一种在铸件缺陷识别中的区域分形分割方法

在利用X射线对铸造缺陷识别的过程中,由于X光图像中铸件形成的背景复杂,传统的区域分割技术难以分割出缺陷集中的兴趣区域。利用缺陷边缘的分形特性从铸件边缘线图中确定缺陷的边缘线,以聚类方法将缺陷边缘线聚集的区域,即缺陷集中的兴趣区域分割出来。针对汽车铝合金轮毂铸件X光图像,用铸造缺陷区域的分割实验结果证明了该方法的有效性。     

多类型热电机组群实时负荷动态分配方法

在运行灵活性提升背景下,考虑热电厂内不同类型热电机组的可行运行区间及其相关辅机运行动态安全边界,以实现热电厂整体效益最大为目标,提出一种多类型热电机组群实时负荷动态分配方法。该方法计及热电厂的售电收益、运行成本、辅助调峰市场收益、负荷跟踪惩罚,并通过设置机组出力最小调整量降低各机组出力调整频次。基于东北地区某热电厂实际数据的算例结果验证了所提方法的有效性和优越性。     

含混合多端直流的电力系统静态电压稳定域构建

针对含混合多端直流输电(Hybrid-MTDC)的交直流系统静态电压稳定域(SVSR)构建难题,提出一种含Hybrid-MTDC的交直流系统静态电压稳定域边界(SVSRB)快速搜索的预测-校正方法。计及多类型换流站的控制策略切换特性和站间控制策略协同,构建含Hybrid-MTDC的交直流系统连续潮流模型,搜索SVSRB上的首个临界点。借助获取的首个临界点信息,根据SVSRB拓扑特性,通过所提预测-校正模型实现含Hybrid-MTDC的交直流系统SVSRB上相邻临界点的快速、准确获取,进而构建出含Hybrid-MTDC的交直流系统SVSR。通过含Hybrid-MTDC的IEEE 5节点和IEEE 118节点测试系统对所提方法进行分析验证,结果表明所提方法可实现含Hybrid-MTDC的交直流系统SVSR高效、准确构建。     

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。     

基于区域协作的Cache压缩

为提高Cache的有效容量,进行了Cache压缩研究,并提出了一种区域协作压缩(RCC)方法,以提升最后一级缓存的压缩率。与传统的Cache压缩算法不同,RCC方法利用了缓存区域的压缩局部性,使用缓存区域中第一个缓存块的字典信息来协作压缩缓存区域中的其他各个缓存块,而不需要对缓存区域进行整体压缩。RCC有效发掘了缓存区域内缓存块之间的数据冗余,实现了接近以缓存区域为压缩粒度的字典压缩的压缩率,然而压缩、解压缩延时却仍然和压缩单个缓存块时相当。实验结果表明,与单缓存块压缩算法C-PACK相比,RCC方法的压缩率平均提升了12.34%,系统的性能提升了5%。与2倍容量的非压缩Cache相比,有效容量提升了27%,系统性能提升了8.6%,而面积却减少了63.1%。     

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.