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。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

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.     

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

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.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

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.     

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.     

预报指标自动归纳和实时自动推理预报系统

本文结合实际应用介绍一种人工智能系统。系统的重要特点是,根据预报指标经验知识的产生过程的机理性知识所设计成功的系统主程序之一的“预报指标自动归纳程序”,能够通过对多因子历史资料的分析归纳,自动生成含有较全面知识规则的规则库。有关专家只须凭感性经验提供可能因子和原始资料,系统便能帮助升华出理性的经验规则,自动建成某一专项业务的应用预报系统。对这种“母系统”的反复使用,就可以根据不同类别的资料生成相应的多种应用系统。既适用于气象上风雨暑寒各类天气预报,也适用于其他某些专业,如水文、地震等的定性预报或分析。 本文除简单介绍系统的构造、功能、设计原理之外,还给出一个应用实例,介绍了如何使用本系统去产生应用性系统的具体方法步骤。     

增氧机自动控制电路

介绍作者自行设计、研制的控制增氧机运行时间的自动控制电路及其原理．该电路采用ＣＭＯＳ集成芯片，结构简单、工作可靠，价格低，业经试验证实：各项性能指标均符合要求．     

水产饲料膨化机设计刍论

据开发应用水产饲料膨化机实践，初步论述了膨化机生产能力与动力匹配，螺杆设计参数长径比，物料在腔内滞留时间，膨化腔设计及其温度调控，并提出改进设计的见解.     

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.