Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In exist...Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.展开更多
This paper presents a comprehensive evaluation of Electric Spring(ES)performance through simulation experiments and polynomial regression modelling.The study pri-marily aims to assess how varying load impedance ratios...This paper presents a comprehensive evaluation of Electric Spring(ES)performance through simulation experiments and polynomial regression modelling.The study pri-marily aims to assess how varying load impedance ratios impact ES performance,develop a polynomial regression model to predict the relationship between ES parameters and the ratio of non-critical to critical load impedance,and validate the model against simulation results.Detailed simulations were performed to analyse the effects of different impedance ratios on ES behaviour.Subsequently,a polynomial regression model was formulated to accurately capture the relationship between the ES parameters and impedance ratios.The findings indicate that the polynomial regression model effectively predicts ES perfor-mance,with the predicted values closely matching the simulation results.The validation process confirms the model's accuracy and reliability,demonstrating its potential for practical applications in optimising ES performance under various impedance conditions.This study offers valuable insights into the enhancement of ES systems through precise modelling and analysis,contributing to improved stability and efficiency in power systems.展开更多
Evaluating and understanding the accuracy of cloud microphysical(MP) schemes in numerical weather prediction(NWP) models is crucial for assimilating satellite radiance data under cloudy conditions. This study leverage...Evaluating and understanding the accuracy of cloud microphysical(MP) schemes in numerical weather prediction(NWP) models is crucial for assimilating satellite radiance data under cloudy conditions. This study leverages surface observations, radar reflectivity data, and Himawari-8 satellite radiance data from both water vapor and window channels to assess the performance of four prevalent cloud MP schemes: WSM6, WDM6, Thompson, and Morrison, as implemented in the Weather Research and Forecasting(WRF) model. The assessment focuses on two typical heavy rain events in South China: a warm-sector torrential rainfall(WSTR) event and a squall line(SL) event. The findings reveal that the cloud MP schemes exhibit varying levels of accuracy across the two events. Notably, for the WSTR event, the WDM6scheme shows the closest alignment with observed rainfall in terms of precipitation forecast. In contrast, the Thompson scheme outperforms the others during the SL event. The simulation of infrared(IR) radiance data from cloud and rain areas remains a significant challenge, particularly for ice clouds, which exhibit greater forecast uncertainty compared to water clouds. Identifying the optimal scheme for describing the full cloud process during rainfall events remains challenging among the evaluated MP schemes. Specifically, the WDM6 scheme stands out in forecasting clear skies and water clouds,while the Morrison and Thompson schemes are found to be more adept at predicting ice clouds. The discrepancies observed between the accuracy of precipitation forecast and cloud prediction highlight the need for further research to identify an MP scheme that effectively balances precipitation forecast with accurate cloudy radiative transfer(RT) simulation for data assimilation(DA). This research offers valuable insights into the selection of cloud microphysics parameterization schemes for all-sky radiance assimilation, particularly under diverse rainfall processes.展开更多
We calculate the electrical and thermal conductivity of hydrogen for a wide range of densities and temperatures by using molecular dynamics simulations informed by density functional theory.On the basis of the corresp...We calculate the electrical and thermal conductivity of hydrogen for a wide range of densities and temperatures by using molecular dynamics simulations informed by density functional theory.On the basis of the corresponding extended ab initio data set,we construct interpolation formulas covering the range from low-density,high-temperature to high-density,low-temperature plasmas.Our conductivity model repro-duces the well-known limits of the Spitzer and Ziman theory.We compare with available experimental data andfind very good agreement.The new conductivity model can be applied,for example,in dynamo simulations for magneticfield generation in gas giant planets,brown dwarfs,and stellar envelopes.展开更多
The operation time of external trucks in a container terminal is one of port operation key performance indicators concerned by port operators,external truck operators and related government authorities.With the traffi...The operation time of external trucks in a container terminal is one of port operation key performance indicators concerned by port operators,external truck operators and related government authorities.With the traffic big data combined with the operation characteristics of the container terminal,the system dynamics method is used to build the simulation model of the operation system for external trucks.The simulation results of the operation time of external trucks are consistent with the actual situation,which provides an effective way to eliminate the“black box”of the operation time of the external trucks.The model can also be applied in multiple scenarios by using the traffic big data,and the simulation results can be adopted by the relevant organizations.展开更多
Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for a...Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for analyzing the data of N-of-1 trials.This simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials.Methods:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 system.The missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 software.Bayesian models are built to estimate the posterior means by Winbugs 14 software.Results:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the results.Therapeutic effects may be underestimated when the coefficient of autocorrelation increases and no filling is used.However,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression filling.In the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression filling.When a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the effect.In this case,the estimated effect after regression filling is closer to the actual effect compared to mean filling.Conclusion:Data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 trials.The present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.展开更多
An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advan...An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.展开更多
The MM5 and its four dimensional variational data assimilation (4D-Var) system are used in this paper. Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re...The MM5 and its four dimensional variational data assimilation (4D-Var) system are used in this paper. Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, the authors generate an optimal initial condition for a typhoon by using the bogus data assimilation (BDA) scheme. BDA is able to recover many of the structural features of typhoons including a warm-core vertex, the correct center position, and the strong circulation. As a result of BDA using a bogus surface low, dramatic improvement is achieved in the 72 h prediction of typhoon Herb. Through several cases, the initialization by BDA effectively generates the harmonious inner structure of the typhoon, but which is lacking in the original analysis field. Therefore the intensity forecast is improved greatly. Some improvements are made in the track forecast, but more work still needs to be done.展开更多
Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity ...Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.展开更多
Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inve...Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.展开更多
This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences(CAMS)climate system model(CAMS-CSM),which are contributing to phase 6 of the Coupled Model Intercomparison Pro...This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences(CAMS)climate system model(CAMS-CSM),which are contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6).The model description,experiment design and model outputs are presented.Three members’historical experiments are conducted by CAMS-CSM,with two members starting from different initial conditions,and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions.The outputs of the historical experiments are also validated using observational data.It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities,including the surface air temperature,precipitation,and the equatorial thermocline.The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM.There are still some biases in the model that need further improvement.This paper can help the users to better understand the performance and the datasets of CAMS-CSM.展开更多
The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the c...The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.展开更多
In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Rea...In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.展开更多
Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitati...Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.展开更多
When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive ...When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.展开更多
The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of win...The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.展开更多
Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing rese...Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.展开更多
Typhoon Rananim （0414） has been simulated by using the non-hydrostatic Advanced Regional Prediction System （ARPS） from Center of Analysis and Prediction of Storms （CAPS）. The prediction of Rananim has generally ...Typhoon Rananim （0414） has been simulated by using the non-hydrostatic Advanced Regional Prediction System （ARPS） from Center of Analysis and Prediction of Storms （CAPS）. The prediction of Rananim has generally been improved with ARPS using the new generation CINRAD Doppler radar data. Numerical experiments with or without using the radar data have shown that model initial fields with the assimilated radar radial velocity data in ARPS can change the wind field at the middle and high levels of the troposphere; fine characteristics of the tropical cyclone （TC） are introduced into the initial wind, the x component of wind speed south of the TC is increased and so is the y component west of it. They lead to improved forecasting of TC tracks for the time after landfall. The field of water vapor mixing ratio, temperature, cloud water mixing ratio and rainwater mixing ratio have also been improved by using radar refiectivity data. The model＇s initial response to the introduction of hydrometeors has been increased. It is shown that horizontal model resolution has a significant impact on intensity forecasts, by greatly improving the forecasting of TC rainfall, and heavy rainstorm of the TC specially, as well as its distribution and variation with time.展开更多
In the last decade,building energy simulation( BES)became a central component in building energy systems’ design and optimization. For each building location,BES requires one year of hourly weather data. Most buildin...In the last decade,building energy simulation( BES)became a central component in building energy systems’ design and optimization. For each building location,BES requires one year of hourly weather data. Most buildings are designed to last50 + years,consequently,the building design phase should include BES with future weather files considering climate change.This paper presents a comparative study of two methods to produce future climate hourly data files for BES: Morphing and typical meteorological year of future climate( F-TMY). The study uses data from a high-resolution( 9 km) regional climate atmospheric model simulation of Iberia,spanning 10 years of historical and future hourly data. This study compares both methods by analyzing anomalies in air temperature,and the impact in BES predictions of annual and peak energy consumption for space heating, cooling and ventilation in 4 buildings.Additionally, this study performs a sensitivity analysis of morphing method. The analysis shows that F-TMY is representative of the multi-year simulation for BES applications.A high-quality Morphed TMY weather file has a similar performance compared to F-TMY( average difference: 8% versus 7%). Morphing based on different baseline climates,low-grid resolution and/or outdated climate projections leads to BES average differences of 16%~20%.展开更多
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金supported by National Natural Sciences Foundation of China(No.62271165,62027802,62201307)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030297)+2 种基金the Shenzhen Science and Technology Program ZDSYS20210623091808025Stable Support Plan Program GXWD20231129102638002the Major Key Project of PCL(No.PCL2024A01)。
文摘Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.
文摘This paper presents a comprehensive evaluation of Electric Spring(ES)performance through simulation experiments and polynomial regression modelling.The study pri-marily aims to assess how varying load impedance ratios impact ES performance,develop a polynomial regression model to predict the relationship between ES parameters and the ratio of non-critical to critical load impedance,and validate the model against simulation results.Detailed simulations were performed to analyse the effects of different impedance ratios on ES behaviour.Subsequently,a polynomial regression model was formulated to accurately capture the relationship between the ES parameters and impedance ratios.The findings indicate that the polynomial regression model effectively predicts ES perfor-mance,with the predicted values closely matching the simulation results.The validation process confirms the model's accuracy and reliability,demonstrating its potential for practical applications in optimising ES performance under various impedance conditions.This study offers valuable insights into the enhancement of ES systems through precise modelling and analysis,contributing to improved stability and efficiency in power systems.
基金Guangdong Province Natural Science Foundation Youth Science Fund (2021A1515110944)。
文摘Evaluating and understanding the accuracy of cloud microphysical(MP) schemes in numerical weather prediction(NWP) models is crucial for assimilating satellite radiance data under cloudy conditions. This study leverages surface observations, radar reflectivity data, and Himawari-8 satellite radiance data from both water vapor and window channels to assess the performance of four prevalent cloud MP schemes: WSM6, WDM6, Thompson, and Morrison, as implemented in the Weather Research and Forecasting(WRF) model. The assessment focuses on two typical heavy rain events in South China: a warm-sector torrential rainfall(WSTR) event and a squall line(SL) event. The findings reveal that the cloud MP schemes exhibit varying levels of accuracy across the two events. Notably, for the WSTR event, the WDM6scheme shows the closest alignment with observed rainfall in terms of precipitation forecast. In contrast, the Thompson scheme outperforms the others during the SL event. The simulation of infrared(IR) radiance data from cloud and rain areas remains a significant challenge, particularly for ice clouds, which exhibit greater forecast uncertainty compared to water clouds. Identifying the optimal scheme for describing the full cloud process during rainfall events remains challenging among the evaluated MP schemes. Specifically, the WDM6 scheme stands out in forecasting clear skies and water clouds,while the Morrison and Thompson schemes are found to be more adept at predicting ice clouds. The discrepancies observed between the accuracy of precipitation forecast and cloud prediction highlight the need for further research to identify an MP scheme that effectively balances precipitation forecast with accurate cloudy radiative transfer(RT) simulation for data assimilation(DA). This research offers valuable insights into the selection of cloud microphysics parameterization schemes for all-sky radiance assimilation, particularly under diverse rainfall processes.
基金supported by the Priority Program SPP 1992 of the German Science Foundation(DFG)The Diversity of Exoplanets under project number 362460292.
文摘We calculate the electrical and thermal conductivity of hydrogen for a wide range of densities and temperatures by using molecular dynamics simulations informed by density functional theory.On the basis of the corresponding extended ab initio data set,we construct interpolation formulas covering the range from low-density,high-temperature to high-density,low-temperature plasmas.Our conductivity model repro-duces the well-known limits of the Spitzer and Ziman theory.We compare with available experimental data andfind very good agreement.The new conductivity model can be applied,for example,in dynamo simulations for magneticfield generation in gas giant planets,brown dwarfs,and stellar envelopes.
文摘The operation time of external trucks in a container terminal is one of port operation key performance indicators concerned by port operators,external truck operators and related government authorities.With the traffic big data combined with the operation characteristics of the container terminal,the system dynamics method is used to build the simulation model of the operation system for external trucks.The simulation results of the operation time of external trucks are consistent with the actual situation,which provides an effective way to eliminate the“black box”of the operation time of the external trucks.The model can also be applied in multiple scenarios by using the traffic big data,and the simulation results can be adopted by the relevant organizations.
基金supported by the National Natural Science Foundation of China (No.81973705).
文摘Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for analyzing the data of N-of-1 trials.This simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials.Methods:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 system.The missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 software.Bayesian models are built to estimate the posterior means by Winbugs 14 software.Results:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the results.Therapeutic effects may be underestimated when the coefficient of autocorrelation increases and no filling is used.However,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression filling.In the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression filling.When a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the effect.In this case,the estimated effect after regression filling is closer to the actual effect compared to mean filling.Conclusion:Data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 trials.The present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.
文摘An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.
文摘The MM5 and its four dimensional variational data assimilation (4D-Var) system are used in this paper. Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, the authors generate an optimal initial condition for a typhoon by using the bogus data assimilation (BDA) scheme. BDA is able to recover many of the structural features of typhoons including a warm-core vertex, the correct center position, and the strong circulation. As a result of BDA using a bogus surface low, dramatic improvement is achieved in the 72 h prediction of typhoon Herb. Through several cases, the initialization by BDA effectively generates the harmonious inner structure of the typhoon, but which is lacking in the original analysis field. Therefore the intensity forecast is improved greatly. Some improvements are made in the track forecast, but more work still needs to be done.
基金funded by the National Natural Science Foundation of China(Grant No.52274048)Beijing Natural Science Foundation(Grant No.3222037)+1 种基金the CNPC 14th Five-Year Perspective Fundamental Research Project(Grant No.2021DJ2104)the Science Foundation of China University of Petroleum-Beijing(No.2462021YXZZ010).
文摘Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.
基金the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404).
文摘Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.
基金supported by the National Key Research and Development Program of China(Grant No.2019YFC1510001)the National Natural Science Foundation of China(Grant No.91637210)+1 种基金the Basic Research Fund of CAMS(Grant No.2018Z007)the Jiangsu Collaborative Innovation Center for Climate Change。
文摘This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences(CAMS)climate system model(CAMS-CSM),which are contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6).The model description,experiment design and model outputs are presented.Three members’historical experiments are conducted by CAMS-CSM,with two members starting from different initial conditions,and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions.The outputs of the historical experiments are also validated using observational data.It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities,including the surface air temperature,precipitation,and the equatorial thermocline.The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM.There are still some biases in the model that need further improvement.This paper can help the users to better understand the performance and the datasets of CAMS-CSM.
基金supported by the National Science and Technology Major Project,China(No.2017-III-0010-0036).
文摘The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.
基金International Partnership Program of Chinese Academy of Sciences,No.131551KYSB20160002 National Natural Science Foundation of China,No.41401602+2 种基金 Natural Science Basic Research Plan in Shaanxi Province of China,No.2014JQ2-4021 Key Scientific and Technological Innovation Team Plan of Shaanxi Province,No.2014KCT-27 Graduate Student Innovation Project of Northwest University,No.YZZ15011
文摘In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.
文摘Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.
基金supported by the New Century Excellent Talents in University(NCET-09-0396)the National Science&Technology Key Projects of Numerical Control(2012ZX04014-031)+1 种基金the Natural Science Foundation of Hubei Province(2011CDB279)the Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province,China(2010CDA067)
文摘When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.
基金financially supported by the National Natural Science Foundation of China (Grant No.52378329)。
文摘The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.
基金supported by the Swiss National Science Foundation
文摘Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.
基金Technical Plan Key Project of Zhejiang Province (2006C13025)Key Subsidiary Project for Meteorological Science of Wenzhou (S200601)Technical Plan Key Project of Wenzhou (S2003A011)
文摘Typhoon Rananim （0414） has been simulated by using the non-hydrostatic Advanced Regional Prediction System （ARPS） from Center of Analysis and Prediction of Storms （CAPS）. The prediction of Rananim has generally been improved with ARPS using the new generation CINRAD Doppler radar data. Numerical experiments with or without using the radar data have shown that model initial fields with the assimilated radar radial velocity data in ARPS can change the wind field at the middle and high levels of the troposphere; fine characteristics of the tropical cyclone （TC） are introduced into the initial wind, the x component of wind speed south of the TC is increased and so is the y component west of it. They lead to improved forecasting of TC tracks for the time after landfall. The field of water vapor mixing ratio, temperature, cloud water mixing ratio and rainwater mixing ratio have also been improved by using radar refiectivity data. The model＇s initial response to the introduction of hydrometeors has been increased. It is shown that horizontal model resolution has a significant impact on intensity forecasts, by greatly improving the forecasting of TC rainfall, and heavy rainstorm of the TC specially, as well as its distribution and variation with time.
文摘In the last decade,building energy simulation( BES)became a central component in building energy systems’ design and optimization. For each building location,BES requires one year of hourly weather data. Most buildings are designed to last50 + years,consequently,the building design phase should include BES with future weather files considering climate change.This paper presents a comparative study of two methods to produce future climate hourly data files for BES: Morphing and typical meteorological year of future climate( F-TMY). The study uses data from a high-resolution( 9 km) regional climate atmospheric model simulation of Iberia,spanning 10 years of historical and future hourly data. This study compares both methods by analyzing anomalies in air temperature,and the impact in BES predictions of annual and peak energy consumption for space heating, cooling and ventilation in 4 buildings.Additionally, this study performs a sensitivity analysis of morphing method. The analysis shows that F-TMY is representative of the multi-year simulation for BES applications.A high-quality Morphed TMY weather file has a similar performance compared to F-TMY( average difference: 8% versus 7%). Morphing based on different baseline climates,low-grid resolution and/or outdated climate projections leads to BES average differences of 16%~20%.
