DISTRIBUTION OF PRIORITY POLYCYCLIC AROMATIC HYDROCARBONS IN THE SEDIMENT OF LAKE BALATON,HUNGARY

The determination of 13 PAH pollutants was carried out on sediment samples collected at 27 sites at the Lake Balaton, Hungary. The aim was to investigate the distribution patterns of PAHs and the correlation of source-sink relationship. Sediment samples were collected from the upper 10 cm and from 20 to 70 cm depth. The dry mass ratio of the fine grain-size fraction (<0.063 mm) and the coarse sand sediments (0.063–500 mm) were analyzed. Principal component analysis (PCA) was performed on the PAH compositional data for 110 samples to estimate the distribution of PAHs in different compartments. The average concentration of PAHs was found as 132 μg/kg dry weight (11–1734 μg/kg) for all sites and depth. Considering the harbors, at some sites, 930–950 μg/kg of total PAHs were obtained. The ratio of phenantrene/anthracene (PHE/AN) and fluoranthene/pyrene (FA/PY) indicated that most of the samples showed pyrogenic origin. It can be established that the upper 10 cm of the sediment is significantly more polluted than the deeper layers. The interim sediment quality guideline (ISQG) values and the probable effect level (PEL) were used to compare our findings with other data. No concentrations of PAHs were found higher than either ISQG and PEL values of samples collected inside of the lake, so the sediment has not been associated with adverse biological effects. However, the maximum concentrations of 7 out of 9 PAH compounds found in samples of harbors were higher than ISOG values but lower than PELs. Analysis of the harbor sediments revealed an elevated amount of contamination probably derived from the fuel of ships.     

基于MPCA-MDPLS的间歇过程的故障诊断

针对间歇过程的故障诊断问题,提出了一种新的混合模型方法——MPCA-MDPLS.这种方法包括两个模型：多向主元分析(MPCA)模型和多向判别部分最小二乘(MDPLS)模型.这两个模型的建模数据不仅包括正常工况的数据,而且还包含了各种已知故障数据.因此,MPCA模型具有检测未知故障的能力.给出了MDPLS模型故障诊断限,对经MPCA模型检测不是未知故障的故障做进一步诊断.如果故障是未知的,可以采取其他的方法来分析新的故障,并按不同类别存入到数据库中.当多次出现这种故障之后（一般≥5次）,把新的故障数据加入到建模数据中,并重新建立MPCA-MDPLS模型.通过对实际工业链霉素发酵过程数据的分析,表明了提出的算法是可行的、有效的,并具有识别未知新故障的能力.     

主元空间中的故障分离与识别方法

主元分析 (PCA)作为数据驱动的一种统计建模方法,在化工产品质量控制与故障诊断方面得到广泛研究和应用.在故障重构技术的基础上,研究了基于T²统计量的故障分离和识别问题,分别获得了主元空间中故障可分离和识别的理论条件.以双效蒸发过程为例,对该生产过程中的10种不同故障进行仿真监测分析,证实了所获理论结果的有效性.     

基于多尺度分析与主元分析的聚丙烯熔融指数神经元软测量预报模型

Prediction of melt index （MI）, the most important parameter in determining the product＇s grade and quality control of polypropylene produced in practical industrial processes, is studied. A novel soft-sensor model with principal component analysis （PCA）, radial basis function （RBF） networks, and multi-scale analysis （MSA） is proposed to infer the MI of manufactured products from real process variables, where PCA is carried out to select the most relevant process features and to eliminate the correlations of the input variables, MSA is introduced to a~quire much more information and to reduce the uncertainty of the system, and RBF networks are used to characterize the nonlinearity of the process. The research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in propylene polymerization processes.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.     

新型节能墙体的综合评价

本文采用主成份分析法的数学模型对我省设计建造的4类11种新型节能墙体的多变量的实测数据进行分析运算,选取可信度大于85%的综合效益目标值,依此排序,并提出了评价与分类的参考建议。     

惯性力系简化探讨

本文给出了任意质点系的惯性力系向任一点简化的主矢和主矩的表达式,并用此表达式导出了运动刚体的惯性力系向某些特殊点简化的结果。     

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional,time‐dependent Processes

High‐dimensional and time‐dependent data pose significant challenges to Statistical Process Monitoring. Most of the high‐dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross‐correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high‐dimensional, time‐dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time‐dependent data are discussed, and areas of possible further research are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1478–1493, 2016     

回转仪的惯性力偶矩公式

本文推导出了使回转仪进动的惯性力偶矩公式及由进动产生的惯性力偶矩公式。     

鲁棒主成分分析的运动目标检测综述

目的 运动目标检测在许多计算机视觉任务中发挥了重要的作用。背景建模是运动目标检测中传统而又常用的方法。然而,许多背景建模方法是基于像素点的,对背景方面的考虑过于简单,难于处理真实视频。最近,将基于低秩和稀疏分解的鲁棒主成分分析应用于运动目标检测成为计算机视觉领域内的研究热点。为使更多国内外运动目标检测的研究者对鲁棒主成分分析方法进行探索和应用,本文对其进行系统综述。方法 融入最新研究进展,基于误差抑制、贝叶斯理论、时间和空间信息、多特征和多因素耦合,对各种国内外的鲁棒主成分分析模型进行归纳,并理论分析其优缺点。结果 本文采用变化检测数据集（change detection dataset）中不同场景的视频序列来对不同算法进行对比实验。从实验结果可知,属于第3类方法的DECOLOR 的检测效果优于其他算法,在均值对比中得到的召回率、精确率和F-measure分别为0.7、0.706和0.66。总体来说,当前改进算法都能有效地弥补最初鲁棒主成分分析方法的缺陷,提高了运动目标检测的精度。结论 鲁棒主成分分析在运动目标检测上取得了较多的研究与应用成果,在智能视频监控应用领域拥有广阔的应用前景。但是,其仍需针对鲁棒主成分分析存在的一些局限性进行深入的研究。融入前景运动目标在视频中的先验知识是基于鲁棒主成分分析的运动目标检测的发展趋势。