基于SVM的多源遥感影像分类研究

本文通过分析单源遥感影像分类的现状和困难,以SAR和SPOT-5影像为实验数据,提出了基于支持向量机(Support Vector Machine,SVM)理论的多源遥感影像分类方法。研究结果表明,本文的方法能够有效地解决单源影像信息分类效果破碎的问题,正确识别地物,对高维输入向量具有高的推广能力,正确率达到94.97%,比多源影像的最大似然分类(Maximum Likelihood Classification,MLC)方法正确率更高。     

统计重建算法综述

本文主要综述应用于PET/SPECT领域的统计重建算法,介绍统计重建算法发展的历史,最新的研究成果及研究方向,总结统计重建算法发展的三个概念:有序子集加速、增量方法和优化变换.对现有算法进行归纳总结,关注统计重建的发展带来变革的概念和方法,重点论述算法的思想,勾勒统计重建方法发展的主线.     

用倾角变异系数预测芙蓉井田构造发育程度

根据计算所得煤层底板倾角变异系数R值,将未采区划分为4种构造复杂程度类别,从而找出未采区不同地段断裂构造的差异性,还可根据R值等值线的方向判定未采区主要断裂构造的走向。      

同级位移多次循环作用下型钢混凝土柱抗震性能试验研究

为研究远场长周期地震动作用下SRC柱的抗震性能,对5个不同含钢率和配箍率的SRC柱进行同级位移循环加载10次的拟静力试验,分析其抗震性能指标。结果表明:同级位移循环次数对SRC柱抗震性能的影响与循环位移幅值有关。位移角不大于1/50时,同级位移循环次数对SRC柱的裂缝发展、承载力退化和耗能能力的影响均很小;位移角1/40时,随着位移循环次数的增加,SRC柱的裂缝不断发展,角部混凝土逐渐掉落,承载力退化幅度开始加大,耗能能力逐渐增强,损伤程度增长较快;位移角1/33时,同级位移多次循环导致SRC柱的损伤急剧发展,承载力快速降低,耗能能力明显增强,破坏程度显著加重。提高含钢率和配箍率均可以改善SRC柱的抗震性能。     

THE MAXIMUM LIKELIHOOD ESTIMATION OF b-VALUE IN MAGNITUDE-FREQUENCY RELATION AND ANALYSIS OF ITS INFLUENCING FACTORS

b-value in the magnitude-frequency(G-R)relationship plays a vital role in seismicity research and seismic hazard analysis, and the most commonly used techniques to simulate it are least square approach and maximum likelihood method. Least square method is simple and easy to apply, therefore widely used in China. However, many researches show that there exist some limits in least square estimation of b-value. Earthquakes with different magnitudes are not equally weighted in this method, and larger events have higher weights, so b-value is vulnerable to the fluctuation of several big earthquakes; meanwhile, least square method needs to divide magnitude intervals artificially. With a small sample size, data points could be not enough if the magnitude interval is too wide, and events in a magnitude interval may be lacking if it is divided to be too narrow. Especially for incremental G-R relationship, it is possible that N(M_i)equals 0 in an interval with large magnitude, so log(N(M_i))loses meaning and has to be ignored, resulting in a low b-value. Therefore, under certain conditions, maximum likelihood method is recommended as an effective substitution or supplementary for least square estimation of b-value. Among numerous previous researches on maximum likelihood estimation of b-value, lots of equations have been provided, based on varied implicit assumptions and different ways of solution. A brief overview is first presented for these equations, and classification and summary are provided based on whether taking account of the effect of binned magnitude, with finite maximum magnitude, using unequal observation periods for different magnitude intervals, and with analytic solution or not. Following this, a total of 6 influential factors are analyzed, such as binning magnitude, measurement errors of magnitude, sample size, magnitude span, minimum completeness magnitude and fore- and aftershocks. At last, reasonable suggestions are provided for using those equations properly. The equations of Aki(1965), Utsu(1965), Page(1968)and Kijko and Smit(2012)are based on assumption that magnitudes are continuous random variables, and have no corrections for this, so these equations are not recommended here. For simplicity, the equations of Utsu(1966)or Tinti and Mulargia(1987)can be used, but magnitude span should be greater than 2.5 due to without finite maximum magnitude in the formulas. For researchers having capability to write code and calculate numerically, Weichert(1980)or Bender(1983)'s algorithm could be utilized. Especially when it is required to apply data with different observation periods for varied magnitudes, the formula of Weichert(1980)is recommended. This study contributes to more accurately understand and use different formulas of estimating b-value by maximum likelihood technique, which can be used as reference for peers.     

大型浅水湖泊藻类模型参数敏感性分析

选取太湖作为典型湖泊在之前研究基础上建立藻类模型,对模型中与藻类有关的40个参数进行拉丁超立方抽样,并使用区域敏感性分析方法和普适似然不确定性分析方法进行敏感性分析.结果表明:在所选的40个参数中,有7个参数是敏感的参数,对模拟的结果影响较大.在藻类生长、基础代谢、牧食和沉降4个藻类变化过程中藻类生长的敏感参数最多,影响最大;在藻类生长项中,叶绿素的消光系数是藻类生长光照限制中的最敏感参数,而最低适宜生长温度及其对藻类生长的影响系数则是温度限制中的敏感参数;并且不同湖区的不确定性在不同时间差异明显,对于藻类低浓度湖区和藻类暴发期的模拟需要加以关注.     

Probabilistic development of shear strength model for reinforced concrete squat walls

In order to reconcile the larger scatter and avoid the biased estimate from deterministic predictions for the shear strength of reinforced concrete (RC) squat structural walls, a probabilistic shear strength model is developed in this paper based on the strut‐and‐tie model and the generalized likelihood uncertainty estimation (GLUE) method. The strut‐and‐tie model is used to derive an appropriate function form for the probabilistic shear strength model, where four unknown model parameters (e.g. k₁, k₂, k₃ and k₄) are defined carefully to guarantee them having a clear physical‐based meaning so that the corresponding prior distribution ranges can be specified reasonably. Then, the GLUE method is adopted to estimate the posterior cumulative distribution of k₁, k₂, k₃ and k₄ with an available experimental database. Furthermore, to demonstrate the stability of the estimated posterior cumulative distribution, the sensitivity of three major aspects in GLUE method is investigated. Finally, based on the estimated cumulative distribution of k₁, k₂, k₃ and k₄, the developed probabilistic shear strength model is simplified as a mean prediction model and a standard deviation prediction model for facilitate using in engineering practice. Therefore, with the developed probabilistic shear strength model, not only can the squat structural walls be designed in confidence, but the accuracy of those deterministic predictions can be evaluated in a probabilistic manner. Copyright © 2016 John Wiley & Sons, Ltd.     

A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units

This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closed-form analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers’ physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.

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Detection of changes in variance of oceanographic time-series using changepoint analysis

Changepoint analysis is used to detect changes in variability within GOMOS hindcast time-series for significant wave heights of storm peak events across the Gulf of Mexico for the period 1900-2005. To detect a change in variance, the two-step procedure consists of (1) validating model assumptions per geographic location, followed by (2) application of a penalized likelihood changepoint algorithm. Results suggest that the most important changes in time-series variance occur in 1916 and 1933 at small clusters of boundary locations at which, in general, the variance reduces. No post-war changepoints are detected. The changepoint procedure can be readily applied to other environmental time-series.