线面拓扑和度量关系的细分描述和计算方法

由于单独的拓扑关系、度量关系无法区分复杂要素之间的空间关系,在语义描述上存在歧义,提出一种二维空间R~2下空间关系的细分表达模型.该模型将面与线求交后,线面可以表示成n-1个拓扑和度量分量的序列矩阵,在语义上进行更细致的分段描述;采用折线节点微小圆求交的算法,考虑面带洞和目标的不确定性,归纳出线面拓扑关系细分判断的7个规则,以及长度、分割度等度量项的计算方法.最后以土地利用中图斑扣除线状地物计算地类面积为实例,验证了该模型和计算方法对解决实际问题的有效性.     

基于高斯滤波C-V模型分割算法的改进

在现实世界经常有一些灰度不均匀的图像给图像分割造成了很大的困难.为了解决由于图像灰度不均匀造成的图像轮廓提取的困难,结合邻域算法和高斯滤波的性质的基础上,提出了基于图像局部区域灰度信息的算法,使曲线的演化过程基于图像的局部信息.同时采用了水平集演化规则化模型,避免了水平集函数的不断初始化.实验结果表明,新算法无论对合成图像还是真实图像,都能提取出准确的物体轮廓.     

浅析计算机专业少数民族班级的教学方法

本文针对西部地区高校中,计算机专业的少数民族班学生学习的现状,分析了教学方法中存在的一些问题及其原因,提出了几点建议。以期对其他同类地方学校的教学起借鉴作用。     

基于重要目标区的三维地形快速渲染方法

三维地形的显示需要绘制大量的三角形网格,为了提高地形显示速度目前广泛采取了三角形网格简化技术。针对弹道导弹作战区域地形的特殊性,提出了一种基于重要目标区的网格简化方法。从BMP位图读取地形高度信息,以四叉树保存网格数据,按照区域重要性静态确定地形网格疏密程度。并采用DirectX平台,实现了导弹战区三维地形显示,提高了渲染效率。     

双重JPEG压缩图像篡改区域检测与定位

对JPEG(joint photographic experts group)图像实施篡改往往会产生双重JPEG(double JPEG,DJPE) 压缩痕迹,分析该痕迹有助于揭示图像压缩历史并实现篡改区域定位。现有算法在图像尺寸较小和质量因子(quality factor,QF) 较低的时候性能不佳,对两个QF的组合情况存在限制。本文提出了一种端到端的混合QF双重JPEG压缩图像取证网络,命名为DJPEGNet。首先,使用预处理层从图像头文件中提取表征压缩历史信息的量化表 (quantization table,Qtable) 特征,将图像从空域转换至DCT(discrete cosine transform)域构造统计直方图特征。然后,将两个特征输入到由深度可分离卷积和残差结构堆叠而成的主体结构,输出二分类结果。最后,使用滑动窗口算法自动定位篡改区域并绘制概率分布图。实验结果表明,在使用不同Qtable集生成的小尺寸数据集上,DJPEGNet所有指标均优于现有最先进的算法,其中ACC提高了1.78%,TPR提升了2.00%,TNR提升了1.60%。     

基于区域划分和余弦定理的DV-Hop算法改进

针对传统距离向量-跳段定位算法(Distance Vector-Hop,DV-HOP)存在的节点密度分布不均引起误差比较大的问题,根据DV-Hop算法特性,采用余弦定理,证明节点密度越大所得定位坐标越准确,使用余弦定理减少在节点密度过大时因空间角度导致节点路径曲折过多而引起的误差,同时提出一种区域划分的改进算法,减少在节点密度较小区域的误差;利用接收信号强度指示(Received Signal Strength Indicator,RSSI)对跳数进行量化分级处理,改善实际距离与计算出的距离相差过大的情况;最后采用最小二乘法估计未知节点坐标。仿真表明,所提出的算法与传统的DV-Hop算法相比,误差有一定的减少。     

A Modified Region Approach for Multivariate Measurement System Capability Analysis

Measurement system capability analysis is to determine whether the measurement system is capable for use in quality control. The existing research has been extended from univariate to multivariate cases. Two approaches, the multivariate analysis of variance (MANOVA) and the weighted principal components (WPC), were advocated in literature. The MANOVA method is constructed based on the volume ratio that treats the volume of constant‐density contours as the variability estimations. However, it ignores the fact that the relative position change of multivariate measurement errors could affect the measurement system capability. The WPC method uses dimension reduction to reduce the complexity but is unable to build the precision‐to‐tolerance ratio because it does not include tolerance. In this paper, we propose a modified‐region‐based method to compute the precision‐to‐tolerance ratio, the percent of repeatability and reproducibility, and the signal‐to‐noise ratio. This method also incorporates the variance–covariance structure of the measurement errors when dealing with the constant‐density contours of tolerances, total variation, and process variation. The performance of the modified‐region‐based method is evaluated based on a dataset from the literature and a set of relevant simulation. The proposed method proves to be effective compared with other methods.Copyright © 2014 John Wiley & Sons, Ltd.     

液氦温区大型低温制冷系统的可靠性评估

针对改进Claude循环的大型氦低温制冷系统,以制冷量不足为顶事件建立了故障树模型。利用结构法对故障树模型进行定性分析,分析得到系统的最小割集为18个。利用来自不同数据库的部件失效率对故障树模型进行定量计算,得到了不同的结果,并分析其原因。对各部件的关键重要度进行了计算,以衡量各个部件对系统发生故障的贡献程度。并通过增加系统冗余部件和提高关键部件可靠度等措施,为提高系统可靠性找到了有效的解决途径。     

Region-based surveillance video retrieval with effective object representation

Retrieving the most relevant video frames that contain the object specified in a given query (query-by-region) remains a challenging task. Two common challenges of region-based retrieval approaches are to accurately extract or segment object(s) and select a proper matching strategy. This paper addresses these problems by proposing a retrieval approach that uses a new region-based matching technique equipped with an effective object representation method. In the first stage, the proposed approach selects the most informative instances of each object that appeared in the video by utilizing an adapted clustering algorithm over the extracted features. In the retrieval stage, the new matching technique returns the most relevant sequences of video by mapping a given region with those identified representative instances of objects based on their similarity scores. The proposed approach is evaluated on standard datasets and the results demonstrate a 31% improvement in the retrieval performance compared to other state-of-the-art methods.