一种融合实体语义知识的实体集合扩展方法

实体集合扩展是开放式信息抽取的一个重要问题,该问题研究如何从一个语义类的若干实体(称为种子)出发,得到该类别的更多实体。现有实体集合扩展方法主要使用上下文模板或种子在语料中的分布信息进行抽取,其缺点是无法解决种子的歧义问题,而该问题会影响方法的有效性。在该文中,作者提出了一种融合实体语义知识的实体集合扩展方法,通过引入语义知识来解决种子歧义性问题。新方法通过使用Wikipedia实现了语义知识的引入,并把基于语义知识的扩展方法和基于模板的扩展方法相融合。实验表明,与单纯基于上下文方法相比,该文方法在准确率上提升了18.5%,召回率上提升了6.8%,MAP值上提升了22.8%。     

普通话发音评估性能改进

为减少噪声环境对评估性能的影响,该文将PNCC参数引入普通话发音评估。结果表明,其评分相关性在普通话测试实录音数据库上较传统MFCC参数提高了6.6%。在此基础上,对汉语声学模型拆分方法进行了研究,提出将声母介音+韵母模型拆分方法应用到发音评估中。使用这种拆分方式的评估系统总错误率降低5.6%,专家打分相关性则提高了0.056。该文还对模型最佳状态数的选取进行讨论,并提出模型状态数混合和不同配置综合评分两种混合评分方案,在相关性上较同等条件下3状态模型分别提高了0.021和0.017。     

数据挖掘中关联规则Apriori算法

深入研究关联规则算法, 针对Apriori算法瓶颈问题提出了一种改进算法, 该算法在构建向量矩阵的基础上, 只需要扫描一次事务数据库, 通过优化连接和剪枝, 提高了算法的运行效率. 研究和实验表明, 改进后的算法在大规模的事务数据库中, 较Apriori算法有明显的优势.     

基于Aprior & FP-growth算法的研究

关联规则挖掘在数据挖掘中占有极其重要的地位,Apriori算法和FP-growth算法是当前关联规则中两大主要频繁项集发现算法.研究了这两种算法的基本思想,指出了算法各自的优缺点并通过具体的实例说明发现频繁项集的方法,最后通过实验对算法进行了性能上的比较.     

便携式汽车发动机检测装置的设计

本设计是以SEP4020为控制芯片,以μC/OS-II为实时操作系统来管理各个任务,移入了μC/GUI来完成TFT彩色液晶屏的操作,加入FAT32文件系统来对SD卡的数据存储功能进行操作,以TLC2543作为数据采集模块的多通道便携式发动机检测装置.实现了数据的实时采集,数据的显示和存储功能.该设计具有结构简单,易于实现,操作方便,效率高,实时性强,移动和通用性好的特点.     

基于通用知识的软件设计安全性评估

安全性作为软件系统的重要属性,越来越受到人们的重视．在软件开发的早期对安全性进行评估,对软件的质量控制和成本控制有着重要意义．当前的软件安全性评估主要依靠专家评审,结果的客观性及准确性常常受到专家主观意见的影响．通过使用通用知识作为评估依据,提出一种可以对UML顺序图形式的软件设计文档进行自动化分析的方法,可以发现软件设计中潜在的安全性漏洞．该方法可以减少结果中的主观性,同时,通过基于该方法的辅助工具的使用,可以大大提高评估效率．     

高职院校数字化校园建设的研究与思考

进入“十二五”,高职院校普遍完成了院校组建的初期建设,开始进入内涵建设的阶段。文章从高职院校进行数字化校园建设的目标和意义展开,探讨数字化校园作为高职院校内涵建设的重要组成部分,对提升高职院校的教学、科研和管理水平以及建设现代化的高等职业教育有着极其重要的作用;并对面向高等职业教育、以服务院校实际业务为目的高职院校数字化校园建设进行研究与思考。     

Extreme value analysis of streamflow time series in Poyang Lake Basin, China

Extreme meteorological and hydrological events may cause major disasters and heavy social and economic losses.Therefore,more and more studies have focused on extreme hydro-meteorological events in various climates and geographic regions.Based on nearly 50 years of observed records of the Poyang Lake Basin,the occurrence and changing trends of extreme streamflow indices,including the annual maximum flow,annual peak-over-threshold flows,and low flows,were analyzed for ten hydrological stations.The results indicate that most annual maximum flows occurred from April to July,highly attributed to the Southeast Asian summer monsoons,whereas the annual minimum flows were concentrated between January and February.As for the low flow indices (the annual minimum flow,annual minimum 7-d flow,and annual minimum 30-d flow),a significant increasing trend was detected in most parts of the Poyang Lake Basin.The trends illustrate the potential effects of climate change and human activities on the hydrological cycle over the Poyang Lake Basin.     

Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

The K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D’s efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.     

Paint Mesh Cutting

We present a novel progressive painting‐based mesh cut out tool, called Paint Mesh Cutting, for interactive mesh segmentation. Different from the previous user interfaces, the user only needs to draw a single stroke on the foreground region and then obtains the desired cutting part at an interactive rate. Moreover, the user progressively paints the region of interest using a brush and has the instant feedback on cutting results as he/she drags the mouse. This is achieved by efficient local graph‐cut based optimizations based on the Gaussian mixture models (GMM) on the shape diameter function (SDF) metric of the shape. We demonstrate a number of various examples to illustrate the flexibility and applicability of our system and present a user study that supports the advantages of our user interface.