异步脑—机接口的空闲状态检测新方法*

提出一种新的空闲状态检测方法,以训练集中各类运动想象样本的类内散度和正确检测率为指标,结合接收机曲线确定分类阈值,设计最佳三分类器,并采用模糊化技术对预测标签进行处理。将该方法应用于2005年BCI竞赛数据IVc,测试的均方误差为0.278 7。实验结果验证了该方法的有效性。     

一种汽轮机组排汽焓和最优初压在线计算方法

通过对已往的汽轮机变工况计算方法的分析,提出了以汽轮机末级抽汽或次末级抽汽(过热蒸汽状态)为计算起点的汽轮机顺序变工况核算方法。根据初始假定的末级流量和现场实际的末级前热力状态和背压,用汽轮机变工况流型判别准则,判别级的流型,然后从末级前参数开始顺序进行一次级的变工况核算,得到新的排汽焓,最后算得机组热耗率和排汽焓。以此方法为变工况计算的基础,并利用系统分析方法,建立了在线确定单元机组最优运行初压的数学模型。为火电机组的优化运行提出了一种新思路。     

基于扫描枪的字符二维条码识读算法研究

不同于传统的图像二维条码,字符二维条码由字符组成条码矩阵.字符二维条码可通过扫描枪获取后,因其结构不同于传统图像二维条码,需对字符二维条码进行定位、分割以及字符识别.在扫描枪有限的内存空间和运行速度限制下,开发了字符二维条码的识读算法.提出了基于圆定位符的字符二维条码定位算法,对条码字符进行投影分割后,采用基于特征融合的识别方法实现了字符识别.实验表明:种字符二维条码识读算法能够很好地运行在扫描枪上,快速、准确地实现字符二维条码的识读.     

巢湖兆河分洪闸泄流特性试验研究

受周边地形条件及环境限制,巢湖兆河分洪闸与河道呈垂直布置,进流条件不利。针对分洪闸与河道垂直布置这一特征,分别对开敞式和涵洞式分洪闸进行了水工模型进口流态试验分析,结果表明涵洞式分洪闸进流流态优于开敞式分洪闸,能更好地保证各闸孔的均匀进流。针对涵洞式分洪闸方案,研究了分洪闸敞泄和控泄的泄流能力,指出敞泄状态下进口行近流速对流量计算影响明显,在水闸规划设计中,应计入行近流速影响。为改善消能效果及出口水流与下游的衔接,进行了多方案消能防冲改进试验,结果表明尾坎作为一种冲击式消能工布置在挖深式消力池末端,可使出口水流流速分布更加均匀并与下游水流衔接更为平顺,明显增强消能防冲效果。     

表面活性剂对气液两相螺旋管流流动特性的影响

为了考察阴离子型表面活性剂十二烷基硫酸钠(SDS)对气液两相螺旋管流流动特性的影响,该文用实验研究了不同浓度的SDS溶液体系气液两相螺旋管流的流型转变及压降规律。实验装置为由有机玻璃圆管制成的长2 m内径23 mm的实验段,以SDS水溶液和空气为实验介质,气液相折算流速均为0―2.5 m/s,SDS溶液质量浓度10―90 mg/kg,螺旋流由5种不同型号的金属螺旋叶轮诱导产生。利用直接观察和高速摄像相结合的方法观测流型的变化,并用液柱式压差计测量实验管段上下游间压差,实验在常温常压条件下进行。实验共得到螺旋线状流、螺旋波状分层流、螺旋轴状流、螺旋弥散流4种流型,与未添加表面活性剂体系相比较,并未得到螺旋泡状流和螺旋团状流这两种流型。同时,随着SDS溶液浓度的增大,气液两相螺旋流逐渐向螺旋弥散流流型转变,这是因为低浓度的SDS溶液随着其浓度的增大,气液界面张力逐渐减小,而气液掺混程度则会不断增大。此外,与未添加表面活性剂体系相比较,添加了SDS体系的气液两相螺旋管流压降梯度将会减小。最后,阐述了气液两相螺旋管流强化天然气水合物生成的研究及应用现状,并针对多相流研究现状,提出了气液两相流相间传热特性应成为今后研究热点等建议。     

A metamodeling approach for pattern specification and management

The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,in which a pattern is modeled as a meta-level class and its participants are meta-level references.Instead of defining a new metamodel,we reuse the Unified Modeling Language(UML)metamodel and incorporate the concepts of Variable and Set into our approach,which are unavailable in the UML but essential for pattern specification.Our approach provides straightforward solutions for pattern-related problems,such as pattern instantiation,evolution,and implementation.By integrating the solutions into a single framework,we can construct a pattern management system,in which patterns can be instantiated,evolved,and implemented in a correct and manageable way.     

Observer模式在．NET多线程并行计算中的应用

对观察者（Observer）设计模式进行了分析,并利用．NET框架对其进行了改进;基于．NET Framework4．0,将改进后的模型应用到多线程并行程序的设计中,从而简化了开发过程,并且使系统结构更清晰,便于扩展和维护。     

将局部二进制模式应用于动态纹理识别的新方法

动态纹理的处理、描述与识别是纹理分析的热门领域。动态纹理是对普通纹理在时间域方面的扩展,包括动态特征和静态特征。基于LBP算法的扩展提出的VLBP算法较好的描述了动态纹理特征,但是计算量过大,模板过多。本文提出了一种由VLBP算法改进的基于局部二进制运动模式的特征提取方法用以动态纹理的描述和识别,它包括提取动态特征和提取静态特征两部分。将LBP算子做为块匹配准则提取局部二进制运动模式柱状图做为动态特征的描述,提取LBP柱状图做为静态特征的描述,并将二者连接得出描述动态纹理特征的联合的局部二进制运动模式柱状图。通过对DynTex集实验的结果表明,本文提出的方法在性能和识别率方面均要优于VLBP。     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.