一种用于软件过程建模的适应性Agent协商

大多软件过程模型是预定义的.在变化的应用环境中,需要由相应人员进行适应性调整.提出一种用于软件过程建模的适应性多边协商模型——AMNM-PA,其采用Agent封装软件过程中所涉及的个体,包含组织、团队、个人等,通过Agent间的协商动态、适应地建立针对给定软件项目的软件过程模型.AMNM-PA基于非静态有限阶段Markov决策过程,采用模型无关的Q学习算法选取协商策略,因此能够支持动态、非预知环境下的适应性协商,从而满足软件过程建模对环境的适应性需求.AMNM-PA已经实施于软件过程管理系统——SoftPM.

聚丙烯酰胺水凝胶的制备与应用研究

本文分三部分论述了聚丙烯酰胺水凝胶的制备、结构、性质和在大分子稀水溶液浓缩方面的应用。I、聚丙烯酰胺水凝胶的制备与研究用以下两种不同方法制备了丙烯酰胺（AM）-丙烯酚钠（AANa）共聚物水凝胶：i、AM-AANa直接辐射共聚：ii、AM水凝胶辐射均聚，再经碱水解。考察以上两个体系的凝胶化剂量，发现前者比后者的凝胶化剂量大一个数量级。理论推导得到水凝胶溶胀比Q_m，与吸收剂量R和共聚物中AANa含量Z之间存在以下关系式：Q_m~(-2/3) ∝ R/Z并用实验证实了所得到的理论关系式。在研究水凝胶溶胀比的影响因素时发现，水凝胶的溶胀比并不随其中的离子基团（-COO~-）含量的增加而无限增加。AM均聚物水凝胶和AANa均聚物水凝胶的溶胀比都不是很大（均不超过400），只有当两者以一定比例混合共聚时，才有最大的溶胀比（大于2000），提出了“有效阴离子”的概念以解释这些现象。在研究pH值对AM-AANa共聚物水凝胶溶胀比的影响时发现，在溶液pH为2.5-3.5之间，水凝胶发生了可逆的相转移，已吸水溶胀的水凝胶突然收缩，释放出所吸入的水，前后体积相差400倍。此外，溶液中无机盐浓度对水凝胶的溶胀比影响很大。当无机盐浓度大于0.01M时，其溶胀比急剧下降。II、用~(13)C-NMR技术研究AM-AANa辐射共聚物的序列分布用以下两种方法合成了线性AM-AANa共聚物：i、AM-AANa直接辐射共聚，ii、AM辐射均聚，再经碱部分水解。研究了AM-AANa共聚体系和AM均聚体系的吸收剂量，对单体转化率和聚合物分子量的影响。用~(13)C-NMR技术研究了AM均聚物、AANa均聚物和AM-AANa共聚物分子链的微观结构。AM均聚物和AANa均聚物中，羰基振动峰的化学位移分别约为180 ppm和185 ppm；而AM-AANa共聚物的羰基振动峰明显分裂成丙烯酰胺（M）和丙烯酸（A）两个区域，而每个区域又分裂成三重小峰。根据这些小峰的相对面积，可以得到其对应三组元的相对强度，由此得到共聚物的序列分布。我们研究了两种不同方法得到的AM-AANa共聚物的序列分布，将各三组元的相对强度，同由一级Markov统计模型所得到的理论曲线进行了比较，结果表明：i、对于AM-AANa直接共聚物，以M为中心的三组元M（M-bar）M、M（M-bar）M、A（M-bar）M的相对强度值同理论曲线较符合，而以A为中心的三组元A（A-bar）A、M（A-bar）A、M（A-bar）M的相对强度则同理论曲线有较大偏离。ii、对于部分水解产物，结果正好相反，即以A为中心的三组元的相对强度值同理论曲线较符合。iii、直接共聚物嵌段成份较多，A（A-bar）A的相对强度值较大；而部分水解产物交替成份较多，M（A-bar）M的相对强度较大。III、水凝胶吸水法浓缩大分子稀水溶液 蛋白质等生物高分子极不稳定，温度、压力等外界条件的变化，都可以导致蛋白质分子的变质，因而其稀水溶液的浓缩是一般方法难以实现的。本工作用AM-AANa共聚物水凝胶吸水溶胀的办法浓缩蛋白质稀水溶液。由于蛋白质分子具有巨大的排斥体积而不被水凝胶吸收，因而其水溶液得到浓缩。本方法在常温常压下进行，不会导致蛋白质分子的变质，而且浓缩过程中并不需要特殊的装置，因而此方法具有很大的可行性。利用AM-AANa共聚物的相转移现象，通过改变pH值，使吸水溶胀的水凝胶收缩，把所吸入的水释放出来，从而可反复使用水凝胶。我们对几种蛋白质分子稀水溶液进行了浓缩试验，浓缩效率均在80%以上，并发现影响浓缩效率的主要因素有以下几种：i、对于不同的蛋白质水溶液，其浓缩效率不同；ii、对于同一种蛋白质分子，浓缩效率与水溶液的浓度有关，溶液越稀，浓缩效果越好；iii、浓缩效率与水凝胶的交联密度有关。对AM-AANa共聚物水凝胶，可通过调节其吸收剂量R，改变其浓缩效率。

一种用于软件过程建模的适应性Agent协商

大多软件过程模型是预定义的.在变化的应用环境中,需要由相应人员进行适应性调整.提出一种用于软件过程建模的适应性多边协商模型——AMNM-PA,其采用Agent封装软件过程中所涉及的个体,包含组织、团队、个人等,通过Agent间的协商动态、适应地建立针对给定软件项目的软件过程模型.AMNM-PA基于非静态有限阶段Markov决策过程,采用模型无关的Q学习算法选取协商策略,因此能够支持动态、非预知环境下的适应性协商,从而满足软件过程建模对环境的适应性需求.AMNM-PA已经实施于软件过程管理系统——SoftPM.

基于随机网络编码的无线广播重传方案及性能分析

针对自动最复重传(ARQ)机制在无线广播系统中吞吐量性能不佳的缺陷,提出一种基于随机网络编码的广播重传方案RNC-ARQ。对于广播节点,采用随机线性码对所有丢失包进行编码组合重传。对于接收节点,当接收的编码包累积到一定数量后可通过解码操作恢复出原始数据。该方案可有效减少重传次数,改善无线广播的吞吐量性能。基于Gilbert-Elliott模型描述的突发错误信道,建立了信道状态和节点接收处理流程合并的多状态马尔可夫模型,并以此为基础推导了RNC-ARQ方案的TQ吐量闭合解。最后,使用NS-2模拟器评估RNC-ARQ方案的性能,结果表明在突发差错信道下,基于随机网络编码重传方案的吞吐量优于传统的选择重传ARQ方案和基于异或编码的重传方案。

基于半马尔科夫条件随机场的命名体识别及其关系抽取研究

随着互联网和电子化办公的发展，出现了大量的文本资源。信息抽取技术可以帮助人们快速获取大规模文本中的有用信息。命名体识别与关系抽取是信息抽取的两个基本任务。本文在调研当前命名体识别和实体关系抽取中采用的主要方法的基础上，分别给出了解决方案。论文开展的主要工作有：（1）从模型选择和特征选择两个方面总结了命名体识别及实体关系抽取的国内外研究现状，重点介绍用于命名体识别的统计学习方法以及用于实体关系抽取的基于核的方法。（2）针对当前命名体识别中命名体片段边界的确定问题，研究了如何将 Semi-Markov CRFs 模型应用于中文命名体识别。这种模型只要求段间遵循马尔科夫规则，而段内的文本之间则可以被灵活的赋予各种规则。将这种模型用于中文命名体识别任务时，我们可以更有效更自由的设计出各种有利于识别出命名体片段边界的特征。实验表明，加入段相关的特征后，命名体识别的性能提高了 4-5 个百分点。（3）实体关系抽取的任务是判别两个实体之间的语义关系。之前的研究已经表明，待判别关系的两个实体间的语法树结构对于确定二者的关系类别是非常有用的，而相对成熟的基于平面特征的关系抽取方法在充分提取语法树结构特征方面的能力有限，因此，本文研究了基于核的中文实体关系抽取方法。针对中文特点，我们探讨了卷积（Convolution）核中使用不同的语法树对中文实体关系抽取性能的影响，构造了几种基于卷积核的复合核，改进了最短路依赖核。因为核方法开始被用于英文关系抽取时，F1 值也只有40%左右，而我们只使用作用在语法树上的卷积核时，中文关系抽取的F1 值达到了35%，可见核方法对中文关系抽取也是有效的。

A methodology of characterizing status and trend of land changes in oases: A case study of Sangong River watershed, Xinjiang, China

National Natural Science Foundation of China [40471134]; program of Lights of the West China by the Chinese Academy of Science

Color to Gray: Visual Cue Preservation

Both commercial and scientific applications often need to transform color images into gray-scale images, e. g., to reduce the publication cost in printing color images or to help color blind people see visual cues of color images. However, conventional color to gray algorithms are not ready for practical applications because they encounter the following problems: 1) Visual cues are not well defined so it is unclear how to preserve important cues in the transformed gray-scale images; 2) some algorithms have extremely high time cost for computation; and 3) some require human-computer interactions to have a reasonable transformation. To solve or at least reduce these problems, we propose a new algorithm based on a probabilistic graphical model with the assumption that the image is defined over a Markov random field. Thus, color to gray procedure can be regarded as a labeling process to preserve the newly well-defined visual cues of a color image in the transformed gray-scale image. Visual cues are measurements that can be extracted from a color image by a perceiver. They indicate the state of some properties of the image that the perceiver is interested in perceiving. Different people may perceive different cues from the same color image and three cues are defined in this paper, namely, color spatial consistency, image structure information, and color channel perception priority. We cast color to gray as a visual cue preservation procedure based on a probabilistic graphical model and optimize the model based on an integral minimization problem. We apply the new algorithm to both natural color images and artificial pictures, and demonstrate that the proposed approach outperforms representative conventional algorithms in terms of effectiveness and efficiency. In addition, it requires no human-computer interactions.

~(13)C-NMR研究丁二烯-异戊二烯共聚合的竞聚率和共聚物的序列分布

用~(13)C-NMR方法研完了不同配料比的丁二烯-异戊二烯本体共聚和溶液共聚物的结构,定量计算出了共聚物二元组的浓度和数均序列长度,采用T(?)DO″S法计算出了本体共聚和溶液共聚的竞聚率,并证明各种共聚产物的序列分布都服从一级Markov统计模型。

基于马尔可夫随机场模型的图像分割技术研究

本文针对基于马尔可夫随机场模型（MRF）的图像分割技术进行研究，通过深入分析马尔可夫随机场模型用于图像分割时的优缺点，提出了改进方案，将其用于单帧图像的无监督分割和动态场景下的运动目标分割。主要研究内容包括以下几部分。 第一部分详细介绍了马尔可夫随机场模型，包括邻域系统和基团的概念、初始标记场的获取、能量函数的确立和MAP估算方法。 第二部分针对噪声图像的预处理，提出一种多尺度双边滤波算法来综合不同尺度下双边滤波的去噪效果。为降低双边滤波的计算复杂性，提出一种双边滤波快速计算方法。该算法能够在去除噪声的同时较好地保留边缘。 第三部分针对MRF模型用于图像分割中遇到的过平滑问题，定义了一种间断自适应高斯马尔可夫随机场模型（DA-GMRF），提出一种基于该模型的无监督图像分割方法。利用灰度直方图势函数自动确定分类数及分割阈值，进行多阈值分割得到标记场的初始化，用Metroplis采样器算法进行标记场的优化，得到最终的分割结果。该方法考虑了平滑约束在图像边缘处的自适应性，避免了边缘处的过平滑，将其应用于无监督图像分割取得了较好的效果。 第四部分针对动态场景下的运动目标分割，提出一种基于间断自适应时空马尔可夫随机场模型的运动目标分割方法。解决了传统时空马尔可夫随机场模型不能对运动造成的显露遮挡现象进行处理问题，也克服了全局一致平滑假设造成的过平滑问题。帧差图像二值化得到初始标记场，初始标记场进行‘与’操作获得共同标记场，用Metroplis采样器算法实现共同标记场的优化。该方法既使用了平滑约束，而又保留了间断，从而使分割得到的运动目标边缘更加准确。

城市交通中车队状态观测器的一种新算法

本文阐述了离散时间点过程理论,时变马尔科夫链及鞅差分序列在城市交通车队状态观测器中的应用。并在此基础上,改进了[5]中的估计算法。用本文提出的非线性最小方差估计算法,对提供的交通状态进行估计,所得结果比[5]中算法精度有明显提高。在大连市某交通干线计算机控制系统中初步应用,取得了令人满意的结果。

基于PMAC开放式数控系统软件开发

基于PC和多轴运动控制器的开放式数控系统是理想的开放式数控系统。介绍了基于PMAC的开放式数控系统结构形式,PMAC的差补、位置控制、伺服功能、以PMAC和PC机为硬件平台搭建了数控系统,并对其硬件构成和软件设计结构进行了分析。着重从软件设计的角度,介绍了PTALK控件的功能和作用,对数控系统软件构成进行了详细的阐述。并设计出了友好的用户界面,在实际应用中具有重要意义。

改善三维波动方程叠前深度偏移处理效果的方法研究

3D wave equation prestack depth migration is the effective tool for obtaining the exact imaging result of complex geology structures. It's a part of the 3D seismic data processing. 3D seismic data processing belongs to high dimension signal processing, and there are some difficult problems to do with. They are: How to process high dimension operators? How to improve the focusing? and how to construct the deconvolution operator? The realization of 3D wave equation prestack depth migration, not only realized the leap from poststack to prestack, but also provided the important means to solve the difficult problems in high dimension signal processing. In this thesis, I do a series research especially for the solve of the difficult problems around the 3D wave equation prestack depth migration and using it as a mean. So this thesis service for the realization of 3D wave equation prestack depth migration for one side and improve the migration effect for another side. This thesis expatiates in five departs. Summarizes the main contents as the follows: In the first part, I have completed the projection from 3D data point area to low dimension are using de big matrix transfer and trace rearrangement, and realized the liner processing of high dimension signal. Firstly, I present the mathematics expression of 3D seismic data and the mean according to physics, present the basic ideal of big matrix transfer and describe the realization of five transfer models for example. Secondly, I present the basic ideal and rules for the rearrange and parallel calculate of 3D traces, and give a example. In the conventional DMO focusing method, I recall the history of DM0 process firstly, give the fundamental of DMO process and derive the equation of DMO process and it's impulse response. I also prove the equivalence between DMO and prestack time migration, from the kinematic character of DMO. And derive the relationship between DMO base on wave equation and prestack time migration. Finally, I give the example of DMO process flow and synthetic data of theoretical models. In the wave equation prestak depth migration, I firstly recall the history of migration from time to depth, from poststack to prestack and from 2D to 3D. And conclude the main migration methods, point out their merit and shortcoming. Finally, I obtain the common image point sets using the decomposed migration program code.In the residual moveout, I firstly describe the Viterbi algorithm based on Markov process and compound decision theory and how to solve the shortest path problem using Viterbi algorithm. And based on this ideal, I realized the residual moveout of post 3D wave equation prestack depth migration. Finally, I give the example of residual moveout of real 3D seismic data. In the migration Green function, I firstly give the concept of migration Green function and the 2D Green function migration equation for the approximate of far field. Secondly, I prove the equivalence of wave equation depth extrapolation algorithms. And then I derive the equation of Green function migration. Finally, I present the response and migration result of Green function for point resource, analyze the effect of migration aperture to prestack migration result. This research is benefit for people to realize clearly the effect of migration aperture to migration result, and study on the Green function deconvolution to improve the focusing effect of migration.

超参数控制下的波阻抗约束反演

The seismic survey is the most effective geophysical method during exploration and development of oil/gas. As a main means in processing and interpreting seismic data, impedance inversion takes up a special position in seismic survey. This is because the impedance parameter is a ligament which connects seismic data with well-logging and geological information, while it is also essential in predicting reservoir properties and sand-body. In fact, the result of traditional impedance inversion is not ideal. This is because the mathematical inverse problem of impedance is poor-pose so that the inverse result has instability and multi-result, so it is necessary to introduce regularization. Most simple regularizations are presented in existent literature, there is a premise that the image(or model) is globally smooth. In fact, as an actual geological model, it not only has made of smooth region but also be separated by the obvious edge, the edge is very important attribute of geological model. It's difficult to preserve these characteristics of the model and to avoid an edge too smooth to clear. Thereby, in this paper, we propose a impedance inverse method controlled by hyperparameters with edge-preserving regularization, the inverse convergence speed and result would be improved. In order to preserve the edge, the potential function of regularization should satisfy nine conditions such as basic assumptions edge preservation and convergence assumptions etc. Eventually, a model with clear background and edge-abnormity can be acquired. The several potential functions and the corresponding weight functions are presented in this paper. The potential functionφLφHL andφGM can meet the need of inverse precision by calculating the models. For the local constant planar and quadric models, we respectively present the neighborhood system of Markov random field corresponding to the regularization term. We linearity nonlinear regularization by using half-quadratic regularization, it not only preserve the edge, and but also simplify the inversion, and can use some linear methods. We introduced two regularization parameters (or hyperparameters) λ2 and δ in the regularization term. λ2 is used to balance the influence between the data term and the transcendental term; δ is a calibrating parameter used to adjust the gradient value at the discontinuous position(or formation interface). Meanwhile, in the inverse procedure, it is important to select the initial value of hyperparameters and to change hyperparameters, these will then have influence on convergence speed and inverse effect. In this paper, we roughly give the initial value of hyperparameters by using a trend- curve of φ-(λ2, δ) and by a method of calculating the upper limit value of hyperparameters. At one time, we change hyperparameters by using a certain coefficient or Maximum Likelihood method, this can be simultaneously fulfilled with the inverse procedure. Actually, we used the Fast Simulated Annealing algorithm in the inverse procedure. This method overcame restrictions from the local extremum without depending on the initial value, and got a global optimal result. Meanwhile, we expound in detail the convergence condition of FSA, the metropolis receiving probability form Metropolis-Hasting, the thermal procession based on the Gibbs sample and other methods integrated with FSA. These content can help us to understand and improve FSA. Through calculating in the theoretic model and applying it to the field data, it is proved that the impedance inverse method in this paper has the advantage of high precision practicability and obvious effect.

基于MRF模型的油气储层属性随机建模方法研究

Stochastic reservoir modeling is a technique used in reservoir describing. Through this technique, multiple data sources with different scales can be integrated into the reservoir model and its uncertainty can be conveyed to researchers and supervisors. Stochastic reservoir modeling, for its digital models, its changeable scales, its honoring known information and data and its conveying uncertainty in models, provides a mathematical framework or platform for researchers to integrate multiple data sources and information with different scales into their prediction models. As a fresher method, stochastic reservoir modeling is on the upswing. Based on related works, this paper, starting with Markov property in reservoir, illustrates how to constitute spatial models for catalogued variables and continuum variables by use of Markov random fields. In order to explore reservoir properties, researchers should study the properties of rocks embedded in reservoirs. Apart from methods used in laboratories, geophysical means and subsequent interpretations may be the main sources for information and data used in petroleum exploration and exploitation. How to build a model for flow simulations based on incomplete information is to predict the spatial distributions of different reservoir variables. Considering data source, digital extent and methods, reservoir modeling can be catalogued into four sorts: reservoir sedimentology based method, reservoir seismic prediction, kriging and stochastic reservoir modeling. The application of Markov chain models in the analogue of sedimentary strata is introduced in the third of the paper. The concept of Markov chain model, N-step transition probability matrix, stationary distribution, the estimation of transition probability matrix, the testing of Markov property, 2 means for organizing sections-method based on equal intervals and based on rock facies, embedded Markov matrix, semi-Markov chain model, hidden Markov chain model, etc, are presented in this part. Based on 1-D Markov chain model, conditional 1-D Markov chain model is discussed in the fourth part. By extending 1-D Markov chain model to 2-D, 3-D situations, conditional 2-D, 3-D Markov chain models are presented. This part also discusses the estimation of vertical transition probability, lateral transition probability and the initialization of the top boundary. Corresponding digital models are used to specify, or testify related discussions. The fifth part, based on the fourth part and the application of MRF in image analysis, discusses MRF based method to simulate the spatial distribution of catalogued reservoir variables. In the part, the probability of a special catalogued variable mass, the definition of energy function for catalogued variable mass as a Markov random field, Strauss model, estimation of components in energy function are presented. Corresponding digital models are used to specify, or testify, related discussions. As for the simulation of the spatial distribution of continuum reservoir variables, the sixth part mainly explores 2 methods. The first is pure GMRF based method. Related contents include GMRF model and its neighborhood, parameters estimation, and MCMC iteration method. A digital example illustrates the corresponding method. The second is two-stage models method. Based on the results of catalogued variables distribution simulation, this method, taking GMRF as the prior distribution for continuum variables, taking the relationship between catalogued variables such as rock facies, continuum variables such as porosity, permeability, fluid saturation, can bring a series of stochastic images for the spatial distribution of continuum variables. Integrating multiple data sources into the reservoir model is one of the merits of stochastic reservoir modeling. After discussing how to model spatial distributions of catalogued reservoir variables, continuum reservoir variables, the paper explores how to combine conceptual depositional models, well logs, cores, seismic attributes production history.