基于Mean-shift影像分割算法的TM影像分类及城市扩展研究

以马来西亚雪兰莪州及首都吉隆坡为例,将Mean-shift算法的影像分割方法应用于研究区的TM影像分类中,较传统方法的分类精度更高,可满足应用需求.依据该分类结果对研究区的空间城市化特征进行分析.发现近期研究区一直处于高速扩展阶段,外延式扩展和填充式扩展并行发展成为该研究区城市扩展的主导模式.     

Simulation of torsional shear test results with neuro-fuzzy control system

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1 Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear stress–strain changes, the number of cycles to reach liquefaction and pore pressure variations were recorded. Results indicate that the liquefaction resistances of uniform sands are significantly affected by the method of sample preparation and initial conditions.     

A positive definite continuous FEM model for advection

This work introduces a continuous and sign-preserving finite element model (FEM) for advection problems. The model is formulated by integrating flux corrected transport technique (FCT) with the characteristic based (CB) FEM. Low order solution for the FCT has the form of element contribution and derives from an upwind monotone scheme with the minimum necessary diffusion for positivity. Selected numerical experiments illustrate the efficacy of the method by comparing results with some successful models, by demonstrating an approximate conservation of the accuracy of original CB algorithm once the complete procedure is operating, and by testing the model for severe time dependent advective flows.     

Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.     

稀疏信号恢复理论在CT图像重建中的应用

新的图像重建算法一直是CT成像前沿研究中的热点问题之一,而实际中常常遇到的有限角度重建问题则是其中的难点.Terence Tao和他的同事提出了一种稀疏信号恢复理论,为解决这个问题提出了可能的应用策略.我们在本文中介绍了这个理论并给出了在锥束CT重建中应用的初步结果.这种理论和策略的实现对很多应用情况都有重要的意义,为该领域的发展开辟了新的方向,也必将对该领域的发展产生重大影响.     

基于相关分析的飞行器地磁匹配算法仿真研究

匹配算法是进行飞行器地磁辅助制导研究的重要内容之一.通过理论分析和借鉴地形匹配算法,采用了基于相关分析的地磁匹配算法,并介绍了几种常用的相关分析方法.分析了飞行器地磁匹配制导过程中可能的噪声来源,确定了主要噪声的模型.以测量数据作为基准图,以测量数据叠加各种噪声模拟实时图,对各种相关分析方法进行了匹配仿真.根据仿真结果,讨论了各种相关分析方法对不同噪声的敏感程度、匹配长度变化对各种相关分析方法的影响以及各种相关分析方法对飞行器地磁匹配制导的适用性,为相关分析方法的选择和对匹配算法进行更深入的研究奠定了一定基础.     

基于波场外推压制水层多次波

重点讨论了基于克希霍夫积分的波场外推方法预测水层多次波的方法原理及具体的数值实现.自适应减法是波动方程方法压制多次波的另一个重要方面,考虑到Monk提出的带约束的互均衡化算法在复杂模型情况下压制效果不是很理想,本文对其算法进行了改进,提出了基于矩形窗和海宁(hanning)窗的分段匹配算法,模型算例的处理结果表明,改进后的算法具有一定的优越性.     

地球物理资料群体智能反演(英文)

复杂地球物理资料的反演问题往往是一个求解多参数非线性多极值的最优解问题。而鸟和蚂蚁等群体觅食的过程,正好与寻找地球物理反演最优解的过程相似。基于自然界群体协调寻优的思想,本文提出了交叉学科的群体智能地球物理资料反演方法,并给出了其对应的数学模型。用一个有无限多个局部最优解的已知模型对该类方法进行了试验。然后,将它们应用到了不同的复杂地球物理反演问题中:(1)对噪声敏感的线性问题;(2)非线性和线性同步反演问题;(3)非线性问题。反演结果表明,群体智能反演是可行的。与常规遗传算法和模拟退火法相比,该类方法有收敛速度相对快、收敛精度相对高等优点;与拟牛顿法和列文伯格一马夸特法相比,该类方法有能跳出局部最优解等优点。     

L1-L2范数联合约束稀疏脉冲反演的应用

稀疏脉冲反演实际上就是利用反褶积原理, 从带有噪声的地震道中计算出具有稀疏分布特征的反射系数的振幅和时间.稀疏脉冲反演是非线性优化问题, 通常都是把非线性优化问题转化为线性优化问题, 然后用线性优化算法求解.以范数约束为基础, 提出L1-L2范数联合约束求解的方法.该算法采用了目前国际流行的内点算法, 与传统的优化算法相比, 这种算法具有精度高, 速度快的优点.通过研究人工模型和南海某油田实际数据, 表明该算法的计算结果和测井记录吻合好, 提高了地震分辨率, 目的层段分辨率优于8m.利用反射系数剖面预测的储层厚度和开发井吻合很好, 大大地降低了资源量计算的风险和油田开发的不确定性.      

基于RAGA的指数曲线模型预测基桩承载力

预测挤扩支盘桩单桩极限承载力指数曲线模型的函数表达式是一个超定非线性方程。采用传统的最小二乘法对该模型参数进行回归处理时,往往由于计算复杂和人为因素的影响使预测结果存在较大的误差。为此,用实数编码加速遗传算法（RAGA）,对指数曲线模型的参数和理论极限承载力进行优化,并基于MATLAB环境下编写了模型的计算程序。结合工程实例,应用指数曲线模型对挤扩支盘桩静载荷试验实测数据进行了拟合分析。结果表明,基于RAGA的指数曲线优化模型能够更好地拟合实测数据和有效地预测单桩极限承载力,且基于RAGA的指数曲线优化模型具有求解速度快、计算精度和自动化程度高、人为干扰因素小、通用性强等优点。