某动力机器基础动力学分析     

孔垂烛  薛尚铃  尹菊丽 《岩土工程技术》2007,21(5):254-256

当动力机器基础体积较大时,采用《动力机器基础设计规范》(GB 50040-96)推荐的简化计算方法进行动力计算,计算结果可能具有较大的误差而不能用于工程设计,此时进行三维数值分析是必要的。在介绍弹性动力学分析的基础上,使用通用有限元软件ANSYS8.0,对某设备基础进行了模态分析和谐相应分析,得出了该动力机器基础的低阶固有频率准确值,以及该动力机器基础随外部荷载频率改变的振幅变化曲线,进而得出一些重要结论,为该工程设计提供了可靠参考。  相似文献   

加权支持向量回归机及其在水质预测中的应用   总被引：1，自引：0，他引：1  

徐红敏  王继广 《世界地质》2007,26(1):58-61

支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。本文对用于回归估计的标准支持向量机加以改进，提出了一种新的用于回归估计的支持向量机学习算法，针对各样本重要性的差异，给各个样本的惩罚系数和误差要求赋予不同权重，并利用加权支持向量回归机的理论及其算法构建水质预测模型。实验结果表明，该方法对水质具有较好的预测效果。  相似文献   

机器基础动力相互作用的研究     

姜忻良  李燕飞  郑刚 《地震工程与工程振动》1998,18(1):95-102

本文分别对单台、双台、联合基础与地基土相互作用体系在扰力作用下的振动特性进行了研究。在计算中考虑了外围土体的影响。通过算例,得到了单台、双台、联合基础的一些振动规律。  相似文献   

Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils     

Pijush Samui  T. G. Sitharam 《国际地质力学数值与分析法杂志》2008,32(17):2033-2043

This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors     

Zhengzheng Zhou  Jie Ren  Xiaogang He  Shuguang Liu 《水文研究》2021,35(11):e14424

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.  相似文献   

数据驱动的蒸散发遥感反演方法及产品研究进展     

刘萌  唐荣林  李召良  高懋芳  姚云军 《遥感学报》2021,25(8):1517-1537

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。  相似文献   

盾构机全站仪激光导向模式误差灵敏度分析     

田正华  姜留涛 《测绘与空间地理信息》2021,44(4):50-53

通过对盾构机全站仪激光导向系统的测量原理研究,并对盾构坐标与施工坐标系的旋转模型转换原理分析,建立盾构姿态参数与坐标旋转模型的关系。然后通过全站仪测量激光标靶棱镜和切口坐标与激光标靶相对位置的误差分析得到相关结论。  相似文献   

三种非线性回归逐时气温预报比较订正   总被引：1，自引：0，他引：1  

梁立为  尹洁  马振富  周开鹏  彭振华 《气象科技》2015,43(6):1116-1120

选取ECMWF和T639的2013年1月至2014年12月的数值预报场构造预报因子,基于神经网络、支持向量机和构造函数的非线性方法,预报地面逐时气温。试验结果显示,在单个方法预报误差较大时,3种方法的偏差订正集成方法更利于减小误差,通过偏差订正,3种非线性方法预报效果良好,平均绝对误差减小了0.5 ℃。在近1年独立样本的预报检验中,集成方法、神经网络、支持向量机和构造函数预报的平均绝对误差分别为1.5 ℃、1.7 ℃、1．8 ℃和1．4 ℃,总体上构造函数预报更为准确。  相似文献   

Data selection using support vector regression     

Michael B.RICHMAN  Lance M.LESLIE  Theodore B.TRAFALIS  Hicham MANSOURI 《大气科学进展》2015,32(3):277-286

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.  相似文献   

基于模糊评判的数控机床零部件制造工艺FMECA研究     

谭壮  李国发  许彬彬  王松  朱光 《南京气象学院学报》2015,7(2):142-148

以数控机床零部件的制造工艺为研究对象,对其实施改进的制造工艺FMECA,通过梯形模糊数评判的方法确定出工艺FMECA中风险优先数的排序,利用模糊综合评判方法确定出工艺系统的风险等级,有效地找出薄弱工艺环节并采取改进措施,从而提高数控机床零部件制造工艺可靠性.  相似文献