带TMD的结构基于动力可靠性约束的优化设计   总被引：4，自引：0，他引：4  

李创第  黄天立  李暾  邹万杰  何益斌 《地震工程与工程振动》2004,24(6):121-125

本文在运用复模态法求得多自由度带TMD结构随机地震响应解析解的基础上．采用基于动力可靠性约束的优化设计方法对TMD装置参数的优化取值进行了系统研究,以结构最大位移响应的期望值为目标函数,以TMD装置响应的动力可靠性为约束条件,运用罚函数法获得到TMD装置的优化设计参数．并给出了算例,从而建立了带TMD结构基于动力可靠性约束的抗震优化设计的一整套方法,本文方法也可用于基础隔震结构、带TLD减震结构以及带TMD和TLD抗风结构的优化设计。  相似文献   

Distribution of the nonlinear random ocean wave period     

侯一筠  李明杰  宋贵霆  司广成  齐鹏  胡珀 《中国海洋湖沼学报》2009,27(4):919-923

Because of the intrinsic difficulty in determining distributions for wave periods, previous studies on wave period distribution models have not taken nonlinearity into account and have not performed well in terms of describing and statistically analyzing the probability density distribution of ocean waves. In this study, a statistical model of random waves is developed using Stokes wave theory of water wave dynamics. In addition, a new nonlinear probability distribution function for the wave period is presented with the parameters of spectral density width and nonlinear wave steepness, which is more reasonable as a physical mechanism. The magnitude of wave steepness determines the intensity of the nonlinear effect, while the spectral width only changes the energy distribution. The wave steepness is found to be an important parameter in terms of not only dynamics but also statistics. The value of wave steepness reflects the degree that the wave period distribution skews from the Cauchy distribution, and it also describes the variation in the distribution function, which resembles that of the wave surface elevation distribution and wave height distribution. We found that the distribution curves skew leftward and upward as the wave steepness increases. The wave period observations for the SZFII-1 buoy, made off the coast of Weihai (37°27.6′ N, 122°15.1′ E), China, are used to verify the new distribution. The coefficient of the correlation between the new distribution and the buoy data at different spectral widths (ν=0.3−0.5) is within the range of 0.968 6 to 0.991 7. In addition, the Longuet-Higgins (1975) and Sun (1988) distributions and the new distribution presented in this work are compared. The validations and comparisons indicate that the new nonlinear probability density distribution fits the buoy measurements better than the Longuet-Higgins and Sun distributions do. We believe that adoption of the new wave period distribution would improve traditional statistical wave theory.  相似文献   

土层随机地震反应的动力可靠度计算     

门玉明  黄义 《地球科学与环境学报》2002,24(1):43-45

研究了土层的随机地震反应分析方法 ,建立了基岩输入地震动加速度功率谱函数为白噪声和过滤白噪声时土层的动力可靠度计算理论  相似文献   

位错模式反演的算法研究   总被引：1，自引：0，他引：1  

李爽  许才军  王新洲 《大地测量与地球动力学》2003,23(1):53-57

综述了大地测量反演算法的发展状况，详细介绍了模拟退火，随机耗费和区间算法等3种优化方法，并基于位错模式，采用模拟的重力测量观测数据，比较了3种算法的反演效果，结果表明：模拟退火法优于随机耗费法，而区间算法的可靠性又优于模拟退火法，区间算法是最可靠的反演方法。  相似文献   

基于数值预报和随机森林算法的强对流天气分类预报技术   总被引：4，自引：2，他引：2  

李文娟  赵放  郦敏杰  陈列  彭霞云 《气象》2018,44(12):1555-1564

随机森林算法是当前得到较为广泛应用的机器学习方法之一,有着很高的预测精度,训练结果稳定,泛化能力强,解决多分类问题有明显优势。本文将随机森林算法应用于强对流的潜势预测和分类,分短时强降水、雷暴大风、冰雹和无强对流四种类别,基于2005—2016年NCEP 1°×1°再分析资料计算的对流指数和物理量,开展强对流天气的分类训练、0～12 h预报和检验,经2015—2016年独立测试样本检验表明,针对强对流发生站点的点对点检验,整体误判率为21. 9%,85次强对流过程基本无漏报,模型尤其适用于较大范围强对流天气。随机森林算法筛选的因子物理意义较为明确,和主观预报经验基本相符,模型准确率高,可用于日常业务。  相似文献   

高分辨率青藏高原历史月降水数据重建     

徐明  石玉立  王彬 《地理科学进展》2018,37(7):923-932

青藏高原对全球气候研究具有重要意义,而降水数据对水文、气象和生态等领域的研究也至关重要,且随着研究内容和尺度的变化,对高时空分辨率的历史降水数据的需求越发迫切。本文基于TRMM 3B43降水数据,采用随机森林算法,引入归一化植被指数(AVHRR NDVI)、高程(SRTM DEM)、坡度、坡向、经度、纬度6个地理因子,建立历史降水重建模型,获得1982-1997年分辨率为0.0833°的青藏高原年降水数据,然后根据比例系数法计算出月降水数据。为提高精度,利用站点数据对月降水数据进行校正。结果表明,该方法能简单有效地获得高时空分辨率的历史降水数据,决定系数R²大部分在0.4~0.9之间,平均值为0.6767,其中夏季效果最好,冬季效果最差;均方根误差RMSE和平均绝对误差MAE均在50 mm以下,RMSE均值为22.66 mm,MAE均值为15.97 mm;偏差Bias较小,基本在0.0~0.1之间。  相似文献   

Classifying natural-language spatial relation terms with random forest algorithm     

Shihong Du  Xiaonan Wang  Chen-Chieh Feng  Xiuyuan Zhang 《International journal of geographical information science》2017,31(3):542-568

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.  相似文献   

基于随机森林模型的珠江三角洲30 m格网人口空间化     

谭敏  刘凯  柳林  朱远辉  王大山 《地理科学进展》2017,36(10):1304-1312

人口空间化是实现人口统计数据与其他环境资源空间数据融合分析的有效途径。本文选取夜间灯光数据、道路网数据、水域分布数据、建成区数据、数字高程模型和地形坡度数据作为影响珠江三角洲人口分布的变量因子,利用随机森林模型对珠江三角洲2010年人口数据进行了30 m格网空间化,并将模拟结果与三个公开数据集作精度对比,最后基于随机森林模型的变量因子重要性分析珠江三角洲人口空间分布的影响因素。结果表明：本文模拟整体精度达到82.32%,均优于WorldPop数据集以及中国公里网格人口数据集,接近GPW数据集,而且在人口密度中等区域模拟精度最高;通过对变量因子重要性进行度量,发现夜间灯光强度是珠江三角洲人口分布的最重要指示性指标,到水域的距离、到建成区的距离和路网密度对珠江三角洲人口分布均具有重要作用。利用随机森林模型结合多源信息能够实现高空间分辨率的人口空间化,可为精细化城市管理提供重要数据源,也可为相关政策决策制定提供支持。  相似文献   

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data     

《International Journal of Applied Earth Observation and Geoinformation》2017

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.  相似文献   

波流共存场中多向随机波浪传播变形数学模型   总被引：1，自引：0，他引：1       下载免费PDF全文

郑金海  Hajime Mase 《水科学进展》2008,19(1):78-83

基于波作用量守恒方程建立了波流共存场中多向随机波浪传播变形数学模型,模型中考虑了波浪绕射的影响和水流引起的波浪弥散多普勒效应,应用包含水流和地形影响的激破波模式计算波浪破碎的能量耗散,采用一阶上迎风有限差分格式离散控制方程。分别计算了有无近岸流情况下单向和多向随机波浪的波高分布,考虑水流影响的数值计算结果与物理模型实验数据吻合良好,比较分析表明,所建立的数学模型能够复演由于离岸流引起的波高增大,可用于波流共存场多向随机波浪传播变形的模拟和预报。  相似文献