Empirical Comparisons of X‐bar Charts when Control Limits are Estimated

A control chart is a very common tool used to monitor the quality of business processes. An estimator of the process variability is generally considered to obtain the control limits of a chart when parameters of the process are unknown. Assuming Monte Carlo simulations, this paper first compares the efficiency of the various estimators of the process variability. Two empirical measures used to analyze the performance of control charts are defined. Results derived from various empirical studies reveal the existence of a linear relationship between the performance of the various estimators of the process variability and the performance of charts. The various Monte Carlo simulations are conducted under the assumption that the process is in both situations of in‐control and out‐of‐control. Copyright © 2015 John Wiley & Sons, Ltd.     

被动定向浮标阵定位误差仿真分析

仿真分析了被动定向浮标阵为正三角形阵时,阵元间距等几种因素对利用最小二乘法解算目标位置精度的影响。仿真结果表明,在相同的位置误差和测向误差下,当目标在一定范围内时,阵元间距增大,定位误差增大,当目标位置超出该范围后,浮标阵定位误差急剧增大,阵元间距大的浮标阵定位误差反而小;在其它条件相同时,位置误差(测向误差)增大,定位误差增大。因此,为提高三角形阵的定位精度,应尽量提高浮标的测向精度和浮标位置精度;使用中应根据浮标性能和实际使用需求选取合理的布阵间距。     

An improved anti-forensics JPEG compression using Least Cuckoo Search algorithm

The lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least Cuckoo Search algorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimization algorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34?dB, and MSE of 0.1789 which prove the efficacy of the proposed method.     

基于改进双曲正切函数的变步长最小平均p范数算法

在信号处理领域,传统的自适应滤波算法采用的固定步长会导致稳态误差和收敛速度无法同时兼顾。针对这个问题,对最小平均p范数(Least Mean p-norm,LMP)算法进行改进,提出了一种基于改进双曲正切(tanh)函数的变步长最小平均p范数算法。该算法利用改进的tanh函数来调节步长,采用移动加权平均法构造变步长函数;同时引入了一个调节函数以进一步提升算法的性能。通过在海洋脉冲噪声干扰下进行仿真,实验表明,与已有的固定步长和变步长算法相比,改进的变步长LMP算法较好地兼顾系统的收敛速度和稳态误差;引入调节函数后的新算法在保证原有算法收敛速度的同时进一步降低了算法的稳态误差,从而兼顾了算法的收敛性和稳定性,具有较好的可行性。     

复合泡沫填充管的高g值冲击防护特性

目的 为避免或减小高g值冲击对弹内轻质元器件的破坏,应加强对轻质元器件缓冲防护结构的研究。方法 基于新型复合泡沫和通孔泡沫铝的2种泡沫填充管,通过万能试验机和落锤冲击系统研究了2种泡沫填充管的静动态力学特性,并运用数值模拟方法研究高g值冲击下等质量的泡沫填充管与夹芯管的加速度缓冲效果和吸能机制。结果 数值模拟所得结构变形和落锤加速度与实验结果较为一致,验证了数值模拟方法的可靠性。复合泡沫平台应力具有显著的应变率效应,其填充管压溃载荷平稳且高于泡沫铝填充管,比泡沫铝填充管体现出更优异的高过载防护性能。等质量的泡沫夹芯管的抗冲击性能优于填充管,2种泡沫填充而成的夹芯管具有相似的高过载防护性能,泡沫材料压缩行为对夹芯管压溃载荷特征的影响低于填充管。结论 所得结果对轻质元器件的高g值缓冲防护有较强的指导意义。     

基于均方误差修正的渗碳12CrNi3钢疲劳强度预测模型

为对渗碳12CrNi₃合金钢提供一种安全可靠的高周疲劳强度预测方法,本文利用高频疲劳测试机,在10⁴～10⁸循环周次内对渗碳12CrNi₃合金钢疲劳试样开展应力比为-1的高周疲劳实验。采用灰色估计法对渗碳12CrNi₃钢疲劳数据的威布尔W(α,β,γ)参数进行评估,并通过估算的威布尔参数,进一步确定渗碳12CrNi₃合金钢强度预测模型的参数。考虑应力-寿命关系,结合疲劳数据的威布尔分布规律,建立了失效概率为1%时的渗碳12CrNi₃合金钢高周疲劳强度预测模型。从预测疲劳强度安全可靠性出发,通过均方误差(MSE)方法对模型进行修正,并将修正后模型99%MSE下界线与渗碳12CrNi₃合金钢疲劳寿命P-S-N曲线(不同存活率P的S-N曲线)进行对比,研究发现12CrNi₃合金钢疲劳寿命数据点均位于模型99%MSE下界线内,预测结果很好。研究表明,在以渗碳12CrNi₃合金钢...     

从测角后交最佳点位规律看规范规定之不足

本文分析了文献[1]所总结的测角后交两类公式在取得最佳点位时的规律,指出当前规范对此问题规定之不足.     

基于频谱分析的Procrustes统计步态识别算法

在信息安全越加重要的现代社会,步态识别以其特有的优势作为一种身份识别手段,得到了很多关注.提出一种基于Procrustes均值形状的傅里叶频谱分析(FSAOPMS)的适用于多视角的步态识别方法.利用Procrustes统计形状分析方法将步态序列中人体轮廓的连续步态变化表示成一个紧致的Procrustes均值形状(PMS...     

多中继通信系统中量化误差补偿的MMSE中继预编码方案

为了补偿量化误差造成的系统性能的降级,文中提出将Grassmannian码本量化误差的统计特性引入多中继系统有限反馈中继预编码的设计,首先在QoS要求下的完全反馈中继预编码与接收均衡MMSE两步优化算法的基础上,给出了有限反馈下量化误差补偿的中继预编码与接收均衡MMSE两步优化方案,进而文中在中继和功率约束下,提出了一种有限反馈下量化误差补偿的中继预编码与接收均衡MMSE联合优化方案,并给出了中继预编码矩阵设计的闭合表达式.计算机仿真表明,文中提出的中继和功率约束下量化误差补偿的MMSE联合优化方案的系统性能优于QoS要求下量化误差补偿的MMSE两步优化方案,而且相比没有采用量化误差补偿的MMSE联合优化方案可以获得更低的误码率性能.     

New Riemannian techniques for directional and tensorial image data

This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton's method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona-Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo's structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images.