scaled based on the convective length and velocity scales. Comparisons are made among the dynamical transport and various microphysical processes. From the scaling analysis, it becomes apparent which parameterized microphysical processes present off-scaled influences in the integration of the set of microphysics equations. The variabilities of the parameterized microphysical processes are also studied using the approach of a controlled parameter space. Given macroscopic dynamic and thermodynamic conditions in different regions of convective storms, it is possible to analyze and compare vertical profiles of these processes. Bulk diabatic heating profiles for a cumulus convective updraft and downdraft are also derived from this analysis. From the two different angles, the scale analysis and the controlled-parameter space approach can both provide an insight into and an understanding of microphysics parameterizations.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(61/43).Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R319)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR24).
文摘Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection methods.Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical systems.Malicious software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the information.The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)model.The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform.To attain this,Zscore data normalization is utilized to scale the data into a uniform format.In addition,BDDBN model is also exploited for recognition and categorization of malware.To effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is applied.This scenario enhances the classification results and also shows the novelty of current study.The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.
基金the Hanoi University of Mining and Geology under project number T19-03Ministry of Education and Training number CT.2019.01.07。
文摘LiDAR(Light Detection and Ranging)technology is now commonly used in geospatial technology when it is an active remote sensing technology and capable of collecting data on large areas.However,with a large dataset of measurement areas,selecting and using the data in accordance with the research purpose takes a lot of time to conduct the classification of points.The algorithm selection in LiDAR data processing problem is important in the use of lidar data.EM(Expectation Maximization)algorithm is a typical algorithm of data mining,with the advantage of being easy to install and understand the algorithm used in classification problems.In this study,the author improved the EM algorithm in classification of lidar point cloud by using scheduling parameters to reduce the convergence time of the algorithm.
文摘针对当前电离层现报和预报数据精度不足、多源异构数据融合困难等问题,本文基于全球卫星导航系统(Global Navigation Satellite System,GNSS)和垂测观测数据,设计了一套电离层融合处理和预报系统。采用限带卡尔曼滤波模型,基于银河麒麟操作系统和云计算平台,利用容器云、高可用、分布式架构实现全球及中国周边区域电离层总电子含量(Total Electron Content,TEC)、F2层临界频率(Critical Frequency of the F2 Layer,foF2)及电子密度的高精度现报和预报。实验测试表明,该系统现报延迟约5 min、空间时间分辨率达到5°×2.5°×15 min,较传统数据处理方法有较大提升。系统支持三维电子密度可视化,为电离层研究、卫星导航修正、短波通信及地基雷达等应用提供可靠数据支撑,为无线电系统应用提供高精度、高时效的电离层环境信息服务。
