Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.     

Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification

System identification provides an effective way to predict the ship manoeuvrability. In this paper several measures are proposed to diminish the parameter drift in the parametric identification of ship manoeuvring models. The drift of linear hydrodynamic coefficients can be accounted for from the point of view of dynamic cancellation, while the drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the parameter drift, reconstruction of the samples and modification of the mathematical model of ship manoeuvring motion are carried out. Difference method and the method of additional excitation are proposed to reconstruct the samples. Using correlation analysis, the structure of a manoeuvring model is simplified. Combined with the measures proposed, support vector machines based identification is employed to determine the hydrodynamic coefficients in a modified Abkowitz model. Experimental data from the free-running model tests of a KVLCC2 ship are analyzed and the hydrodynamic coefficients are identified. Based on the regressive model, simulation of manoeuvres is conducted. Comparison between the simulation results and the experimental results demonstrates the validity of the proposed measures.     

EMD在广西季节降水预报中的应用

气候系统是一种耗散的、具有多个不稳定源的非线性、非平稳系统。该文利用支持向量机(SVM)算法在处理非线性问题中的优越性和经验模态分解(EMD)算法在处理非平稳信号中的优势,采用将EMD与SVM相结合的短期气候预测方法,并应用到广西季节降水预报中。选取广西88个气象观测站1957—2005年6—8月逐年降水量的距平百分率序列作为试验数据,通过EMD算法将标准化处理后的距平百分率序列分解成多个本征模态函数(IMF)分量和一个趋势分量,在分解中针对EMD算法存在的端点极值问题选择两种方法分别进行处理,对比得出极值延拓法效果更好。对每个分量构建不同的SVM模型进行预测,并通过重构形成最后的预测结果。试验中采用不经EMD处理的反向传播(BP)神经网络和SVM算法进行对比验证,结果表明:相对于直接预测方法,该文提出的方案均方误差最小,能够较为准确地反映出降水序列未来几年的变化趋势,具有更高的预测精度和较好的推广前景。     

地下水观测数据拟合与预测的支持向量机方法

影响地下水位变化因素有很多,在正常情况下,地下水位的变化实际上反应了气压、固体潮和降雨这些因素的变化,但是这些影响因子与地下水位之间有着较强的非线性关系。该文使用支持向量机方法建立起崇明中学观测站地下水位与气压、固体潮和降雨这些因素之间的非线性关系模型,并用于地下水观测数据拟合与预测,得到了较理想的结果,明显优于逐步回归方法。研究结果表明,支持向量机方法在地震前兆数据处理中有着广泛的应用前景。文中还对支持向量机方法在实际应用中的有关问题进行了讨论。     

中国大陆强震时间序列预测的支持向量机方法

统计学习理论（Statistical Learning Theory或SLT）是研究有限样本情况下机器学习规律的理论。支持向量机（Support Vector Machines或SVM）是基于统计学习理论框架下的一种新的通用机器学习方法。它不但较好地解决了以往困扰很多学习方法的小样本、过学习、高维数、局部最小等实际难题，而且具有很强的泛化（预测）能力。文中使用支持向量机对中国大陆最大地震时间序列进行预测，预测次年的我国大陆最大地震震级，结果表明该方法具有较好的预报效果。研究结果还表明我国大陆强震活动除了与强震时间序列本身有关外，还与全球的强震活动、太阳黑子活动等有密切的关系。尽管这种关系还不清楚，但是通过支持向量机可以很好地反应出这种非线性关系。     

支持向量机在遥感数据分类中的应用新进展

支持向量机是一种基于统计学习理论的新型机器学习算法,它通过解算最优化问题,在高维特征空间中寻找最优分类超平面,从而解决复杂数据的分类及回归问题.随着应用面的不断扩大,支持向量机在遥感领域也得到了广泛关注.该算法已经成功的应用于遥感数据的土地覆盖、土地利用分类,多时相遥感数据的变化检测,多源遥感数据信息融合等,并且在高光谱遥感数据处理中得到了广泛应用.综述了支持向量机算法在遥感数据分类中的应用.首先对支持向量机的理论进行简要介绍,进而综述了该算法在不同遥感问题中的应用进展,最后阐述了新型支持向量机算法的发展以及在遥感中的应用.     

结合PCA、多尺度分割及SVM的ASTER遥感蚀变信息提取

为了利用遥感影像进行更加精确的找矿预测,本文选择新疆东天山尾亚地区ASTER数据进行矿化蚀变信息提取方法研究.为了提高信息提取精度,本文提出了结合主成分分析(PCA)、多尺度分割和支持向量机(SVM)的遥感矿化蚀变信息提取方法.首先,分析ASTER数据的特征,选取各矿化蚀变信息的特征波段,对组合波段进行主成分分析,获得...