基于PSR-WSVM模型的边坡位移预测

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。     

Geometric Derivation of the Delaunay Variables and Geometric Phases

We derive the classical Delaunay variables by finding a suitable symmetry action of the three torus T³ on the phase space of the Kepler problem, computing its associated momentum map and using the geometry associated with this structure. A central feature in this derivation is the identification of the mean anomaly as the angle variable for a symplectic S ¹ action on the union of the non-degenerate elliptic Kepler orbits. This approach is geometrically more natural than traditional ones such as directly solving Hamilton–Jacobi equations, or employing the Lagrange bracket. As an application of the new derivation, we give a singularity free treatment of the averaged J ₂-dynamics (the effect of the bulge of the Earth) in the Cartesian coordinates by making use of the fact that the averaged J ₂-Hamiltonian is a collective Hamiltonian of the T³ momentum map. We also use this geometric structure to identify the drifts in satellite orbits due to the J ₂ effect as geometric phases.     

Hydrogeochemical investigations and groundwater provinces of the Friuli Venezia Giulia Plain aquifers,northeastern Italy

Water resources are a key factor, particularly for the planning of the sustainable regional development of agriculture, as well as for socio-economic development in general. A hydrochemical investigation was conducted in the Friuli Venezia Giulia aquifer systems to identify groundwater evolution, recharge and extent of pollution. Temperature, pH, electric conductivity, total dissolved solids, alkalinity, total hardness, SAR, Ca²⁺, Na⁺, K⁺, Mg²⁺, Cl⁻, SO₄ ²⁻, NO₃ ⁻, HCO₃ ⁻ _, water quality and type, saturation indexes and the environmental stable isotope δ¹⁸O were determined in 149 sampling stations. The pattern of geochemical and oxygen stable isotope variations suggests that the sub-surface groundwater (from phreatic and shallow confined aquifers) is being recharged by modern precipitations and local river infiltrations. Four hydrogeological provinces have been recognised and mapped in the Friuli Venezia Giulia Plain having similar geochemical signatures. These provinces have different degrees of vulnerability to contamination. The deep confined groundwater samples are significantly less impacted by surface activities; and it appears that these important water resources have very low recharge rates and would, therefore, be severely impacted by overabstraction.     

基于AGA的SVM需水预测模型研究

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.     

Prediction of local scour around circular piles under waves using a novel artificial intelligence approach

Abstract

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R?=?0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles.     

基于矢量信号处理的水声定位系统

将传统的水声定位系统与矢量水听器相结合，设计了一种全新的轻便型长基线被动水声定位系统。介绍了系统的组成和工作原理，并结合近年来出现的矢量信号处理技术，设计了新的实时信号处理软件。经湖试和海试，系统的可行性得到了初步的验证。     

Outer wave number spectrum and statistical distribution of apparent wave number vector

Based on the ray theory and Longuet-Higgins’s linear model of sea waves, the joint distribution of wave envelope and apparent wave number vector is established. From the joint distribution, we define a new concept, namely the outer wave number spectrum, to describe the outer characteristics of ocean waves. The analytical form of the outer wave number spectrum, the probability distributions of the apparent wave number vector and its components are then derived. The outer wave number spectrum is compared with the inner wave number spectrum for the average status of wind-wave development corresponding to a peakness factor P = 3. Discussions on the similarity and difference between the outer wave number spectrum and inner one are also presented in the paper.     

P矢量方法在南海夏季环流诊断计算中的应用

基于1998年6～7月南海调查航次的CTD资料,对南海环流采用最近发展的P矢量方法进行诊断计算.计算结果:黑潮向西入侵南海,然后做反气旋弯曲向东北方向流动,最终有通过巴士海峡流出南海的趋势.在南海北部存在一个气旋性环流,这个环流的强度和范围随深度增加而减小.该环流的冷中心位置随深度增加稍向南移.南海中部、越南以东海域存在一个明显的气旋涡和反气旋涡,尤其在200m及其以上水层均相当稳定,反气旋涡位于越南以东,其中心位置在11°53'N,111°50'E,气旋涡的中心位置在13°17'N,112°55'E,两者的尺度皆约为250km.吕宋岛西侧存在一个反气旋涡.在计算海区南部、巴拉望岛西南海域,100m以上层存在一个反气旋式涡.从各层流场分布均可以显示海流在西部强化的现象.     

基于光谱和纹理的SVM矿化蚀变信息提取研究

针对传统矿化信息提取方法单一,利用光谱或纹理、信息量相对较少、需要大量样本的缺陷,利用基于光谱和纹理的支持向量机(SVM)原理,建立矿化信息提取模型.选择青海泽库县析界日地区作为典型研究区.首先提取研究区光谱和纹理信息,选取训练样本;然后求解最优超平面,进而确定决策函数;最后泛化推广识别其他待识别的样本.通过所提取的遥感蚀变异常信息与重砂异常点叠加分析,叠加基本吻合;从野外实地验证来看,均发现了不同程度的矿化现象,并指出了5个重点异常区.     

区域似大地水准面确定的最小二乘支持向量机方法

支持向量机(SVM)是近年来发展起来的机器学习的新方法,它较好地解决小样本、非线性、高维数、局部极小点等实际问题.文中研究支持向量机的拓展算法--最小二乘支持向量机(LSSVM),并将其应用于确定大面积复杂似大地水准面.通过工程实例并与神经网络模型和二次曲面多项式拟合模型相比较,验证确定区域似大地水准面的LSSVM方法的有效性.