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.     

论虚拟企业

网络的出现和广泛运用，以及企业间的跨越了行业间的藩篱，推动了业际化的进程；业际化与网络相结合促使企业组织形式发生质变，出现了虚拟企业。与大工业时代的企业相比较，虚拟企业具有了全新的特征、类型、经济形式和生产模式，其经济效益提升到一个新的平台。     

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GPS实现测频等功能的虚拟仪器的研制

设计和研制了一种适用于大专院校示范教学实验的虚拟仪器。该虚拟仪器以GPS接收模块输出的1PPS(秒脉冲)信号为基准,测量无源石英晶体振荡器频率,同时利用GPS信号实现了仪器的时间同步与测距功能。从硬件和软件两方面描述了该虚拟仪器的构成及设计方法,主要介绍和讨论基于GPS和虚拟仪器技术实现多功能测量的途径。     

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.     

Numerical Experiment on Two-Dimensional Line Thermal

The time evolution of a two-dimensional line thermal-a turbulent flow produced by an initial element with signifi-cant buoyancy released in a large water body, is numerically studied with the two-equation k - e model for turbulence closure. The numerical results show that the thermal is characterized by a vortex pair flow and a kidney shaped concentra-tion structure with double peak maxima; the computed flow details and scalar mixing characteristics can be described by self-similar relations beyond a dimensionless time around 10. There are two regions in the flow field of a line thermal: a mixing region where the concentration of tracer fluid is high and the flow is turbulent and rotational with a pair of vortex eyes, and an ambient region where the concentration is zero and the flow is potential and well-described by a model of doublet with strength very close to those given by early experimental and analytical studies. The added virtual mass coeffi-cient of the thermal motion is found to be approximat     

A Forced System of Two Cylinders with Various Spacings

The spectrum characteristics and wake structures for a circular cylinder oscillating in a wake are investigated by use of the currently modified virtual boundary method. A forced system of two cylinders with a small spacing ( the downstream one is made to oscillate in the transverse direction) is studied and interesting flow characteristics are observed. A vortex switch and the change of vortex modes (between 2S mode and 2P mode) are observed in the “lock-in“ region. Vortex bands are formed and lost with the increasing excitation frequency. Information concerning saddle points in the flow field is obtained for different excitation frequencies. For a forced system of two cylinders with a large spacing, the upstream cylinder sheds vortexes because there is no downstream cylinder oscillating in the wake. No distinct “lock-in“ response is found for the downstream cylinder.     

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．