高精度直升机机载LiDAR在高速公路改扩建勘测中的应用可行性研究          下载免费PDF全文

郑伟安 《山东国土资源》2018,34(8)

机载LiDAR在公路勘测方面的用途日益广泛。该文对直升机机载LiDAR在高速公路改扩建中的应用技术路线可行性进行了研究论证,从地面控制测量、点云数据获取、点云数据处理、成果应用等多个方面进行了阐述,通过分析LiDAR点云数据在5种不同地面控制点布设方案校正下的点云数据精度,论证了利用地面控制点对直升机机载LiDAR点云数据进行平面和高程校正的可行性。  相似文献   

鄂尔多斯地块周缘垂直形变场负位错反演与应变积累研究     

张希  刘立炜  郝明  白卓立 《大地测量与地球动力学》2019,39(6):551-556

利用鄂尔多斯地块及其周缘1970～2014年的垂直形变速率场资料，借助负位错反演研究该区域长期应变积累。结果表明，地块东北缘山西断陷带中北段年均能量积累增量、剪应力强度都较高，西南缘六盘山断裂与渭河断裂西段次之；山西断陷带中南段至晋陕交界处年均剪应力强度较高且显示一定程度的能量积累；西秦岭构造区尤其西秦岭北缘断裂西段、晋冀蒙交界区也反映一定程度的能量积累特性。  相似文献   

Developing constitutive models from EPR‐based self‐learning finite element analysis          下载免费PDF全文

Ali Nassr  Akbar Javadi  Asaad Faramarzi 《国际地质力学数值与分析法杂志》2018,42(3):401-417

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.  相似文献   

利用短期静态PPP结果反演海潮负荷位移     

魏国光  王琪洁  彭葳  郭良琦 《大地测量与地球动力学》2019,39(6):607-612

采用香港11个GPS测站的观测资料进行1 h、2 h、3 h和4h静态PPP解算，获得4组PPP坐标序列，利用调和分析求取11个测站处8个主要分潮的负荷位移参数（振幅和相位），将其与海潮模型计算的负荷位移参数进行对比，并比较分析PPP反演值与海潮模型值改正海潮负荷信号的效果。结果表明，垂直和水平方向上，不同PPP结果反演8个分潮的负荷位移分别具有约5 mm和7 mm的差异；PPP反演8个分潮垂向负荷位移优于全球海潮模型，但水平方向上的反演效果稍弱。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Muon density measurements with the KASCADE central detector     

《Astroparticle Physics》2002,16(4):183-386

Frequency distributions of local muon densities in high-energy extensive air showers (EAS) are presented as signature of the primary cosmic ray energy spectrum in the knee region. Together with the gross shower variables like shower core position, angle of incidence, and the shower sizes, the KASCADE experiment is able to measure local muon densities for two different muon energy thresholds. The spectra have been reconstructed for various core distances, as well as for particular subsamples, classified on the basis of the shower size ratio N_μ/N_e. The measured density spectra of the total sample exhibit clear kinks reflecting the knee of the primary energy spectrum. While relatively sharp changes of the slopes are observed in the spectrum of EAS with small values of the shower size ratio, no such feature is detected at EAS of large N_μ/N_e ratio in the energy range of 1–10 PeV. Comparing the spectra for various thresholds and core distances with detailed Monte Carlo simulations the validity of EAS simulations is discussed.  相似文献   

On the accretion geometry of Cyg X-1 in the low/hard state     

F. Eugenio Barrio  Chris Done  Sergei Nayakshin 《Monthly notices of the Royal Astronomical Society》2003,342(2):557-563

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Warm gas kinematics in shell galaxies     

R. Rampazzo  H. Plana  M. Longhetti  P. Amram  J. Boulesteix  J.-L. Gach  O. Hernandez 《Monthly notices of the Royal Astronomical Society》2003,343(3):819-830

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Deuterium fractionation on interstellar grains studied with the direct master equation approach     

Tatiana Stantcheva  Eric Herbst 《Monthly notices of the Royal Astronomical Society》2003,340(3):983-988

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The emission distribution in RR Pictoris     

L. Schmidtobreick  C. Tappert  I. Saviane 《Monthly notices of the Royal Astronomical Society》2003,342(1):145-150

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