青蟹属的系统进化和中国沿海拟穴青蟹的群体遗传结构研究

为了进一步研究青蟹属系统进化的科学问题，并揭示我国东南沿海青蟹群体遗传结构和群体进化细节信息，本论文主要开展了以下两个方面的研究：（1）基于线粒体12S rRNA、16S rRNA和COI三种基因序列探讨中国东南沿海青蟹的种类归属与青蟹属的系统进化；（2）利用线粒体COI基因标记分析中国东南沿海拟穴青蟹的群体遗传结构。序列特征、遗传距离和系统进化分析结果都表明本文研究的青蟹均为S. paramamosain。NJ、BAYES和ML系统进化树显示S. paramamosain与S. tranquebarica互为姐妹种，S. olivecea应该是4种青蟹中最早分化出来的种类。10个地理群体130只拟穴青蟹的线粒体DNA（mitochondrial DNA，mtDNA）细胞色素氧化酶亚基I（COI）基因序列Mantel检验结果显示群体间的遗传分化程度与地理距离没有显著的相关性。分子进化中性检验结果表明自然选择在分子进化过程中起了重要作用，并暗示该物种在最近经历了一个快速的群体爆发及扩张事件。

人脸检测的一种混合方法研究

人脸检测作为自动人脸识别系统的第一个环节具有非常重要的作用,为了解决目前大部分人脸检测方法存在的分类器训练困难和检测计算量大等问题,提出了一种人脸检测的混合方法。该方法由两级分类器组成,第一级为粗分类器主要过滤大部分非人脸区域,第二级为核心分类器,在由第一级粗分类的基础上利用非线性SVM算法进行人脸检测。在CMU数据库上的实验结果表明,该方法具有较高的人脸检测率,检测速度得到大幅提高。

基于模糊神经网络的人脸识别方法研究

人脸识别是模式识别研究领域的重要课题,具有广阔的应用前景。本文提出了基于模糊神 经网络的人脸识别方法。首先用最优鉴别分析方法提取人脸的最优鉴别矢量集,构成特征空间,然后在 特征空间中设计模糊神经网络分类器。在ORL人脸图象库上的实验结果表明了该方法的有效性。

基于修正的最小均方误差分类算法的移动机器人运动规划

研究了不确定性环境下移动机器人躲避运动轨迹未知的移动障碍物的一种新方法．通过实时最小均方误差估计算法预测每个障碍物的位置及运动轨迹，并利用模式识别中最小均方误差分类器的修正模型计算出机器人的局部避障路径，再运用船舶导航中使用的操纵盘技术来确定每个导航周期中移动机器人的速．度仿真结果表明了该方法的可行性

混合模式识别系统研究

讨论基于多种分类方法的模块组合实现的混合模式识别系统，它不同于利用多分类器输出结果表决的集成系统．提出两个系统：一个面向印刷体汉字文本识别，另一个面向自由手写体数字识别．利用多种特征和多种分类方法的组合、部分识别信息控制混淆字判别策略以及提出的动态模板库匹配后处理方法，使系统的性能与传统单一分类器系统比较，获得明显改善．实验表明：多方法多策略混合是解决复杂和增强系统鲁棒性的一条途径

神经网络用于多模式分类

本文叙述一种改进型HAMMING网在印刷汉字文本识别实用系统中作为粗分类的应用.给出了以3755印刷汉字为多模式分类对象的神经网络分类器的结构及其相应的算法.该方法在微型机上用软件仿真得以实现.取得令人满意的结果.

一个多元逐步回归的二叉树分类器的设计与实现

作者设计并实现了一个基于多变元逐步回归的二叉树分类器.在树结构和特征子集的选择中采用了穷举法,比有限制条件的选择更合理更优化.用 FORTRAN 语言实现的“遍历”二叉树,充分利用了 FORTRAN 处理可调数组的能力,并采取适当技巧,从而最大限度地利用了计算机内存.该通用分类器,可用来对任何具有统计数据的模式进行分类.在对白血球的分类中,取得了五类97%,六类92.2%的高识别率.

基于改进的模糊C均值的BP分类器设计

提出了一种基于改进模糊C均值的BP神经网络分类器的设计,通过改进的模糊C均值算法对大量的数据进行聚类划分,然后设计BP神经网络对划分后的数据进行训练和测试,最后由计算机进行综合判断.试验证明该分类器是有效的,可以对高速公路车辆的车型进行迅速判别.

基于窗口精细结构描述的岩体质量评价及力学参数综合优选

This thesis bases on horizontal research project “The research about the fine structure and mechanical parameters of abutment jointed rock mass of high arch dam on Jinping Ⅰ Hydropower Station, Yalong River” and “The research about the fine structure and mechanical parameters of the columnar basalt rock mass on Baihetan Hydropower Station, Jinsha River”. A rounded system about the fine structure description and rock mass classification is established. This research mainly contains six aspects as follow: (1) Methods about fine structure description of the window rock mass; (2) The window rock mass classification about the fine structure; (3) Model test study of intermittent joints; (4) Window rock mass strength theory; (5) Numerical experimentations about window rock mass; (6) The multi-source fusion of mechanical parameters based on Bayes principle. Variation of intact rock strength and joint conditions with the weathering and relaxation degree is studied through the description of window rock mass. And four principal parameters: intact rock point load strength, integration degree of window rock mass, joint conditions, and groundwater condition is selected to assess the window rock mass. Window rock mass is classified into three types using the results of window rock mass fine structure description combined with joints develop model. Scores about intact rock strength, integrality condition, divisional plane condition and groundwater conditions are given based on window rock mass fine structure description. Then quality evaluation about two different types of rock mass: general joint structure and columnar jointing structure are carried out to use this window rock mass classification system. Application results show that the window rock mass classification system is effective and applicable. Aimed at structural features of window structure of “the rock mass damaged by recessive fracture”, model tests and numerical models are designed about intermittent joints. By conducting model tests we get shear strength under different normal stress in integrated samples, through samples and intermittent joints samples. Also, the changing trends of shear strength in various connectivity rates are analyzed. We numerically simulate the entire process of direct shear tests by using PFC2D. In order to tally the stress-strain curve of numerical simulation with experimental tests about both integrated samples and through samples, we adjust mechanical factors between particles. Through adopting the same particle geometric parameter, the numerical sample of intermittent joints in different connective condition is re-built. At the same time, we endow the rock bridges and joints in testing samples with the fixed particle contacting parameters, and conduct a series of direct shear tests. Then the destructive process and mechanical parameters in both micro-prospective and macro-prospective are obtained. By synthesizing the results of numerical and sample tests and analyzing the evolutionary changes of stress and strain on intermittent joints plane, we conclude that the centralization of compressive stress on rock bridges increase the shear strength of it. We discuss the destructive mechanics of intermittent joints rock under direct shear condition, meanwhile, divide the whole shear process into five phases, which are elasticity phase, fracture initiation phase, peak value phase, after-peak phase and residual phase. In development of strength theory, the shear strength mechanisms of joint and rock bridge are analyzed respectively. In order to apply the deducted formulation conveniently in the real projects, a relationship between these formulations and Mohr-Coulomb hypothesis is built up. Some sets of numerical simulation methods, i.e. the distinct element method (UDEC) based on in-situ geology mapping are developed and introduced. The working methods about determining mechanical parameters of intact rock and joints in numerical model are studied. The operation process and analysis results are demonstrated detailed from the research on parameters of rock mass based on numerical test in the Jinping Ⅰ Hydropower Station and Baihetan Hydropower Station. By comparison，the advantages and disadvantages are discussed. Results about numerical simulation study show that we can get the shear strength mechanical parameters by changing the load conditions. The multi-source rock mass mechanical parameters can be fused by the Bayes theory, which are test value, empirical value and theoretical value. Then the value range and its confidence probability of different rock mass grade are induced and these data supports the reliability design.

地质规律约束下的波阻抗反演

Impedance inversion is very important in seismic technology. It is based on seismic profile. Good inversion result is derived from high quality seismic profile, which is formed using high resolution imaging resolution. High-resolution process demands that signal/noise ratio is high. It is very important for seismic inversion to improve signal/noise ratio. the main idea is that the physical parameter (wave impedance), which describes the stratigraphy directly, is achieved from seismic data expressing structural style indirectly. The solution of impedance inversion technology, which is based on convolution model, is arbitrary. It is a good way to apply the priori information as the restricted condition in inversion. An updated impedance inversion technology is presented which overcome the flaw of traditional model and highlight the influence of structure. Considering impedance inversion restricted by sedimentary model, layer filling style and congruence relation, the impedance model is built. So the impedance inversion restricted by geological rule could be realized. there are some innovations in this dissertation: 1. The best migration aperture is achieved from the included angle of time surface of diffracted wave and reflected wave. Restricted by structural model, the dip of time surface of reflected wave and diffracted wave is given. 2. The conventional method of FXY forcasting noise is updated, and the signal/noise ratio is improved. 3. Considering the characteristic of probability distribution of seismic data and geological events fully, an object function is constructed using the theory of Bayes estimation as the criterion. The mathematics is used here to describe the content of practice theory. 4. Considering the influence of structure, the seismic profile is interpreted to build the model of structure. A series of structure model is built. So as the impedance model. The high frequency of inversion is controlled by the geological rule. 5. Conjugate gradient method is selected to improve resolving process for it fit the demands of geophysics, and the efficiency of algorithm is enhanced. As the geological information is used fully, the result of impedance inversion is reasonable and complex reservoir could be forecasted further perfectly.

电阻率测井反演方法应用研究

Formation resistivity is one of the most important parameters to be evaluated in the evaluation of reservoir. In order to acquire the true value of virginal formation, various types of resistivity logging tools have been developed. However, with the increment of the proved reserves, the thickness of interest pay zone is becoming thinner and thinner, especially in the terrestrial deposit oilfield, so that electrical logging tools, limited by the contradictory requirements of resolution and investigation depth of this kinds of tools, can not provide the true value of the formation resistivity. Therefore, resitivity inversion techniques have been popular in the determination of true formation resistivity based on the improving logging data from new tools. In geophysical inverse problems, non-unique solution is inevitable due to the noisy data and deficient measurement information. I address this problem in my dissertation from three aspects, data acquisition, data processing/inversion and applications of the results/ uncertainty evaluation of the non-unique solution. Some other problems in the traditional inversion methods such as slowness speed of the convergence and the initial-correlation results. Firstly, I deal with the uncertainties in the data to be processed. The combination of micro-spherically focused log (MSFL) and dual laterolog(DLL) is the standard program to determine formation resistivity. During the inversion, the readings of MSFL are regarded as the resistivity of invasion zone of the formation after being corrected. However, the errors can be as large as 30 percent due to mud cake influence even if the rugose borehole effects on the readings of MSFL can be ignored. Furthermore, there still are argues about whether the two logs can be quantitatively used to determine formation resisitivities due to the different measurement principles. Thus, anew type of laterolog tool is designed theoretically. The new tool can provide three curves with different investigation depths and the nearly same resolution. The resolution is about 0.4meter. Secondly, because the popular iterative inversion method based on the least-square estimation can not solve problems more than two parameters simultaneously and the new laterolog logging tool is not applied to practice, my work is focused on two parameters inversion (radius of the invasion and the resistivty of virgin information ) of traditional dual laterolog logging data. An unequal weighted damp factors- revised method is developed to instead of the parameter-revised techniques used in the traditional inversion method. In this new method, the parameter is revised not only dependency on the damp its self but also dependency on the difference between the measurement data and the fitting data in different layers. At least 2 iterative numbers are reduced than the older method, the computation cost of inversion is reduced. The damp least-squares inversion method is the realization of Tikhonov's tradeoff theory on the smooth solution and stability of inversion process. This method is realized through linearity of non-linear inversion problem which must lead to the dependency of solution on the initial value of parameters. Thus, severe debates on efficiency of this kinds of methods are getting popular with the developments of non-linear processing methods. The artificial neural net method is proposed in this dissertation. The database of tool's response to formation parameters is built through the modeling of the laterolog tool and then is used to training the neural nets. A unit model is put forward to simplify the dada space and an additional physical limitation is applied to optimize the net after the cross-validation method is done. Results show that the neural net inversion method could replace the traditional inversion method in a single formation and can be used a method to determine the initial value of the traditional method. No matter what method is developed, the non-uniqueness and uncertainties of the solution could be inevitable. Thus, it is wise to evaluate the non-uniqueness and uncertainties of the solution in the application of inversion results. Bayes theorem provides a way to solve such problems. This method is illustrately discussed in a single formation and achieve plausible results. In the end, the traditional least squares inversion method is used to process raw logging data, the calculated oil saturation increased 20 percent than that not be proceed compared to core analysis.

Stable Mixing of Complete and Incomplete Information

An increasing number of parameter estimation tasks involve the use of at least two information sources, one complete but limited, the other abundant but incomplete. Standard algorithms such as EM (or em) used in this context are unfortunately not stable in the sense that they can lead to a dramatic loss of accuracy with the inclusion of incomplete observations. We provide a more controlled solution to this problem through differential equations that govern the evolution of locally optimal solutions (fixed points) as a function of the source weighting. This approach permits us to explicitly identify any critical (bifurcation) points leading to choices unsupported by the available complete data. The approach readily applies to any graphical model in O(n^3) time where n is the number of parameters. We use the naive Bayes model to illustrate these ideas and demonstrate the effectiveness of our approach in the context of text classification problems.

Recovering Intrinsic Images from a Single Image

We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, each image derivative is classified as being caused by shading or a change in the surface's reflectance. Generalized Belief Propagation is then used to propagate information from areas where the correct classification is clear to areas where it is ambiguous. We also show results on real images.

Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.