An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

同异反统计方法的应用及优势

介绍了一种新的统计方法———同异反统计 ,例举了这种统计方法的应用 ,并与传统数理统计方法相比较 ,肯定这种新统计方法的优越性     

高校科技创新团队组建的要素分析

高校科技创新团队建设作为推进人才组织模式创新的重大举措,对于提升高等学校的创新能力和竞争实力具有重要作用,但目前对于高校科技创新团队的组建和管理还处于摸索阶段,还有很多问题需要解决。在对高校科技创新团队组建背景、研究现状及其概念、类型、特点等综合分析的基础上,可以将高校科技创新团队组建的要素概括为六个方面,即为什么组建、由谁组建、组建的条件、组建的时间、地点以及如何组建。在此基础上创建的高校科技创新团队组建的创新模型对在实际中组建高校科技创新团队具有一定的参考价值。     

“多元整合”程序教法在球类教学中的运用研究

通过教学试验,研究了“多元整合”程序教法在球类教学中的运用效果,结果表明：“多元整合”程序教法以整合练习带单项技术,符合球类技术的掌握规律和学生的学习心理。     

一般信息系统的PoClustering与概念格

传统聚类方法生成的子集,一般来说都是不相交的.而严格的不相交分类结构,不能充分表现象本体这样的事物间丰富的类关系.在基因本体中,类与子类既不是简单的树也不是格结构,而是一个有向非循环图,其任何子女都可能有多个父结点.PoClustering是相异数据的一种无损聚类方法,概念格则反映了数据的对象和属性的对应关系.采用了PoClustering方法,在保持尽量多的信息的前提下建立一般数据集(信息系统)的属性确定下的概念化分类,讨论了它的算法,从概念格的角度研究了这种类的结构特征.     

R~2上完备性定理的等价性循环证明

给出了R2上五个完备性定理的循环证明,从理论上更详细地说明了定理之间的等价性.     

Chan-Vese图像分割模型的快速实现算法的研究

重初始化是水平集方法耗时的一个主要原因,通过将水平集函数与符号距离函数的偏差作为能量项引入C-V模型,以此来约束水平集函数成为符号距离函数,从而去掉了重初始化步骤。在检测多目标时,采用了曲线族代替单曲线作为初始曲线。在数值算法上,利用加性可操作分裂算子(AOS)消除了对时间步长的限制,可以选择大步长,从而加速了演化过程而且绝对稳定。实验结果表明,分割速度相对于经典的C-V模型有了很大的提高,而精度损失可以忽略。     

基于粗集理论的PPP项目风险评价研究

能否正确识别PPP项目管理中存在的风险以及对风险的重要性作出评价关乎PPP项目的成败。为了解决PPP项目管理中的这类问题,通过定性方法德尔菲法对广东某条高速公路的PPP项目风险进行识别并且加以分析,首先提出基础设施PPP项目中存在的风险因素,然后采取定量方法基于层次分析法的模糊综合评价法结合粗集理论进行模糊定量分析,从而对PPP项目风险进行评估和预测。最后得出模糊综合评价法在PPP风险评估的优越性以及此PPP项目的风险系数。     

传统饮料热灌装系统的灌装控制改进和结构优化

第1代PET塑料瓶饮料热灌装机存在灌装控制系统简易、工艺不稳定、易发生料液渗漏的问题,使得产品质量不稳定,存在一定风险。笔者提出一种基于传统热灌装系统的新控制系统和灌装通路优化方法：通过对灌装阀组设计增加新的卫生级气动控制模块,实现灌装工艺的时间变量控制;同时通过优化分配器和排气管路结构,提高了灌装过程中排气通道的可靠性,消除了冷凝水回流对产品的污染。该改进方法实现了以少量的改造投入,有效提高了工艺稳定性,提高了产品的灌装质量。     

Reducing differences between profiles of weights: A “peer-restricted” cross-efficiency evaluation

This paper deals with the selection of the profiles of weights to be used in cross-efficiency evaluations. In an attempt to prevent unrealistic weighting schemes, one of the issues of main interest that we address here is that of the zero weights, since their use implies that some of the variables considered are excluded from the assessments to be made. In the calculation of cross-efficiency scores, we propose to ignore the profiles of weights of the DMUs that cannot make a choice of non-zero weights among their alternate optima. The different units are therefore assessed in a peer-evaluation that does not consider the profiles of weights of some inefficient DMUs. This approach is referred to as “peer-restricted” cross-efficiency evaluation. Aside from avoiding zero weights, the choice of weights that we make also seeks to reduce the differences between the weights profiles selected as much as possible. Thus, in the “peer-restricted” cross-efficiency evaluation in the present paper we also try to avoid that the different DMUs attach very different weights to the same variable. Finally, we extend this approach to derive a common set of weights by exploiting the idea of similarity between profiles of weights.