基于高斯烟羽模型的船舶尾气扩散研究

为有效对港区大气污染进行治理、分析船舶尾气,本文详细介绍了一种基于高斯烟羽模型,通过MATLAB模拟仿真模型,其包括实验仿真过程、技术原理及理论模型对船舶尾气扩散进行的研究。该模型是在传统的高斯烟羽模型的基础上,通过对实源像源进行加权选择输入参数;通过矢量合成确定了气体扩散的方向,利用合成后的"风速"进行计算仿真,有效模拟了船舶尾气在港区或者海洋环境中的气体扩散模型。其模型简单且可以有效模拟船舶尾气扩散。并且进一步对后续模型的精确优化进行分析。     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

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.     

基于噪声随机模型的加权观测融合方法

信息融合技术中,在各局部传感器的有色观测噪声为一阶AR模型的情况下,可以利用观测扩增方法消除有色噪声的影响,得到最优加权观测融合方程,从而实现状态的最优滤波解。对于有色观测噪声为MA或ARMA模型的情况,观测扩增方法不再适用。提出了基于有色观测噪声随机模型级数展开的方法,求解出各局部传感器有色观测噪声的方差,并利用该方差对加权观测融合滤波器进行了构造。通过计算实例证明,该方法不仅适用于观测噪声为AR模型,同时适用于噪声MA或ARMA模型。     

交通流量分析与实时最优控制

通过将车流量的增大或减小转化为路长权重的变化。将交通流量的动态问题转化为静态问题，用解决最短路问题的Dijkstra方法，给出交通流量实时最优控制的可行性模型及其有效算法。     

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.     

P-III分布参数的概率权重矩法S函数计算

概率权重矩法是一种估计统计分布参数的方法.本文根据不完全Γ函数在无限区间积分,推导了P-Ⅲ分布参数的S函数的计算公式.通过现有计算公式比较,其计算结果具有较高的计算精度,避免了的大量的数值积分计算.文中公式只要借助于计算编程进行求解,给定超几何函数项一定的截断误差,其运算具有较高的运行速度.文中计算公式是一种P-Ⅲ分布参数S函数的计算途径.     

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.     

多波束数据的海底数字地形模型构建

提出大批量多格式原始多波束数据的DTM构建方法，以满足大区域、大比例尺海底地形制图的需要。在多种格式原始多波束数据接口和系统内部标准数据结构的基础上，通过对数据文件、数据种类和数据运算量的有效组织管理，实施边读入、边权重配赋的网格插值，分析了高斯、指数和距离平方反比权重函数的适用性，及最小值、最大值和平均值的实用性。在权重配赋网格插值基础上，提出分形fBM和张力样条配合使用的方程式插值方法，保证DTM数据的有效外延和地形分辨。整套算法效率高，并能有效保证DTM的精度，对存储在外部介质的数据遍历一次即可完成网格插值。