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MAO Duan-qian ZHANG Jie BAO Hao-sheng 《中国地理科学(英文版)》2005,15(3):275-280
1IN T R O D U C T IO NAn image isa setofbelief,s ideasand impressionsthatapersonholdsaboutan object(KOTLER,1991).Similar-lydefinitionistheimage of a destinationas thesum ofbelief,sideasand impressionsthatpeoplehave ofa placeor destination(CROMPTON,1979;KO… 相似文献
73.
沉积物粒度分析在厦门市第四纪环境研究和地层划分对比中的应用 总被引：6，自引：0，他引：6
采用MS2000型激光粒度分析仪进行测试，利用计算机粒度分析软件对数据进行整理和计算，绘制出样品的频率曲线、概率累积曲线以及粒度众数位值曲线等图件并进行沉积环境分析。厦门地区第四纪环境演化经历了中更新世同安组(含泥中粗砂、含泥细中砂、砂为主)海积一冲积一更新统上部龙海组(含泥粗中砂、含泥细中砂、含泥粗砂、粗砂、细中砂、含细砾中粗砂、中细砂等)冲积一洪冲积一更新世上部东山组(砂、砂砾、含泥中粗砂、粗砂、泥质中细砂、细砂、中粗砂等)冲洪积及部分海积(粉砂、细粉砂、含碳质泥、粘土、淤泥质粘土、粉砂质粘土等)一全新世长乐组(粉砂，淤泥质粘土、粉砂质粘土、碳质粘土、含砂淤泥质粘土，粉砂质亚粘土、粉细砂亚粘土等)海湾沉积。在解释环境变化的同时，说明粒度变化曲线在一定程度上可以作为地层划分的依据之一，并以此对研究区地层进行了详细划分。 相似文献
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本文通过对成都10个重污染日进行天气学分析,将污染浓度与气象要素进行聚类、研究了重污染日形成原因及污染浓度与气象要素的关系。在此基础上,建立了SO_2、TSP日平均浓度分级预报方程。 相似文献
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This study presents a finite element (FE) micromechanical modelling approach for the simulation of linear and damage‐coupled viscoelastic behaviour of asphalt mixture. Asphalt mixture is a composite material of graded aggregates bound with mastic (asphalt and fine aggregates). The microstructural model of asphalt mixture incorporates an equivalent lattice network structure whereby intergranular load transfer is simulated through an effective asphalt mastic zone. The finite element model integrates the ABAQUS user material subroutine with continuum elements for the effective asphalt mastic and rigid body elements for each aggregate. A unified approach is proposed using Schapery non‐linear viscoelastic model for the rate‐independent and rate‐dependent damage behaviour. A finite element incremental algorithm with a recursive relationship for three‐dimensional (3D) linear and damage‐coupled viscoelastic behaviour is developed. This algorithm is used in a 3D user‐defined material model for the asphalt mastic to predict global linear and damage‐coupled viscoelastic behaviour of asphalt mixture. For linear viscoelastic study, the creep stiffnesses of mastic and asphalt mixture at different temperatures are measured in laboratory. A regression‐fitting method is employed to calibrate generalized Maxwell models with Prony series and generate master stiffness curves for mastic and asphalt mixture. A computational model is developed with image analysis of sectioned surface of a test specimen. The viscoelastic prediction of mixture creep stiffness with the calibrated mastic material parameters is compared with mixture master stiffness curve over a reduced time period. In regard to damage‐coupled viscoelastic behaviour, cyclic loading responses of linear and rate‐independent damage‐coupled viscoelastic materials are compared. Effects of particular microstructure parameters on the rate‐independent damage‐coupled viscoelastic behaviour are also investigated with finite element simulations of asphalt numerical samples. Further study describes loading rate effects on the asphalt viscoelastic properties and rate‐dependent damage behaviour. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
78.
Dong-Mei Qin Ping Guo Zhan-Yi Hu Yong-Heng ZhaoNational Laboratory of Pattern Recognition Laboratory Institute of Automation Chinese Academy of Sciences Beijing dmqin@nlpr.ia.ac.cnDepartment of Computer Sciences Beijing Normal University Beijing National Astronomical Observatories Chinese Academy of Sciences Beijing 《中国天文和天体物理学报》2003,3(3)
For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1 相似文献
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