Establishment of HPLC Fingerprint, Cluster Analysis and Principle Component Analysis of Citri Reticulatae Pericarpium Viride

[Objectives] This study aimed to establish HPLC fingerprint and conduct cluster analysis and principle component analysis for Citri Reticulatae Pericarpium Viride. [Methods] Using the HPLC method, the determination was performed on XSelect~® HSS T3-C_(18) column with mobile phase of acetonitrile-0.5% acetic acid solution(gradient elution) at the flow rate of 1.0 mL/min. The detection wavelength was 360 nm. The column temperature was 25℃. The sample size was 10 μL. With peak of hesperidin as the reference, HPLC fingerprints of 10 batches of Citri Reticulatae Pericarpium Viride were determined. The similarity of the 10 batches of samples was evaluated by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012 edition) to determine the common peaks. Cluster analysis and principal component analysis were performed by using SPSS 17.0 statistical software. [Results] The HPLC fingerprints of the 10 batches of medicinal materials had total 11 common peaks, and the similarity was 0.919-1.000, indicating that the chemical composition of the 10 batches of medicinal materials was consistent. There were 11 common components in the 10 batches of medicinal materials, but their contents were different. When the Euclidean distance was 20, the 10 batches of samples were divided into two categories, S4 in the first category, and the others in the second one. When the Euclidean distance was 5, the second category could be further divided into two sub-categories, S1 and S10 in one sub-category, and S2, S3, S5, S6, S7, S8 and S9 in the other one. The principle component analysis showed that cumulative contribution rate of the two main component factors was 92.797%, and the comprehensive score of S7 was the highest with the best quality. [Conclusions] The results of HPLC fingerprinting, cluster analysis and principle component analysis can provide reference for the quality control of Citri Reticulatae Pericarpium Viride.

RELATIVE PRINCIPLE COMPONENT AND RELATIVE PRINCIPLE COMPONENT ANALYSIS ALGORITHM

In this letter,the new concept of Relative Principle Component (RPC) and method of RPC Analysis (RPCA) are put forward. Meanwhile,the concepts such as Relative Transform (RT),Ro-tundity Scatter (RS) and so on are introduced. This new method can overcome some disadvantages of the classical Principle Component Analysis (PCA) when data are rotundity scatter. The RPC selected by RPCA are more representative,and their significance of geometry is more notable,so that the application of the new algorithm will be very extensive. The performance and effectiveness are simply demonstrated by the geometrical interpretation proposed.

GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

Celiac Disease Seen with the Eyes of the Principle Component Analysis and Analyse Des Données

This paper aims to deepen the quality of life of people with celiac disease with a focus on compliance to the diet through Principle Component Analysis and Analyse des Données. In particular, we will try to understand whether these analyzes are also applicable in the context of research web2.0 carried out with web-survey.

Retrieve Sea Surface Salinity Using Principal Component Regression Model Based on SMOS Satellite Data

The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Transformer’s Condition Assessment Method Based on Combination of Cloud Matter Element and Principal Component Analysis

With the development of power grid, as one of the key equipment, the transformer’s condition assessment method has always receive attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the transformer’s complex structure, the assessment system is not comprehensive enough, or the assessment system is too complex, the indexes are not easy to quantify, such problems are emerging. The traditional method is complex and the degree of quantification is not enough. Therefore it is necessary to propose a condition assessment method that is easy to carry out the condition assessment work and does not affect the assessment results. In this paper, we propose a method to assess the state of the transformer’s complex structure. First, we establish a comprehensive assessment system, then apply the method of principal component analysis to optimize the index system, and then use the theory of cloud-matter-element. Finally the reliability and rationality of the method are verified by an example.

The Minimum Energy Principle in Description of Nonlinear Properties of Orthotropic Material

In this paper the conception of theoretical determine the relations between material experimental characteristics is presented. On the base of stress-strain relations for nonlinear elastic anisotropic material and geometrical interpretation of deformation state, the general form of strain energy density function was introduced. Using this function and variational methods the relations between material characteristics were achieved. All considerations are illustrated by a short theoretical example.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

Online prediction of network-level public transport demand based on principle component analysis

Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.

基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型

根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。

基于Creo的无人机三维布线设计

该文分析三维设计软件Creo/Cabling的线缆敷设方法,包括三维布线的流程和优势,介绍手动布线的5个基本步骤(线束创建、线轴创建、元件设置、线缆敷设和修改布线路径)。并将该方法应用到无人机的电缆布线设计。通过实际布线仿真,针对布线时各步骤需要注意的问题进行详细说明;结合电磁兼容设计的需求,对无人机电缆进行分类,提出无人机三维布线设计中需注意的几个问题,并总结无人机线缆布线的设计原则。

基于近红外光谱技术鉴别丹参产地及其伪品

目的探讨应用近红外光谱技术结合化学计量学算法对不同产地丹参及其伪品的鉴别效果。方法收集陕西、山东、四川、河北等9个产地的265批丹参样品,以及市场常见的续断、牛蒡根和甘西鼠尾3种丹参伪品61批,利用SabIR漫反射光纤探头采集样品粉末的近红外漫反射光谱,并采用多元散射校正、标准正则变换、S-G平滑、Norris平滑、一阶微分及二阶微分等对光谱进行预处理。应用TQ Analyst分析系统,采用主成分分析结合马氏距离的判别分析方法,确定主成分数,建立丹参产地鉴别和真伪鉴别模型,并采用四重交叉验证测试模型预测性能。结果采用MSC+一阶+Norris的光谱预处理方式,确定最佳主成分数为10,建立模型并经过验证,产地鉴别准确率达到98%以上,真伪鉴别准确率达到100%。结论该方法可快速、准确鉴别丹参产地及其伪品,是对现有丹参鉴别方法的科学补充。

CuO掺杂C_(3)N对C_(5)F_(10)O分解组分吸附性能的第一性原理研究

全氟五碳酮(C_(5)F_(10)O)作为可替代SF_(6)的新型环保绝缘气体已被投入到实际应用中.当绝缘设备内部发生局部放电等故障时,C_(5)F_(10)O会分解产生弱绝缘性的CF_(4)、C_(2)F_(6)以及剧毒的CF_(2)O、HF等有害组分,为保证绝缘设备的安全运行,需有选择地通过吸附去除这些分解组分.新型类石墨烯C_(3)N材料在气体吸附领域具有良好的应用前景,文中基于第一性原理计算了CuO分子掺杂C_(3)N对主要分解组分CF_(4)、C_(2)F_(6)及剧毒产物CF_(2)O、HF的吸附过程,计算并分析了各分解组分吸附时的吸附能、态密度、电荷转移量、差分电荷密度以及不同环境温度下的恢复时间.结果表明,CuO-C_(3)N对HF表现出良好的吸附性,CF_(2)O次之,但其无法吸附CF_(4)与C_(2)F_(6),因此CuO-C_(3)N可以作为一种高性能的气体吸附剂对C_(5)F_(10)O绝缘设备内的剧毒分解组分HF进行吸附去除.

7份羊茅属种质芽期抗旱性评价

为了探究羊茅属种质芽期对逆境的胁迫能力,以沟羊茅新品系(FV)与紫羊茅常用品种传奇(CQ)、草原(CY)、梦神(MS)、北方(BF)、玉山(YS)和多花(DH)为材料,采用不同浓度PEG-6000(0、5%、10%、15%、20%、25%)模拟干旱胁迫,在芽期测定了发芽率、发芽势、活力指数、胚根长、胚芽长、根芽比等萌发指标,分析干旱对其种子萌发的影响,并结合主成分分析、隶属函数分析、聚类分析等对其抗旱性进行评价。结果表明,高浓度PEG(20%~25%)显著降低了羊茅属种质的发芽势、发芽率、发芽指数、活力指数、胚根长、胚根长及根芽比,而5%~15%PEG则因种质的差异而变化趋势有所不同,其中0~10%PEG对FV、BF、YS的发芽率、发芽势、活力指数及发芽指数具有显著的引发作用;综合评价表明,主成分分析将7个相对单项性状指标降维成2个新的综合指标,累积贡献率达85.06%;以新合成的综合指标为依据,采用隶属函数法结合聚类分析,将7份羊茅属种质分为三类,其中,FV、CQ和BF的抗旱能力较强,CY及YS的抗旱能力一般,而MS、DH的抗旱能力较弱。

75份青稞种质的品质性状综合评价

为有效利用青稞种质资源、促进专用型品种的选育,利用主成分分析和因子分析法对75份青稞品种(系)在3个生态点的籽粒中蛋白质、γ-氨基丁酸、总淀粉、β-葡聚糖和花青素含量共5个籽粒品质性状进行分析与评价。结果显示,基因型、生态点及生态点与基因型互作对5个被测性状均有显著影响,5个性状的变异系数为3.88%~118.92%,广义遗传率为15.29%~90.13%,其中蛋白质、β-葡聚糖和花青素含量的广义遗传率大于60%,表明遗传是其含量差异的主要原因。利用主成分分析可将5个性状简化为3个公因子,其携带总信息量的73.73%。经过Promax旋转后的载荷结果显示,3个公因子分别反映青稞籽粒的氨基酸累积量、次生代谢物累积量和多糖累积量。加权评分后的综合排名和分层聚类结果显示,勾芒青稞、龙中紫、2009-119、XQ0168、次玛、4TH HBSNS-57、813、长芒黑青稞、白勾芒、耐那、对芒黑青稞、褐青稞、北青4号、白青稞和昆仑14号共15个材料的综合品质较好;单性状优势的统计分析结果表明,高蛋白育种适宜选用龙中紫,低β-葡聚糖育种适宜选择白勾芒、对芒黑青稞和褐青稞,高蛋白质且低β-葡聚糖含量育种适宜选择2009-119和XQ0168,高γ-氨基丁酸和高花青素育种适宜选择次玛,高γ-氨基丁酸和低葡聚糖育种适宜选择4TH HBSNS-57。

双相多主元合金热机械处理的进展

双相多主元合金作为一种新型金属结构材料,一经提出便受到广泛关注和研究。作为金属材料,热机械处理是调控其微观组织,提升综合力学性能的重要方法。本文综述了国内外有关双相多主元合金热机械处理的研究现状,从轧制工艺、时效参数、多级处理等方面讨论了热机械处理对双相多主元合金微观组织的调控作用,进而总结了热机械处理对其力学性能的影响,以期对双相多主元合金的热机械处理研究提供参考。

羊草种质萌发阶段耐盐性状与评价

羊草(Leymus chinensis)是我国北方草原优质乡土草种,是退化草地修复和人工草地建植的重要种质资源,其萌发和幼苗生长是羊草播种建植成功的关键.本试验以6个羊草种质(草都1号、草都2号、吉生1号、多伦羊草、海拉尔羊草、长岭羊草)为研究对象,对标准和盐胁迫(100 mmol·L^(-1))培养条件下种子萌发特征进行比较和分析,评价种质的应用场景和耐盐性.结果表明:标准萌发条件下,草都1号羊草发芽率、根长、苗高、总长(根长+苗高)、简化活力指数均显著高于其他种质;盐胁迫条件下,草都2号和多伦羊草发芽和幼苗生长指标显著高于其他种质,发芽各指标盐胁迫抑制率均显著低于其他种质,表现出最好的耐盐性.通过发芽率(发芽数量指标)、总长(幼苗生长速率指标)和简化活力指数(生长潜势指标)主成分提取,提取的主成分1包含了三个指标超过90%以上的信息,相应的得分排序表明标准萌发条件下草都1号最高,盐胁迫条件下草都2号最高,耐盐性多伦羊草最好.该结果为羊草草地品种选择提供依据,为羊草耐盐性适应性评价提供方法.

铝电解大修渣废旧阴极炭的处理技术研究现状及展望

铝电解槽大修渣废旧阴极炭的无害化处理和资源化利用是电解铝行业实现绿色可持续发展的瓶颈所在。介绍了铝电解废阴极炭中的有毒组分及有毒元素的迁移规律,重点总结了高温火法、液相浸出法和协同处理法的原理、研究现状及优缺点,并对未来发展趋势进行了展望。

玄武岩纤维品质影响因素及应用分析

介绍了玄武岩纤维的制备原理和加工过程,分析了影响玄武岩纤维品质的因素,综述了其作为高性能纤维的应用,最后探讨了玄武岩纤维未来的发展方向。

基于K-I-ELM多模型集成的分布式光伏出力短期预测方法

为响应“双碳”目标,高比例新能源接入的新型电力系统已成为下一个发展目标。光伏作为当前电力系统能源发电主体形式之一,其出力特性数据尚存在多源、异构及高维等分布特点,导致不同特征作用机理、机制较为复杂,继而加大分布式光伏系统出力的预测难度。为此,首先构建核主成分分析(kernel principle component analysis,KPCA)模型,通过核函数在特征空间中依据不同特征的有效信息蕴含度提取主成分;然后采用信息熵(information entropy,IE)模型,根据各主成分信息负载度量加权系数,综合求解相应作用权重;最后依据特征评估结果,针对性设置极限学习机(extreme learning machine,ELM)网络参数,降低预测不确定度。最终整合多类别数据挖掘模型,构建K-I-ELM预测方法,在复杂数据环境下实施光伏出力短期预测。基于某实际台区光伏发电数据进行案例分析,论证所提方法针对不同数据环境的适应性及较高的预测精度。