WSN中面向非均匀形状事件的动态检测方案

高效且准确地事件检测与跟踪是无线传感器网络研究的热点问题之一,现有的事件检测方案无法实现对形状不定的区域事件的检测与跟踪,为了解决这一问题,提出了一种改进的动态事件检测和跟踪方案.首先,该方案利用事件质心确定事件的具体位置,然后,考虑了节点的读数强度及该读数与事件质心的距离,提出节点动量概念以引导事件融合和分割检测.仿真实验结果表明,该方案可在各传感器结点中分布式运行,延迟低,能效高,在各种条件下均有很高的准确度.不论事件大小和事件数量如何,它始终可以确定正确的事件数量,给出正确的事件形状.     

Identification methods for anomalous stress region in coal roadways based on microseismic information and numerical simulation

It is believed that the microseismicity induced by mining effect and gas gradient disturbance stress is a precursor to the essential characteristics of roadway unstability. In order to effectively identify and evaluate the stability of coal roadways in the process of mine development and extraction, a microseismic monitoring system was deployed for the study of the stress evolution process, damage degree and distribution characteristics in the tailgate and headgate. The mine under study is the 62113 outburst working face of Xin Zhuangzi coalmine in Huainan mining area. The whole process of microfractures initiation,extension, interaction and coalescence mechanisms during the progressive failure processes of the coal rock within the delineated and typical event clusters were investigated by means of a two dimensional realistic failure process analysis code(RFPA2D-Flow). The results show that the microseismic events gradually create different-sized event clusters. The microseismicity of the tailgate is significantly higher than that of the headgate. The study indicates that the greater anomalous stress region matches the area where microfractures continuously develop and finally connect to each other and form a fissure zone.Due to the mine layout and stress concentration, the ruptured area is mainly located on the left shoulder of the tailgate roof. The potential anomalous stress region of the coal roadway obtained by numerical simulation is relatively in good agreement with the trend of spatial macro evolution of coal rock microfractures captured by the microseismic monitoring system. The research results can provide important basis for understanding instability failure mechanism of deep roadway and microseismic activity law in complex geologic conditions, and it ultimately can be used to guide the selection and optimization of reinforcement and protection scheme.     

半监督中文事件抽取中的模板过滤和转换方法

事件模板是指导事件抽取工作的依据,半监督方法下模板的准确性显得尤为重要.目前,基于双视图的“触发词-论元”模板的中文信息事件抽取系统不能有效地解决触发词一词多义的现象和模板稀疏现象.提出了一种借助论元进行触发词语义消歧的方法,并利用该方法进行模板过滤以消除无效模板的影响.另外,针对几种特殊的中文句型,根据句法结构提出了模板转换规则,从而提高了模板的适用性.在ACE2005中文语料上的测试表明,该方法可有效地提高半监督中文信息事件抽取系统的性能.     

中文事件时序关系的标注和分类方法

事件时序关系的研究在问答系统、信息抽取和文本自动摘要等自然语言处理领域起着重要的作用。首先借鉴英文的标注方法,将中文事件的时序关系分为4类,并给出了具体的标注方法。然后给出了一个中文时序关系的分类方法。最后,在标注语料库上测试了中文事件时序关系分类的性能,结果表明该方法优于规则方法。     

事件常识的获取方法研究

在动态语义学的基础上,以事件的语义、文法和常识为标准构建多层次的事件分类体系,并使用该分类体系提取事件的常识知识。事件采用框架的方式来表示,称为事件框架。事件框架的内容包括事件的定义、事件之间的关系、事件的文法表达、事件的谓词表示、事件的例句、事件的前提常识和后果常识。利用事件框架的内容提取事件的常识。为了说明该方法的实用性,以事件分类体系中典型的“交易类”事件为例来描述事件常识的获取方法。     

基于Event-B和Rodin开展形式化软件工程教学

形式化软件工程是软件工程的重要组成部分。Event-B方法是一种软件形式化开发方法,Rodin是支持Event-B方法的开放工具集。基于Event-B方法和Rodin开展形式化软件工程教学,有益于学生正确理解精化等重要的软件工程概念,理解并掌握开发可信软件的方法,是软件工程教学的重要补充。     

一个事件驱动的中间件平台

事件驱动具有异步多点通信的优点,引起了广泛的研究兴趣。提出了一个由基层和元层两层结构组成的自适应中间件框架,元层主要由接口元模型、组装元模型和感知元模型3个相互独立的模型组成。感知元模型负责数据在对象间流动,为应用提供运行时的环境。给出了感知元模型的设计和实现方法,基于有限状态自动机和时序逻辑提出了系统的形式化规范。为兼顾系统和应用两级并发,系统设计结合了事件和线程。图形用户接口系统在平台上的实现证明了平台在开发复杂的并发应用方面有着广阔的前景。     

基于C#语言的Windows应用程序设计的课堂引入

本文通过一个具体的点名应用程序的设计与实现,讲解了如何将学生引入学习基于C#语言的Windows应用程序设计的过程。     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

Continuous query processing over music streams based on approximate matching mechanisms

It is foreseen that more and more music objects in symbolic format and multimedia objects, such as audio, video, or lyrics, integrated with symbolic music representation (SMR) will be published and broadcasted via the Internet. The SMRs of the flowing songs or multimedia objects will form a music stream. Many interesting applications based on music streams, such as interactive music tutorials, distance music education, and similar theme searching, make the research of content-based retrieval over music streams much important. We consider multiple queries with error tolerances over music streams and address the issue of approximate matching in this environment. We propose a novel approach to continuously process multiple queries over the music streams for finding all the music segments that are similar to the queries. Our approach is based on the concept of n-grams, and two mechanisms are designed to reduce the heavy computation of approximate matching. One mechanism uses the clustering of query n-grams to prune the query n-grams that are irrelevant to the incoming data n-gram. The other mechanism records the data n-gram that matches a query n-gram as a partial answer and incrementally merges the partial answers of the same query. We implement a prototype system for experiments in which songs in the MIDI format are continuously broadcasted, and the user can specify musical segments as queries to monitor the music streams. Experiment results show the effectiveness and efficiency of the proposed approach.