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。     

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。     

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。     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

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.     

DFT investigation analyzed with data mining technique of rare-earth dihydrides REH2 for hydrogen storage

Ab initio calculation and data mining are very useful methods to predict the interrelationships among structure properties. In this paper, the first-principles modelling based on the density functional theory (DFT) was used to explore the structural, mechanical, and electronic properties of ten hydrides REH₂ (RE = La, Ce, Pr, Nd, Sm, Tb, Dy, Ho, Er, Tm). From the present work, it can be noted that LaH₂, CeH₂, NdH_2, and PrH₂ compounds crystallizing in the hydrogen-rich cubic phase present good mechanical properties which could be of use to future applications in hydrogen storage. Furthermore, the electronic structures were also evaluated to provide a deep insight regarding their trends and confirm the metallic character of these systems. While, HoH₂, DyH₂, ErH₂, TbH₂, TmH₂, SmH₂ hydrides are mainly hard and rigid systems with high values of the bulk modulus (B), shear modulus (G), Young's modulus (E), micro-hardness (H), and low values of B/G ratio. More significantly, the adopted principal component analysis (PCA) provided an advantageous technique in selecting and identifying the minimum of inputs variables necessary to capture all information of the systems.     

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%。     

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.     

基于太赫兹时域光谱的芝麻油品种识别研究

为了实现对芝麻油品种的快速鉴别,本文基于太赫兹时域光谱(THz-TDS)提出一种芝麻油品种识别的方法。选取0~2.5 THz范围内的光谱进行分析,通过主成分分析法(PCA)对时域光谱数据进行降维,选择前4个主成分(累计贡献率大于99%)代表原始数据,然后利用支持向量机(SVM)方法对不同品种芝麻油进行分类识别,分类时使用3种不同的核函数建模,并采用网格搜索算法获得最优模型及其模型参数。使用径向基核函数(参数为惩罚函数C=0.01,核函数系数γ=0.1)的模型识别率最高,达到100%,说明太赫兹时域光谱技术结合PCA和SVM方法可以快速可靠的进行食用油的识别,为食品安全的识别提供一种新的技术手段。     

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.