Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

乳酸菌对红曲黄酒品质的影响研究

该研究通过传统可培养法从米酒样品中分离出9株乳酸菌,在相同的条件下添加9株乳酸菌发酵制备红曲黄酒,并采用酸碱直接滴定法、折光仪法、高效液相-示差折光法和电子舌技术,同时结合统计学的方法对添加不同乳酸菌制备的红曲黄酒的总酸、可溶性固形物、有机酸含量和滋味品质进行评价。结果表明,这9株乳酸菌发酵制备的红曲黄酒总酸、可溶性固形物、有机酸的含量和滋味较未添加乳酸菌制备的红曲黄酒有较大差异,且添加乳酸菌发酵的红曲黄酒的可溶性固形物的含量显著高于对照组,总酸的含量的低于对照组,并经主成分分析可知相同种类的乳酸菌发酵红曲黄酒样品的品质有相似的影响。     

基于太赫兹光谱的人参和西洋参鉴别

为了快速区分人参和西洋参，依据两者所含的人参皂苷在太赫兹波段具有不同指纹光谱，提出了一种基于人参皂苷的太赫兹光谱鉴别人参和西洋参的方法。采用MATLAB软件识别人参和西洋参的太赫兹光谱，同时根据太赫兹光谱特征并通过标准的主成分分析(PCA)区分出人参、西洋参。实验结果表明，基于太赫兹光谱的主成分分析可精确区分出人参、西洋参，并且还可以应用于其他类似物质的区分中。     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa Cavendish)

The objective of this study was to evaluate the effect of sample presentation (tissue type) and maturity (ripe and unripe) on the classification of banana (Musa Cavendish) samples sourced from two different geographical regions and analysed using mid infrared (MIR) spectroscopy. The coefficient of determination (R²) and the standard error of cross-validation (SECV) obtained using partial least squares discriminant analysis were 0.83 (0.33), 0.75 (0.25) and 0.94 (0.19) for the prediction of maturity, geographical origin and tissue type, respectively. No effect of either of type of tissue (e.g. pulp or peel) or maturity was observed. The results of this study demonstrated that MIR spectroscopy might be used to classify the origin of the banana samples at different degrees of ripeness. However, one of the limitations of this study is on the number of samples analysed and further validation must be recommended using samples from other sources, regions and harvest seasons.     

一种灰度化混合法在集装箱箱号识别中的运用

研究一种基于机器视觉的集装箱箱号识别方法。对于集装箱彩色图像预处理过程中的灰度化方法，传统的灰度化算法不能有效弥补图像中污损或其他信息缺失的部分，因此，本文提出使用主成分分析法（PCA）结合贝叶斯阈值估计灰度变化率的混合法对图像的灰度化进行优化，可以在判断图像中某一点灰度值与周围相邻像素点的灰度值的变化率后，弥补缺失信息，有效确定边缘特征，从而使后续的字符识别准确率大大提高。最后使用该算法模型设计实现一套用于港口集装箱的智能检测系统。经过Matlab实验验证，在对50幅港口集装箱箱号图像的识别中，通过使用本文提出的混合灰度化方法，与普通的均值法和加权平均法的灰度化方法相比，准确率更高，其中单一字符准确率可达96%，箱号准确率可达92%。     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs) for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN) method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA) and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.     

基于深度学习的番茄叶部病害识别模型

番茄叶病种类多、成因复杂,其预防和识别难度较大。传统基于机器学习的方法多靠人工识别,需要一定的专家经验,且具有主观性强、识别准确率不高等缺点。为实现番茄叶病特征的自动提取,并提高识别准确率,提出一种基于深度学习的番茄叶病识别模型。该模型基于卷积神经网络对番茄叶部病害特征进行自动提取,获得高维特征后,采用PCA降维算法去除冗余特征;从增大类间距离并减小类内距离的角度改进了softmax分类器,提高了识别准确率。将该模型在CrowdAI提供的数据集上进行了仿真验证,结果表明,该模型能够对番茄叶部常见10种病害进行自动提取特征和识别,综合识别准确率达到95%以上。