The non-invasive identification of heroin covered by simulation skin based on coherence function algorithm and wavelet

As body-packing is a well-known means of illicit drug carriage across international borders, the goal of our study is to provide a new method for the detection of body-packing (ingested drug packets) based on energy dispersive X-ray diffraction (EDXRD) and algorithms. Packets containing heroin (purity > 90%) were introduced into the experimental body in which pork was used as simulation body. The samples were repeatedly scanned by energy dispersive X-ray diffraction spectrometer, and then, threshold wavelet and coherence function were used to analyze the obtained EDXRD spectra. In our investigation, two criteria were used for the identification of heroin in pork. One was that matching the diffraction profile of unknown material and samples via coherence function which was on the basis of setting-up samples database of heroin in pork. The other was that matching the positions and shapes of heroin diffraction peaks. On the basis of the experimental tests and data analysis, it is indicated that the presented methods are effective and reasonable for the detection of heroin in body.     

Feature extraction for identification of drug and explosive concealed by body packing based on positive matrix factorization

In the analysis towards the energy dispersive X-ray diffraction (EDXRD) spectra of drug and explosive concealed by body packing, positive matrix factorization (PMF) was introduced to extract features from EDXRD spectra of samples in a set of drugs and explosive concealed in the anthropomorphic phantom, because PMF prevents the negative factors from occurring, avoids contradicting physical reality, and makes factors more easily interpretable. In order to compare with the features extracted by PMF, Principal Component Analysis (PCA) and robust PCA were investigated. Then, K-nearest neighbour (KNN) and support vector machine (SVM) were introduced to classify the samples according to the features extracted by PMF, PCA and robust PCA. It is shown that the recognition rates obtained by PMF are highest (above 99.5%) and insensitive to classifiers. This work demonstrates that PMF is effective in feature extraction for identification of drug and explosive concealed by body packing.     

基于非线性主成分分析的车身制造偏差源诊断

对主成分分析在轿车车身制造偏差源诊断方面的不足进行了分析,进而在其基础上提出采用非线性主成分分析,从而更有效、更准确地来对轿车车身制造偏差源进行诊断,并给出了计算实例。实例证明该方法优于主成分分析,并在工程上有较大的可用性。     

Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Immunohistochemistry section staining is not always easy to interpret. Manual quantification of immunohistochemical staining is limited by the observer visual ability to detect changes in level staining. Hence, the quantification of immunostaining by means of digital image analysis allows us to measure accurately protein expression percentages in immunobiological stained tissues and ensures to overcome the visual limitations. We perform an experimental study to analyse the impact of folic acid (FA) deficiency into collagen IV and laminin‐1 expression in the embryonic mouse lens. The study starts with microscope images of embryos mouse lens whose mothers fed a diet deficient in FA during 2 and 8 weeks. A principal component analysis (PCA) image processing is used to analyse these images coming from control and FA deficit groups. The method permits to define an index of over‐ or infraexpression of collagen IV and laminin‐1 associated to different spatial organisation structures (PC processes). Additionally, it permits to determine in precise percentage the exact quantity of the overexpression or infraexpression and finally to comprehend molecular regionalisation and expression in both control and deficient groups. The results suggest that even with 2 weeks of deficit of FA the expression and distribution of both molecules is affected.     

Modified multivariate process capability index using principal component analysis

The existing research of process capability indices of multiple quality characteristics mainly focuses on nonconforming of process output, the concept development of tmivariate process capability indices, quality loss function and various comprehensive evaluation methods. The multivariate complexity increases the computation difficulty of multivariate process capability indices（MPCI）, which makes them hard to be used in practice. In this paper, a new PCA-based MPCI approach is proposed to assess the production capability of the processes that involve multiple product quality characteristics. This approach first transforms the original quality variables into standardized normal variables. MPCI measures are then provided based on the Taam index. Moreover, the statistical properties of these MPCIs, such as confidence intervals and lower confidence bound, are given to let the practitioners understand the capability indices as random variables instead of deterministic variables. A real manufacturing data set and a synthetic data set are used to demonstrate the effectiveness of the proposed method. An implementation procedure is also provided for quality engineers to apply our MPCI approach in their manufacturing processes. The case studies demonstrate the effectiveness and feasibility of this new kind of MPCI, which is easier to be used in production practice. The proposed research provides a novel approach of MPCI calculation.     

改进MKPCA方法及其在发酵过程监控中的应用

针对间歇发酵过程缓慢时变和非线性等特点,提出一种基于滑动窗技术的多向核主元分析(MWMKPCA)方法.该方法结合了核主元分析(KPCA)和滑动窗口技术的优点,其中KPCA能有效解决过程数据的非线性问题,保证数据信息抽取的完整性;而滑动窗口技术能有效避免MKPCA在线应用时预报未来测量值所引入的误差,提高监控性能.对于已判断正常的新批次过程数据,将其加入模型参考数据库进行更新,从而提高间歇过程性能检测的准确性.将该方法应用到工业青霉素发酵过程的监控中,并与MPCA、MKPCA方法的监测性能进行了比较.结果表明:该方法能有效提取过程变量间的非线性关系,降低运行过程的误报率,对缓慢时变的间歇过程具有更可靠的检测性能.     

Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysis coupled with principal component analysis

This paper investigates optimization problem of the cutting parameters in high-speed milling on NAK80 mold steel. An experiment based on the technology of Taguchi is performed. The objective is to establish a correlation among spindle speed, feed per tooth and depth of cut to the three directions of cutting force in the milling process. In this study, the optimum cutting parameters are obtained by the grey relational analysis. Moreover, the principal component analysis is applied to evaluate the weights so that their relative significance can be described properly and objectively. The results of experiments show that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters and the proposed approach can be a useful tool to reduce the cutting force.     

基于主成分分析的直线运动模糊参数估计

为了快速准确地估计直线运动模糊图像中的模糊参数,分析了直线运动模糊参数的长度和方向在频率域图像和倒谱图像中的表现形式,提出了一种基于主成分分析的运动模糊参数估计方法.首先,基于高斯建模对模糊图像中的倒谱图像进行二值化分割,得到倒谱图像中的亮线区域.然后,基于主成分分析提取亮线区域的主成分分量,主成分分量的方向即为模糊角度;依据估计的模糊角度,计算模糊图像傅里叶频率域图像相应角度的Radon变换,进行滤波去“毛刺”处理.最后,通过计算极小值之间的间距,估计模糊长度.实验结果表明:估计的模糊角度和模糊长度平均误差分别为0.138 4°和0.273 9 pixel,在同精度条件下,速度是传统的Radon变换方法的10倍左右,表明该方法能快速、准确地估计直线运动模糊参数.     

Using a principal components analysis for developing a robust design of electron beam welding

This paper presents the application of principal components analysis for Taguchi orthogonal experiments to develop a robust electron beam welding treatment (EBWT) process with high efficiency multiple performance characteristics (MPCs). In this study, the principal components analysis (PCA) design incorporating the correlation matrix of tested trials is employed. In the first step, the MPCs are reduced to two independent components using PCA. Both components accounted for 98.8% of the total variance. The first principal component (PC), which refers to the integrated hardening capability index of the EBWT process, accounts for 70.7% of the total variation. The remaining 28.1% were contributed by the second PC, which can be interpreted as the penetration capability index. In the second step, we identify the most important PC loading vectors using PCA, and estimate the importance of the PCs. By using PCA, relationships between different MPCs can be investigated and the most important factors for the variance of the EBWT process can be identified. The experimental results show that redundant information could be eliminated by using principal components in conjunction with Taguchi’s orthogonal array experiments. This proposed approach is simple, effective, and efficient for developing a robust and high-efficiency EBWT process of high quality. In this study, the MPCs in the EBWT process are successfully optimized .     

Support vector machines combined with wavelet-based feature extraction for identification of drugs hidden in anthropomorphic phantom

A new recognition method of Support Vector Machines (SVMs) combined with wavelet-based feature extraction is proposed for identifying drugs hidden in human body. Preliminary data sets of eight kinds of samples are acquired by a home-built instrument using energy-dispersive X-ray diffraction (EDXRD) technology in a short detection time. Small sample size, poor signal-to-noise ratio (SNR) and high dimension of data make drugs identification a challenging problem. In this paper, the potential effective method solves the problem well. The spectral signal with poor SNR is obtained and processed with wavelet for feature extraction and then the wavelet coefficients are used as the inputs of SVMs. A multi-classifier of SVMs based on binary tree architecture (SVMs-BAT) is trained. The method of SVMs-BAT combined with wavelet-based feature extraction (WSVMs-BAT) is firstly compared with two methods: one is single SVMs-BAT which uses original data as inputs without preprocessing, the other is SVMs-BAT combined with feature extraction based on principal component analysis (PCA-SVMs-BAT). The high identification accuracy of WSVMs-BAT indicates that the method of feature extraction using wavelet can effectively represent the original data better. Then the recognition result of the proposed method is also compared with artificial neural network (ANN) and K-nearest neighbor (KNN) methods. Our findings show that the proposed method combined with EDXRD technology provides a good access to achieve the aim of automatic identification of illicit drugs.     

基于主元分析确定多变量系统控制极限的方法

针对存在多个稳定状态的多变量统计过程控制的控制极限问题,提出先分析系统变量的历史数据得到其经验分布,确定该变量的不同稳定状态及对应的状态域;将数据样本按不同的状态分组标准化后,分别进行主元分析,得到不同稳定状态下的Hotellings T2及平方预测误差SPE（Q）控制极限。用本方法和普通主元分析法对某钢铁公司局部蒸汽管网的流量数据模拟监控的效果进行对比,表明本方法由于区分不同状态,确定的Hotellings T2及平方预测误差SPE（Q）控制极限更精确,能有效降低漏报警和误报警的概率。     

能谱定量分析的不确定度评估

本文详细分析了能谱(EDS)定量分析过程中不确定度的来源,探讨随机变量、激发电流大小、工作距离、电子束焦距、取样点大小、电流值的可读性和电流稳定性以及ZAF因子等能谱参数对测量结果不确定度的影响。结果显示电流大小和工作距离是影响有标定量分析结果准确性的关键参数。EDS对钛非晶合金样品中Zr、Ti、Ni和Cu元素的有标定量分析结果符合定量分析要求,各元素相对扩展不确定度分别为4.68%、4.26%、5.04%和5.02%(包含因子k=2)。     

利用ANSYS软件对数控万能铣头主轴的应力分析

利用ANSYS软件对数控万能铣头主轴进行数值模拟分析,求出其应力分布状况,找出危险区域,为主轴的设计和失效分析提供了详实可靠的理论依据。     

基于多模态数据全信息的概率主成分分析故障检测研究

针对工业过程复杂的数据分布特性,本文提出了一种基于局部近邻标准化(LNS)的概率主成分分析(PPCA)故障检测方法(LNSPPCA)来解决由于过程数据的多模态特性和不确定性所引起的故障检测效果不理想问题.首先,通过LNS解决数据多模态问题,使标准化后数据尽可能的服从单一高斯分布,然后,使用PPCA方法从概率的角度对数据...     

Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution

Optimizing multi-response problems has become an increasingly relevant issue when more than one correlated product quality characteristic must be assessed simultaneously in a complicated manufacturing process. This study proposes a novel optimization procedure for multiple responses based on Taguchi’s parameter design. The signal-to-noise (SN) ratio is initially used to assess the performance of each response. Principal component analysis (PCA) is then conducted on the SN values to obtain a set of uncorrelated components. The optimization direction for each component is determined based on the corresponding variation mode chart. Finally, the relative closeness to the ideal solution resulting from the technique for order preference by similarity to ideal solution (TOPSIS) is determined as an overall performance index (OPI) for multiple responses. Engineers can easily employ the proposed procedure to obtain the optimal factor/level combination for multiple responses. A case study involving optimization of the chemical-mechanical polishing of copper (Cu-CMP) thin films from an integrated circuit manufacturer in Taiwan is also presented to demonstrate the effectiveness of the proposed procedure.     

紫外可见光谱法和主成分分析对蓝色圆珠笔油墨分类的研究

采用薄层色谱法和紫外可见光谱法分析34支蓝色圆珠笔油墨。截取400～700nm之间的紫外可见光谱数据进行主成分分析。主成分分析表明主成分1、2和3的累积贡献率已达96.5%,以主成分1、2对所有样品的得分值作图,对不同品牌的圆珠笔有很好的聚类作用。将主成分分析的分类结果同薄层色谱法的分类结果相比较,结果表明,采用主成分分析技术可以达到对蓝色圆珠笔油墨字迹的良好识别。     

Identification of fat,protein matrix,and water/starch on microscopy images of sausages by a principal component analysis-based segmentation scheme

A color-based segmentation scheme applied to microscopy images of cryosectioned sausages is proposed. The segmentation scheme is capable of segmenting three different levels on the microscopy images: the fat particles, the protein matrix, and water/starch. The method is based on principal component analysis. A user-friendly program was developed for the manual segmentation of a selection of image pixels by microscopists. Principal component models based on the manually classified pixels are then used to segment fat, protein matrix, and starch/water on microscopy images. The program can also be used as a training tool for microscopists.     

Differentiation of human hair by colour and diameter using light microscopy,digital imaging and statistical analysis

This research introduces and evaluates a novel method that offers the potential of providing objective criteria to forensic microscopical hair comparisons. The method combines hair diameter with numeric characterisations of red, green, and blue colour content as determined with the use of digital imaging at defined locations of the hair. Thirty hairs were collected from each of twenty participants, all with naturally coloured brown hair. The hairs were examined with an Olympus BX53® polarising light microscope and digital images were viewed with an Olympus DP72® camera under 400× magnification. Using Olympus cellSens^? Entry software, hair diameter was measured at 1000, 1500 and 2000 m from the base of the root. The Olympus cellSens^? Entry software uses a red, green and blue (RGB) colour model to quantitatively define the colour of each pixel on an image based on its composition of these three principal colour components. This software was used to collect numerical characterisations of hair colour at each distance interval. The diameter and colour values for each hair were compared using discriminant analysis (DA) and principal component analysis. Although a large amount of intrapersonal variation was observed, the degree of interpersonal variation was greater and enabled the statistical model to differentiate between the hair samples from each participant. The DA model achieved sample reclassification with an error rate of 7.33%. A validation study was conducted on a subset of hair samples from which 18 of the 20 were correctly assigned to the participant from whom they originated. These results support the potential of this method to provide an objective addition to current microscopical hair comparison practices.     

Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model

The application of parameter design methodology has been considerable in recent years to make system performance robust over a wide range of input conditions. This notion has been referred to as a robust design with dynamic characteristics. Due to product complexity, multiple correlated characteristics must be simultaneously evaluated for improving product quality. Dynamic multi-response optimization is becoming an important issue to contemporary industry. This study developed a novel procedure of optimizing dynamic multi-responses using principal component analysis (PCA) and multiple criteria evaluation of the grey relation model. PCA can consider the correlations among multiple quality characteristics to obtain uncorrelated components. These components are then substituted into multiple criteria evaluation of the grey relation model to determine the optimal factor level combination. A case study demonstrates the effectiveness of the proposed procedure for optimizing dynamic multi-response processes.     

紫外可见主成分分析法对蓝色签字笔油墨的分类研究

本文采用薄层色谱法和紫外可见光谱法分析33支蓝色签字笔油墨。截取400～700 nm之间的紫外可见光谱数据进行主成分分析。主成分(PC)分析结果表明主成分1、2和3的累积贡献率已达86.84%,以主成分1、2对所有样品的得分值作图,结果显示时不同品牌的蓝色签字笔有很好的聚类作用。将主成分分析的分类结果同薄层色谱法的分类结果相比较,结果表明,采用主成分分析技术可以达到对蓝色签字笔墨字迹的良好识别。