Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line

Product recovery involves the recovery of materials and components from returned or end-of-life products. Disassembly, an element of product recovery, is the systematic separation of an assembly into its components, subassemblies or other groupings. Stricter environmental regulations together with dramatic decrease in natural resources and landfills have increased the importance of disassembly as all product recovery options require some level of disassembly. Due to changes made during the lifetime of a product by customers or service personnel, the number and the version of components prior to disassembly is unknown. Customers may also discriminate between and demand different versions of components. The existence of non-functional components further adds to the uncertainty associated with disassembly yield. Sensors implanted into products during their production can address this uncertainty by providing information on the number, condition and version of components prior to disassembly. In this study, we evaluate the impact of sensor embedded products (SEPs) on the various performance measures of a washing machine (WM) disassembly line controlled by a multi-kanban system, which takes into consideration the highly stochastic behavior of the line while managing material and kanban flows. First, separate design of experiments studies based on orthogonal arrays are performed for conventional products and SEPs. In order to observe the response of each experiment, detailed discrete event simulation (DES) models for both types of products are developed considering the precedence relationships among the components of a WM. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. According to the results, SEPs provide significant reductions in all costs (viz., backorder, holding, disassembly, disposal, testing and transportation) while increasing revenue and profit.     

Attribute Charts for Monitoring the Mean Vector of Bivariate Processes

This article proposes two Shewhart charts, denoted np_xy and np_w charts, which use attribute inspection to control the mean vector (μ_x; μ_y)′ of bivariate processes. The units of the sample are classified as first‐class, second‐class, or third‐class units, according to discriminate limits and the values of their two quality characteristics, X and Y. When the np_xy chart is in use, the monitoring statistic is M = N₁ + N₂, where N₁ and N₂ are the number of sample units with a second‐class and third‐class classification, respectively. When the np_w chart is in use, the monitoring statistic is W = N₁ + 2N₂. We assume that the quality characteristics X and Y follow a bivariate normal distribution and that the assignable cause shifts the mean vector without changing the covariance matrix. In general, the synthetic np_xy and np_w charts require twice larger samples to outperform the T² chart. Copyright © 2014 John Wiley & Sons, Ltd.     

自然环境下果实的自动识别技术

利用计算机视觉技术可以识别自然环境下植株上的果实,为自动化采摘提供可能。常用的果实识别方法都具有一定的局限性。以番茄为研究对象,提出基于区域特征的果实自动识别方法,该方法克服了常规方法的局限性,具有一定的通用性和实用价值。     

基于鉴别稀疏保持嵌入的高光谱影像地物分类

在高光谱影像地物分类应用中时常因光谱波段数多而导致"维数灾难"问题,提出了一种鉴别稀疏保持嵌入的维数约简算法。该方法利用稀疏表示的自然鉴别力,分别构建了类内e_1图和类间e_1图;在低维嵌入空间中,保持同类数据的内在稀疏流形结构,同时分离开非同类数据,提取出鉴别特征。DSPE不仅继承了稀疏表示的优点,而且增加了非同类数据间的可分性。在PaviaU和Urban高光谱数据集上的地物分类实验结果表明,该方法的总体分类精度分别提高到87.53%和80.49%。提出的方法能自适应地揭示出数据间的内在关系,更有效地提取出鉴别特征,改善地物分类精度。     

新歼HUD画面练习软件的效度研究

通过平视显示器（HUD）使用经验不同的30名飞行员应用HUD画面练习软件成绩的比较,检验软件的效度。结果表明：三组被试第一次成绩比较显示,有新歼HUD使用经验组与其它两组比较有统计学意义,存在显著性差异（F=20.44,21.50,P〈0.05）。但无HUD使用经验的两组比较无明显差异（F=1.06,P〉0.05）。6次比较的重复测量结果显示,有新歼HUD使用经验组与无HUD使用经验普通练习强度组有统计学意义,存在显著性差异（F=13.32,P〈0.05）。无HUD使用经验加大练习强度组与有HUD使用经验组和无HUD使用经验普通练习强度组比较无明显差异（F=7.24,6.08,P〉0.05）。对三组被试自身对照的重复测量结果进行分析,各组被试的第一次练习成绩与第五、六次成绩均存在明显差异（F=-12.71,-13.39,-34.07,-35.13,-26.04,-27.83,P〈0.01）。结论：新歼HUD画面练习软件对于不同经验背景的被试的区分效度较好,能在一定程度上提高被试HUD画面的练习成绩,有助于提升飞行员HUD认读的能力。     

非线性判别函数的基于MDL标准的勒让德多项式构造法

1.引言在模式识别中,分类决策问题是最基础也是最重要的内容,所谓分类决策就是根据被识别对象特征的观察值将其分到某个类别中去,其基本作法是在样本训练集基础上确定某个判决规则(即样本特征空间的一个函数,也称为判别函数或分类器),使按这种判决规则对被识别对象进行分类所造成的错误率最小或引起的损失最小。当样本分布是正态分布时,从最小错误率来说二次分类器是最优分类器。此外,作为一种特殊情况,如果这些分布有相同的协方差矩阵,那么线性分类器就是最优的。然而这些假定在很多实际问题中并不成立,因此线性或二次分类器对     

ON THE CONNECTION BETWEEN THE COMPLEXITY AND CREDIBILITY OF INFERRED MODELS

Abstract

The connection between the simplicity of scientific theories and the credence attributed to their predictions seems to permeate the practice of scientific discovery. When a scientist succeeds in explaining a set of nobservations using a model Mof complexity c then it is generally believed that the likelihood of finding another explanatory model with similar complexity but leading to opposite predictions decreases with increasing nand decreasing c. This paper derives formal relationships between n, c and the probability of ambiguous predictions by examining three modeling languages under binary classification tasks: perceptrons, Boolean formulae, and Boolean networks. Bounds are also derived for the probability of error associated with the policy of accepting only models of complexity not exceeding c. Human tendency to regard the simpler as the more trustworthy is given a qualified justification.     

基于双光纤光栅温度压力同时区分测量的研究

提出了一种基于薄壁圆柱壳体的压力温度同时区分测量的光纤Bragg光栅(FBG)传感器结构。将双FBG沿着轴向分别粘贴在壁厚度不均匀的柱体外表面,由于两个FBG受到温度影响而引起的波长漂移量是相同的,这时两光栅Bragg波长漂移量之差就完全取决于压力,从而实现对压力温度的区分测量。实验测得,在0～7MPa内传感器的压力响应灵敏度为0.073nm/MPa,在22.6～112.6℃内的温度灵敏度为0.037nm/℃,分别是裸FBG的24倍和3.7倍。结果也表明,这种传感器具有良好的线性度与可重复性。     

干涉仪测向技术现状与发展研究

从解模糊和天线排列形式、鉴相技术、通道幅相不一致校正方法和接收数据处理方式四个方面论述了干涉仪测向技术的现状和发展趋势。对每一方面现存的不同实现方法的主要原理和优劣性做了详细说明和比较,探讨了下一步的发展和改进思路。为干涉仪测向装备在特定条件下应用技术的选择提供理论依据,为新装备的研制提供参考意见。     

用物元分析法判别膨胀土胀缩等级

将膨胀土胀缩等级、判别指标及其特征值作为物元,根据膨胀土分级标准建立关联函数,提出用物元分析判别膨胀土胀缩等级的新方法。结合实例与灰色聚类法和模糊评价模型得到的结果进行了对比。