基于PSR-WSVM模型的边坡位移预测     

李建新  刘小生  肖钢  周文  刘仁志 《大地测量与地球动力学》2020,40(6):577-580

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。  相似文献   

深水铺管船托管架疲劳分析及动态监测点选取研究     

闫宏生  王哲  王晓波  陈永訢  孟祥伟 《海洋工程》2018,36(1):107-113

作为S型铺管作业的关键性装备,托管架长期承受着交变载荷的作用,随着海洋开发向超深水发展,结构疲劳破坏问题不容忽视。采用疲劳谱分析的方法并结合线性累积损伤理论,对托管架结构频域下的变形进行了分析,计算了正常海况下和极端海况下托管架疲劳损伤度并对疲劳寿命进行了评估。研究发现托管架在正常海况下作业符合安全要求,在极端海况下局部结构会受到破坏。并从托管架结构安全监测角度,筛选了结构疲劳分析关键点位,为监测点位的选取提供了依据。  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   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

JD直连式深孔绳索取心钻杆的研制与应用          下载免费PDF全文

许华松  覃强华  熊海书 《探矿工程》2008,35(10):33-35

根据深部找矿的需要,立足于我国目前钻探设备现状和材料冶炼及机械加工水平,通过对钻杆选材、结构、制造工艺的创新,研制了一种两端镦粗后整体热处理再车螺纹的新型深孔绳索取心钻杆及配套钻具,经使用,效果达到了设计要求。  相似文献   

手掘式顶管在市政排水工程中的应用          下载免费PDF全文

刘会 《探矿工程》2008,35(10):52-54

从混凝土管材的选定、工作坑的设计及施工、顶管顶进中若干关键问题入手,介绍了手掘式顶管施工技术在成都市中心城水环境综合整治工程G标段管道排水工程中的应用。  相似文献   

PVC-U塑料管在浅层地热能地下水资源开发工程中应用与研究     

卢予北 《探矿工程》2008,35(11):1-6

PVC-U塑料管具有耐腐蚀、不结垢、质量轻、使用寿命长等特点。将其应用于浅层地热能开发和供水管井工程中,不但可以解决金属井管腐蚀破裂、结垢和水源热泵回灌难等问题,而且还减少了水井维修费用、延长其使用寿命。结合河南郑州实施的437、400 m两眼浅层地热能开发和供水管井示范工程实践,详细论述了PVC-U塑料管成井工艺、存在问题及解决方法的研究成果。  相似文献   

基于AGA的SVM需水预测模型研究   总被引：1，自引：0，他引：1  

张灵  陈晓宏  刘丙军  王兆礼 《水文》2008,28(1):38-42,46

需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统.其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策.作为一种集中参数预报方法,支持向量机方法具有对未来样本的较好的泛化性能,对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果.基于此,本文将支持向量机方法引入需水预测领域,建立了需水预测支持向量机模型.同时,本文将加速遗传算法和支持向量机方法耦合起来,构造了支持向量机模型参数的自适应优化算法.模型在珠海市的应用实例表明:与简单遗传算法比较,AGA的模型参数寻优效率更高;与BP神经网络模型相比,SVM模型较好地解决了小样本、经验性等问题,并取得了较高的预测精度.  相似文献   

Design and Strength Analysis of A Spherical Connector for Lifting Subsystem in Deep Sea Mining System     

冯雅丽张文明  冯福璋 《中国海洋工程》2006,20(4):605-613

1 .IntroductionThe lifting systemis composed of pipes andtheir connectors .It is not onlythe lifting channel formanganese nodule betweenthe miningship andintermediate bin,but alsothe supporting massfor deepsea miningequipment and cables .Sothe reliability…  相似文献