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.     

结构抗震可靠度二种简化解析表达式的一致性证明

结构抗震可靠度分析的解析求解一直是结构可靠度领域和地震工程领域研究者们的追求目标.解析表达式不仅有利于简化结构抗震可靠度分析这个极端困难的工作,而且也有利于进行基于可靠度的结构概率抗震性能设计与评定.从全概率定理的两种不同表达式出发,介绍了国际上流行的两种结构抗震可靠度解析表达式,过去一直认为这是两种理论基础不同的表达式.本文通过对地震易损性函数关系的解析推导,揭示了若干重要关系,证明了两种表达式的一致性.     

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.     

复杂二维/三维大地电磁的有限单元法正演模拟策略

复杂二维和三维大地电磁模型的正演数值模拟具有一定的挑战性。对于复杂的二维和三维大地电磁正演问题,我们采用有限单元法进行求解。有限单元法最后形成一个线性方程组,系数矩阵是大型稀疏的带状对称复系数矩阵,并且其条件数远大于1,为严重病态矩阵,求解其对应方程组会遇到很多困难。不完全LU分解处理的Bi-CGSTAB迭代方法可用于该线性方程组的求解,并且具有速度快、精度高和稳定性好等优点;为了模拟无穷远边界及满足计算机的内存需求,在保证计算精度的情况下设计了非均匀网格剖分;在程序编制中,只存储有限元系数矩阵的非零元素,大大减少了正演计算的时间。通过对二维和三维模型电磁响应的计算,验证了算法的正确性。     

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。