Improved method of estimating the product quality after multiple inspections

A complex product is often inspected more than once in a sequential manner to ensure the product’s quality. Based on the number of defects discovered during each round of inspection process, we can estimate the number of defects still remaining in the product. For each defect, the probability that the defect will be detected during each inspection cycle is usually assumed to be a known ‘constant’. However, in many practical situations, some defects are easily detected, while others are much more difficult to identify. In this paper, we propose a ‘beta-geometric’ inspection model in which the heterogeneity in detection probability is described by a beta distribution. In a numerical study, we show that our more realistic inspection model clearly outperforms traditional estimation methods that are based on the assumption of a constant detection probability.     

Error estimation for the automated multi‐level substructuring method

In this study, we propose an effective method to estimate the reliability of finite element models reduced by the automated multi‐level substructuring (AMLS) method. The proposed error estimation method can accurately predict relative eigenvalue errors in reduced finite element models. A new, enhanced transformation matrix for the AMLS method is derived from the original transformation matrix by properly considering the contribution of residual substructural modes. The enhanced transformation matrix is an important prerequisite to develop the error estimation method. Adopting the basic concept of the error estimation method recently developed for the Craig–Bampton method, an error estimation method is developed for the AMLS method. Through various numerical examples, we demonstrate the accuracy of the proposed error estimation method and explore its computational efficiency. Copyright © 2015 John Wiley & Sons, Ltd.     

Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model

Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

A short note on the effect of sample size on the estimation error in Cp

Process capability indices such as C_p are used extensively in manufacturing industries to assess processes in order to decide about purchasing. In practice, the parameter for calculating C_p is rarely known and is frequently replaced with estimates from an in-control reference sample. This article explores the optimal sample size required to achieve a desired error of estimation using absolute percentage error of different C_p estimates. Moreover, some practical tools are created to allow practitioners to find sample size in different situations.     

平面声场中四传声器阵列理论精度的对比分析

主要探讨了两种四传声器阵列的理论精度,针对相应的传声器布置形式进行数值计算,比较了正四面体和直角四面体传声器探头在不同工况下的幅值误差和方向误差。结果表明,在平面波作用下,1~4 k Hz范围内正四面体传声器的幅值误差小于直角四传声器,且两者均小于1 d B,外接球心位置的幅值精度在高频段优势明显;当频率小于5 k Hz时,方向误差均控制在2°以内,因此可以满足相关的建筑声学测试要求。     

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。     

对无临近点信息干涉条纹的参数估计

用广义radon变换可以从运动目标产生的干涉条纹中估计出目标的运动参数和波导不变量。但是当干涉条纹中没有出现临近点时,用此方法估计不出真实值。为了解决此问题,提出了一种新的估计方法。主要过程是先从时频图中提取出干涉条纹轨迹,再去搜索与此轨迹误差最小的最优曲线,此曲线的参数值代表目标的运动参数和波导不变量。仿真和海试结果表明,在干涉条纹无临近点信息情况下,用广义radon变换估计的参数值与真实值偏差很大,但是文中提出的方法仍能估计出较准确的结果。     

RME:基于滑动窗口的实时人脸检测算法

研究了基于滑动窗口的视频实时人脸检测,提出了刚性运动估计(RME)算法.该算法以小尺度人脸瞬时刚性运动为假设,根据几何变换对窗口图像的运动进行描述,以光流代替运动矢量计算运动参数进而识别窗口图像的刚性、非刚性运动类型,通过排除非刚性窗口以提高人脸检测效率.对比实验与分析表明,该算法在准确率与时间效率方面具有优势.