基于软总线的分布式桥梁健康监测系统研究

对桥梁的健康状况进行实时监测和长期统计分析具有重要的意义;为了降低一个城市多座桥梁设施的监控维护成本,提高监控能力,对多座桥梁实施松耦合分布式集中监控是一个可行的途径;但目前已建立的单一桥梁健康监测系统采用了不同的监控技术和网络结构,直接通过某种硬件设备适配实现互联,难度高且代价大;在桥梁健康监测系统中引入工业控制软总线,利用软总线平台实现各种不同桥梁现场子系统的分布式接入和集中监控,突破了传统的单一专用桥梁监测系统和目前基于标准网络接口的远程专用桥梁健康监测系统,可节约运行维护成本,提高监控效率和性能;目前,国内外这方面的应用研究处于起步试验阶段.     

离散事件系统与连续系统仿真的C语言实现

现行的计算机仿真语言分为通用仿真程序语言和专用仿真程序语言，前者如GP田、DYNAMD，后者如IDIMS‘GCMS等。前者对用户是一个黑盒系统，而后者又存在专用性太强、应用范围小的局限性。本文通过两个实例的剖析和实现，力图在广大程序员都十分熟悉的C语言与系统仿真的实现之间架起一座桥梁。     

网上沟通畅所欲言——谈留言板的制作

如果在网络课件中添加一个留言板，就在课件中架起了一座教师与学生间的桥梁，可以给学生一个交流的空间。下面主要介绍一下制作留言板的关键步骤，相信经过以下的学习，您一定能独立制作出一个设计出色、功能完整的留言板。     

基于物联网的桥梁健康监控系统设计与实现

物联网技术的研究和应用在各个领域都得到了蓬勃发展,主要针对桥梁健 康安全隐患,设计、实现了一套基于物联网技术的桥梁健康监控系统,并进行了实例验证。 通过物联网技术对桥梁结构的拱肋变形、拱座水平相对位移、拱座不均匀沉降、主梁变形、 吊杆系杆索力、拱肋主梁应变及环境信息进行实时智能采集与传输;研发了中间件套件,实 现对桥梁结构实时信息的接收、存储;通过可视化监控软件进行远程监控与预警,为桥梁监 控、管理、养护提供决策依据,保证桥梁基础设施的安全使用。论文结合物联网技术在某大 桥健康监控中的应用案例分析,是有益的探索。     

浅论信息技术与小学数学教学的关系

数学是抽象性、逻辑性很强的一门学科。而小学生的思维正处于由具体形象思维为主向抽象逻辑思维为主的过渡阶段，所以小学数学必须在数学知识的抽象性和学生思维的形象性之间架起一座桥梁，信息技术正是这样一座桥梁。在信息化进程迅猛发展的今天，信息技术与学科整合的形势已刻不容缓，在学校教学中所占重要地位不言而喻。     

带OCL约束条件的类图到object—Z规格说明的转换

如何提高软件的可靠性是目前软件研究领域的一个热点。将形式化方法和主流的软件开发方法相结合是一个可行的方法。本文研究UML语言和Object-Z语言相结合的方法，为主流的软件开发人员所使用的图形化规格说明技术与形式方法提供的精确的分析和验证技术架起了一座桥梁。本文定义如何将带0CL约束条件的类图转换到Object-Z规格说明的方法。这样不仅可以通过支持Object-Z语言的工具采对UML语言描述的系统性质进行验证和确认，而且能够帮助规格说明人员方便地构造Object-Z规格说明。     

充分利用信息技术辅助小学数学教学

数学是抽象性、逻辑性很强的一门学科。小学生的思维正处于由具体形象思维为主向抽象逻辑思维为主的过渡阶段，所以小学数学教学必须在数学知识的抽象性和学生思维的形象性之间架起一座桥梁。信息技术教学手段正是这样一座桥梁。如何利用好这座桥梁，帮助学生顺利达到成功的彼岸呢？我认为应该做好以下几个方面。     

Text转换为Access MDB

Text文件类型在很多软件中都为一般应用程序与数据库之间架起一座桥梁，你可以使用Text ISAM驱动程序和SQL来把Text文件转换成Access MDB数据库文件。首先为文本文件创建一个SCHEMA．INI文件，然后使用下面的代码来实现转换：     

基于World Wide Web的决策支持模型研究

文中通过对基于Ｗｏｒｌｄ Ｗｉｄｅ Ｗｅｂ（ＷＷＷ）上建立决策支持电子环境的研究，利用中介代理的概念，在用户和技术的拥有者之间架起了一座桥梁，构成了决策网，从而使ＷＷＷ上的决策技术得到共享。讨论了其结构、性能和各种功能，为进一步研究ＷＷＷ上的决策支持提供了一条途径。     

桥梁作为潜在恐袭目标的ANP仿真模型

为评估桥梁恐怖袭击风险,提出了一种基于网络分析法（ANP）的桥梁恐怖袭击风险评估仿真模型,通过构建桥梁恐怖袭击风险评估指标体系和ANP网络模型,采用delphi咨询法和Saaty标度法对部分定性评估指标进行了量化,利用Super Decision软件,分别使用ANP和AHP两种方法对某地区5座桥梁作为潜在恐袭目标进行了风险评估和对比分析。仿真结果表明,ANP方法能改进AHP风险评估模型的不足,评估结果可为相关部门反恐提供决策参考依据。     

Deadline guaranteed packet scheduling for overloaded traffic in input-queued switches

Many applications need to solve the deadline guaranteed packet scheduling problem. However, it is a very difficult problem if three or more deadlines are present in a set of packets to be scheduled. The traditional approach to dealing with this problem is to use EDF (Earliest Deadline First) or similar methods. Recently, a non-EDF based algorithm was proposed that constantly produces a higher throughput than EDF-based algorithms by repeatedly finding an optimal scheduling for two classes. However, this new method requires the two classes be non-overloaded, which greatly restricts its applications. Since the overloaded situation is not avoidable from one iteration to the next in dealing with multiple classes, it is compelling to answer the open question: Can we find an optimal schedule for two overloaded classes efficiently? This paper first proves that this problem is NP-complete. Then, this paper proposes an optimal preprocessing algorithm that guarantees to drop a minimum number of packets from the two classes such that the remaining set is non-overloaded. This result directly improves on the new method.     

Reclassification as supervised clustering

In some branches of science, such as molecular biology, classes may be defined but not completely trusted. Sometimes posterior analysis proves them to be partially incorrect. Despite its relevance, this phenomenon has not received much attention within the neural computation community. We define reclassification as the task of redefining some given classes by maximum likelihood learning in a model that contains both supervised and unsupervised information. This approach leads to supervised clustering with an additional complexity penalizing term on the number of new classes. As a proof of concept, a simple reclassification algorithm is designed and applied to a data set of gene sequences. To test the performance of the algorithm, two of the original classes are merged. The algorithm is capable of unraveling the original three-class hidden structure, in contrast to the unsupervised version (K-means); moreover, it predicts the subdivision of one of the original classes into two different ones.     

Characteristic analysis of Otsu threshold and its applications

This paper proves that Otsu threshold is equal to the average of the mean levels of two classes partitioned by this threshold. Therefore, when the within-class variances of two classes are different, the threshold biases toward the class with larger variance. As a result, partial pixels belonging to this class will be misclassified into the other class with smaller variance. To address this problem and based on the analysis of Otsu threshold, this paper proposes an improved Otsu algorithm that constrains the search range of gray levels. Experimental results demonstrate the superiority of new algorithm compared with Otsu method.     

具有随机化统计检验的聚类分析算法与网络实现

聚类分析是应用最为广泛的数学方法之一，但又被认为是数学上不严格的一类方法。主要原因在于聚类过程及其结果没有统计学标准。本文建立了具有随机化统计检验的聚类分析算法，用于对若干个样品进行有显著性标记的聚类分析。该算法由三部分组成：距离测度计算、随机化检验和系统聚类。在该算法中，有14种距离测度、三种系统聚类方方法及指标加权与否可供选择。样品之间的距离定义为：1-随机化检验的P检验值；两类间的距离若满足P检验标准则合并为同一类是统计上显著的、可接受的，否则就是不显著的、不可接受的。算法的特点是：用随机化方法进行差异显著性检验，使得对多种距离测度可进行严格的统计检验，随机化检验不需统计前提和假设，适用于各种统计问问题；用于差异显著性检验的随机化方法需要随机化数值为正整数值，适用范围过窄，用数值同步移位和平移方法可使之适用于实数域。算法用Java语言网络化实现，包含六个类和一个HTFML文件。可通过网络在多种Java兼容的浏览器上实现算法共享。根据水稻田无脊椎动物多样性的调查数据，本文对该算法进行了对比分析，并讨论了选择距离测度的一些原则和进一步研究的途径等问题。     

DOOD类等级中派生类的定位算法及实现

文中给出了在DOOD中类的定义和表示，并以此为基础介绍了利用规则生成派生类。着重讨论了在类等级中派生类的定位和实现问题，提出了一种定位算法，最后讨论了基类和派生类之间的链接方法。     

Average Case Analysis of Gosper’s Algorithm for a Class of Urn Model Inputs

In this paper we perform an asymptotic average case analysis of some of the most important steps of Gosper’s algorithm for indefinite summation of hypergeometric terms. The space of input functions of the algorithm is described in terms of urn models, and the analysis is performed by using specialized probabilistic transform techniques. We analyze two different types of urn model classes: one in which the input functions are assumed to be rational, and another for which a certain function of two inputs is assumed to be rational. The first set of results shows that the asymptotic complexity of the algorithm is the same within each of the two classes. The second set of results indicates that the complexity of the algorithm scales differently for the two classes of models: one can observe the logarithmic versus square root type of difference that is also present in other combinatorial models.     

Data Clustering with Partial Supervision

Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semi-supervised clustering algorithm based on a modified version of the fuzzy C-Means (FCM) algorithm. The objective function of the proposed algorithm consists of two components. The first concerns traditional unsupervised clustering while the second tracks the relationship between classes (available labels) and the clusters generated by the first component. The balance between the two components is tuned by a scaling factor. Comprehensive experimental studies are presented. First, the discrimination of the proposed algorithm is discussed before its reformulation as a classifier is addressed. The induced classifier is evaluated on completely labeled data and validated by comparison against some fully supervised classifiers, namely support vector machines and neural networks. This classifier is then evaluated and compared against three semi-supervised algorithms in the context of learning from partly labeled data. In addition, the behavior of the algorithm is discussed and the relation between classes and clusters is investigated using a linear regression model. Finally, the complexity of the algorithm is briefly discussed.     

部分四值逻辑中Sheffer函数的判定

多值逻辑是指一切逻辑值的取值数大于2的逻辑。Sheffer函数的判定问题是多值逻辑完备性理论中的一个重要问题,此问题的解决依赖于定出多值逻辑函数集中所有准完备集的最小覆盖。在深入研究部分四值逻辑中Sheffer函数的基础上,根据部分四值逻辑中准完备集的最小覆盖,给出了一个部分四值逻辑中Sheffer函数的判定算法。此算法能够判定任意一个函数是不是部分四值逻辑中的Sheffer函数。     

Multiclass classifier based on boundary complexity

This paper presents several criteria for partition of classes for the support vector machine based hierarchical classification. Our clustering algorithm combines support vector machine and binary tree, it is a divisive (top-down) approach in which a set of classes is automatically separated into two smaller groups at each node of the hierarchy, it splits the classes based on the normalized cuts clustering algorithm. Our clustering algorithm considers the involved classes rather than the individual data samples. In the new proposed measures, similarity between classes is determined based on boundary complexity. In these measures, concepts such as the upper bound of error and Kolmogorov complexity are used. We reported results on several data sets and five distance/similarity measures. Experimental results demonstrate the superiority of the proposed measures compared to other measures; even when applied to nonlinearly separable data, the new criteria perform well.     

A new design for end-to-end proportional loss differentiation in IP networks

This paper describes the algorithms and the architecture of a network able to provide end-to-end proportional packet loss probabilities at the flow level. We show that the combination of a simple classification technique at the sources, and a network core having two internal service classes, is sufficient to achieve proportional service without the need to deploy coordinated, complex per-hop scheduling schemes or signaling protocols, which is the conventional approach. The proposed architecture is complementary to any differentiation algorithm used by the routers. Our results show that any network endowed with some internal service classes with respect to packet loss probabilities can be exploited to build a set of external service classes with end-to-end and per-flow guarantees.