基于词内部模式的新词识别

提出一种基于词内部模式的新词识别算法,该算法在重复串查找的基础上,结合词内部模式的特征提出改进位置成词概率和首尾单字成词概率的加权,依次判断互信息、邻接类别等统计量,对新词进行识别。通过不同的实验对比发现,该算法在一定程度上能有效提取新词。     

基于模糊Lyapunov函数方法的模糊广义系统H∞控制

研究一类T-S模糊广义系统的容许性条件和H∞控制问题. 首先将原系统表示成增广系统, 进而基于新的模糊Lyapunov函数和模糊控制器得到容许性条件. 所得开环容许条件不要求子系统是容许的; 闭环容许条件中不含有控制输入矩阵与控制增益矩阵的乘积项. 对于H∞控制问题, 利用隶属度函数的性质对H∞控制条件进行了放宽, 并得到了改进的严格线性矩阵不等式(LMI)形式的H∞控制条件. 数值算例表明所得结论较已有文献具有较小的保守性.     

Hamiltonian-connected graphs

For a simple graph G, let . In this paper, we prove that if NCD(G)≥|V(G)|, then either G is Hamiltonian-connected, or G belongs to a well-characterized class of graphs. The former results by Dirac, Ore and Faudree et al. are extended.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

A novel fault-tolerant sensor system for sensor drift compensation

The conventional fault-tolerant sensor systems would fail when outputs from incorporated sensors are either noisy or drifting. This paper presents a novel real-time fault compensation method, which uses state estimation and compensation techniques, that the sensor system can perform robust measurements even when outputs from every incorporated sensor are noisy and drifting. In a simulation example, the proposed design can detect and correct the sensor errors (dc bias and drift) in real time. For the dc bias, the minimum detectable offset value is 0.1, which is the same as the standard deviation of the sensor noise. The compensated sensor output is biased at values smaller than 0.02. For the sensor drifts, the proposed method can compensate drifts for the change rate of drifts up to four times faster than that of the signal to be measured. The highest change rate of drifts, that can be compensated by this method, is determined by the standard deviation of the sensor noise.     

大容量闪存的层次型热数据识别框架

热数据识别对于提高闪存垃圾回收的效率、延长闪存寿命都有重要的作用。针对大数据量下热数据识别难的情况,该文提出一种层次型热数据识别框架,采用在多个层次上记录热数据的方法。实验证明,该框架能够在消耗较少资源的情况下,识别和预测出大数据量中的热数据。     

IP地址的可持续使用研究与定位

针对IP资源的可再利用,提出了对IP协议的改进,在理论上进行了有益的探索,在概述IP协议的基础上针对IP资源被耗尽的问题提出了对IP协议的改进,以使IP资源可持续利用;并在改进IP协议的过程中带了一种附加效益,即利用改进后的IP协议对网络设备进行简便而准确的地理定位。并阐述了改进后IP地址的结构和面临的问题。     

浅析软件开发项目过程中的风险管理

软件项目开发中存在的各类风险会影响项目计划的实现,如果项目风险变成现实,就有可能影响项目的进度,增加项目的成本,甚至使软件项目不能实现。成功的项目管理一般都对项目风险进行了良好的管理。该文就软件开发项目过程中存在的风险进行分析,并比较了几种主流的风险管理模型,探讨如何对项目进行有效的风险管理,从而最大限度的减少风险的发生。     

基于路面识别实时控制车轮滑移率的ABS系统

汽车防抱死制动系统(ABS)是一种很重要的汽车主动安全技术。并针对路面具体情况,对车辆防抱制动系统的滑移率实时控制进行研究。该文在MATLAB/Simulink仿真环境下,建立车辆动力学模型,实现了对路面状况识别,同时对基于滑移率控制的防抱制动系统的计算机仿真。仿真结果表明,该系统能真实地反映汽车ABS系统的实际工作过程,达到了满意的控制效果。     

改进自组织映射神经网络在指纹识别中的应用

改进自组织映射神经网络方法是将常规自组织映射神经网络方法结合确定性水平,对网络的输入矢量进行预处理。通过实验比较了这种改进的自组织映射神经网络识别方法与常规的自组织映射神经网络识别方法的识别效果,在识别性能上有了很大的提高。