城市蔬菜产品品种污染监控系统的研究及其应用

蔬菜产品中的农药残留问题一直为市民所关心,为了让市民了解日常消费的蔬菜产品品种的质量状况和污染水平,本文结合深圳市布吉、福田两大农产品批发市场2003年1~4月份的监测抽样数据,应用主成分分析及Hotelling统计量等多元统计方法,结合SAS系统与Delphi系统建立城市蔬菜产品品种污染监控系统。该监控系统不仅可以为市场提供及时的蔬菜产品品种质量状况和污染水平的信息,而且对于及时监控流入市场的蔬菜质量、保障市民身体健康、引导市场消费等都具有重要意义。     

可变抽样区间的单边控制图

利用质量控制图监督生产过程时 ,通常每隔固定时间从过程抽取固定容量的样本。本文在前文[1] 的基础上设计具有可变抽样区间的单边标准差 (S)图、极差 (R)图和不合格品数 (np)图。计算了这三个图发信号前的平均样本数和平均时间 ,并同固定抽样区间的常规控制图作比较。所设计的控制图能缩短过程失控时间从而减少不合格品数。     

基于可视化图形特征的入侵检测方法

入侵检测是保障网络安全的重要措施,网络攻击手段的多样性和隐蔽性不断增强导致入侵检测愈加困难,迫切需要研究新的入侵检测方法。结合可视化技术和k近邻分类算法,提出一种基于图形特征的入侵检测方法。采用信息增益方法对原始特征进行排序选择,并进行雷达图可视化表示,提取雷达图的图形特征构成新的数据集并送入k近邻分类器进行训练和测试。通过KDDCUP99数据集仿真实验表明,该方法不仅能直观显示攻击行为,而且获得较好的攻击检测性能,对DOS攻击的检测率可达97.9%,误报率为1.5%。     

Multi-array iterative receiver for underwater acoustic OFDM communications with EXIT chart evaluation

In this paper, a multi-array iterative receiver based on log-likelihood ratio (LLR)-combining detection involving joint sparse channel estimation and decoding is proposed for underwater acoustic OFDM communication. First, Extrinsic information transfer (EXIT) chart analysis is applied to evaluate the convergence behavior of the iterative receiver using the real data collected from the Kauai Acomms MURI 2011 (KAM11) experiment. This experiment was conducted in about 106 m-depth shallow water west of Kauai, HI, in June 2011, with a 20 kHz bandwidth (12–32 kHz) at range up to 3 km. It helps to explain the impact of different data configurations, detectors, and the diversity combinations in a highly inhomogeneous underwater environment and to predict the bit-error rate (BER) performance of the proposed receiver. Then the BERs as a function of the number of combined elements are illustrated to verify the prediction and analysis via the EXIT chart. Data transmission using 16QAM modulation achieves a BER of 10⁻⁴ at a data rate of 21 kb/s. The results provide guidance for the design of system parameters including the data configurations, the number of iterations for both iterative processing and low density parity check (LDPC) decoding, which are beneficial to achieve a good efficiency-performance tradeoff.     

Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis

Email: mgt.liu{at}utoronto.ca Email: makis{at}mie.utoronto.ca Received on 4 August 2006. Accepted on 14 December 2006. An effective gearbox failure diagnosis helps prevent catastrophicgearbox failure and can contribute to significant economic benefits.This paper proposes a gear failure diagnosis method based onvector autoregressive modelling of high-frequency vibrationdata, dimensionality reduction applying dynamic principal componentanalysis (PCA) and condition monitoring using a multivariatecontrol chart. After extracting useful information from thevibration data obtained from distinct directions via dynamicPCA, a failure diagnosis scheme is implemented and tested usingreal gearbox vibration data. It is shown that the failure diagnosisscheme can indicate the gear teeth failure pattern when thegear is damaged, which has not been demonstrated in the previousstudies. For a comparison, PCA is applied to the same data set.The results show that the advantages of dynamic PCA over PCAfor failure diagnosis using vibration data consist not onlyin indicating more accurately the occurrence of incipient faultand the actual gear condition, but also in a much lower falsealarm rate.     

可变抽样区间的二项EWMA控制图

在制造过程中,对产品的不合格品数进行监控时,通常选用计数性控制图-np图,它是基于过程服从二项分布建立的,一般对于过程中出现的较大波动效果明显。为了提高控制图对不合格品数较小波动的监控效果,本文设计了产品不合格品数服从二项分布的EWMA控制图。提出可变抽样区间的二项EWMA控制图,并采用马可夫链法计算其平均报警时间。对固定抽样区间以及可变抽样区间二项EWMA控制图对比研究,表明当过程失控时,可变抽样区间二项EWMA控制图具有较小的失控平均报警时间,能够迅速监测出过程中的异常波动,明显优于固定抽样区间的二项EWMA控制图。     

An evaluation of the multivariate dispersion charts with estimated parameters under non‐normality

Various charts such as |S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in‐control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non‐parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non‐parametric bootstrap multivariate control charts |S|, W, and G are introduced, and their performances are compared against Shewhart‐type control charts. The proposed method is based on bootstrapping the data used for estimating the in‐control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study.     

Optimal inspection policy for three-state systems monitored by control charts

This paper presents the formulations of the expected long-run cost per time unit for a system monitored by a static control chart and by an adaptive control chart respectively. The static chart has a fixed sampling interval and a fixed sample size. The adaptive chart has a fixed sample size but variable sampling intervals. The system is supposed to have three states, normal working state, failure delay time state, and failed state. Two levels of repair are used to maintain the system. A minor repair is used to restore the system if a detectable defect is confirmed by an inspection. A major repair will be performed if the system fails. The expected cost per time unit for maintaining such a system is obtained. The objective of such analysis is to find an optimal sampling policy for the inspection process. An artificially generated data example and a real data example are used to compare the expected cost per time unit for both the static and adaptive control charts.     

“高分子物理”图表教学法初探

图表教学法是高分子物理传统教学法的有益补充。利用图表高分子物理繁杂而相互联系的理论体系有机结合,强化学生对知识的掌握。试卷分析证明,图表教学法可以提高高分子物理课程教学效果。最后,提出进一步完善高分子图表教学法的一些设想。     

控制图法对底排药剂组分结果的探讨

针对烟火剂型底排药剂组分分析结果与生产投料配比存在较大的差异,使用均值-极差(X)-R控制图法对结果数据进行统计分析,判定结果所处状态.结合药剂特性及生产工艺、取样情况、分析方法实际状况,对统计分析结果展开讨论,查找均值失控的原因,采取工艺控制、方法改进等措施,从而利用分析数据实现有效的质量控制,确保产品质量满足要求.