Energy-efficient Scheme for Multiple Access Network Selection Using Principal Component Analysis

This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.

Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis

To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.

Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods

Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

基于模态分解及GRU-XGBoost短期电力负荷预测

精确的短期电力负荷预测能有效提高电力系统运营水平。针对电力负荷数据受多种因素影响,波动性和随机性强等问题,提出了一种基于模态分解及混合模型的负荷预测方法。首先,采用主成分分析法(principal component analysis,PCA)对负荷特征向量进行处理,去掉冗余信息,再用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将历史负荷分解为简化的几个子序列;其次,选择引入样本熵(sample entropy,SE)来计算子序列熵值,将相近的子序列重构得到随机、细节、低频和趋势分量后选用不同结构门控循环单元(gate recurrent unit,GRU)对不同分量类型进行预测,再使用极致梯度提升模型(extreme gradient boosting,XGBoost)对各分量残差进行拟合,各重组序列的预测值为GRU预测值与XBGoost拟合值之和,重组各序列得到最终预测值。选取3年时电力负荷数据进行实验,结果表明,所提模型的均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolutepercentage error,MAPE)和平均绝对误差(mean absolute error,MAE)分别为370.676 MW、99.07%和246.89 MW,与单一模型和混合模型相比,实现了评价指标的明显减少。

基于PCA-ShapeDTW-QWGRU的分布式光伏集群短期功率预测

针对分布式光伏短期功率预测建立基于主成分分析、改进的动态时间规整算法与量子加权门控循环单元(PCAShapeDTW-QWGRU)的集群功率预测模型。针对集群划分不够精细、光伏电站数据蕴含的信息难以捕捉的问题,提出基于主成分分析结合密度聚类算法(PCA-OPTICS)的集群划分方法;针对目前选取代表电站与集群相似性较低的问题,提出基于改进的动态时间规整算法(ShapeDTW)的代表电站的选取方法,利用ShapeDTW度量相似性距离,选取最小值作为代表电站,并利用基于均方根传播梯度下降法优化的量子加权门控循环单元(RMSprop-QWGRU)模型进行预测;为了解决代表电站与集群功率的变换系数转换差异较大的问题,采用实时变换系数对代表电站进行集群功率值预测计算。实验结果表明,所提方法能有效提升光伏集群功率预测的精度。

一测多评法测定法制半夏曲中11种成分含量及其GRA、EW-TOPSIS质量评价

目的采用一测多评(QAMS)法同时测定法制半夏曲中肌苷、鸟苷、腺苷等11种成分含量,并建立其灰色关联度分析(GRA)联合熵权逼近理想解排序分析法(EW-TOPSIS)综合质量评价方法。方法采用Shimadzu C 18色谱柱;乙腈-0.5%醋酸为流动相,梯度洗脱,流速1.0 mL·min-1;检测波长254和290 nm。以对甲氧基肉桂酸乙酯为内参比物质,计算其他10个成分的相对校正因子(RCF),测定各成分含量。采用GRA联合EW-TOPSIS模型对法制半夏曲进行综合质量评价。结果法制半夏曲中11种成分在一定浓度范围内线性关系良好,相关系数均>0.999;平均加样回收率96.94%~100.12%(RSD<2.0%,n=9);QAMS与外标法(ESM)实测值无明显差异。GRA模型相对关联度0.2903~0.6187,EW-TOPSIS模型相对接近度0.2114~0.6343;GRA和EW-TOPSIS模型综合评价结果基本一致。结论QAMS法便捷、准确,可用于法制半夏曲多指标成分定量控制,GRA联合EW-TOPSIS模型可用于法制半夏曲综合质量评价。

Water Quality Evaluation Model Based on Principal Component Analysis and Information Entropy:Application in Jinshui River

水质评价对决策者决定水的使用功效尤为重要。水质综合评价系统中涉及到大量因子与指标,因子之间相互作用,致使水质的评价工作相对困难。主成分分析法可以消除因子间的相关性,因而被广泛应用于水质评价,但其忽略了数据离散程度的问题。熵值法则考虑了数据的离散特点。为更好地进行水质的综合评价,本文提出把主成分分析法和熵值法结合起来确定指标权重的方法,建立了水质评价模型,并采用该模型对郑州市金水河再生水2009年的水质情况进行评价,将评价结果与单独采用主成分分析或熵值法的结果进行了比较。结果表明了该方法的可行性与实用性,能够为非常规水资源利用提供理论依据和决策参考。

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

Multivariate Cluster and Principle Component Analyses of Selected Yield Traits in Uzbek Bread Wheat Cultivars

Investigation of genetic diversity of geographically distant wheat genotypes is a useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues > 1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was the greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18,373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).

基于WKPCA与IEDO-XGBoost的变压器故障诊断方法研究

针对变压器故障的特点,将加权核主成分分析技术与IEDO-XGBoost相结合,提出了一种新的变压器故障诊断模型。该方法主要将溶解气体分析技术与无编码比值法相结合,获取变压器的故障特征,利用WKPCA对其进行降维处理,并将归一化处理后的故障样本数据作为IEDO-XGBoost模型的输入,输出变压器故障诊断类型及其诊断准确率。选取20维变压器故障特征数据进行WKPCA降维处理,加快了模型的收敛速度;采用自适应正余弦策略和高斯变异策略对指数分布优化器算法进行改进,并用10个典型测试函数对改进后的指数分布优化算法性能进行了测试,结果表明改进后的指数分布优化算法具有更快的收敛速度和全局搜索能力。然后,利用改进的指数分布算法来确定XGBoost模型中的多个最优参数。仿真结果表明,该模型的诊断准确率为91.82%,分别比EDO-XGBoost、NGO-XGBoost、GJO-XGBoost、GWO-XGBoost和WOA-XGBoost故障诊断模型高2.73%、3.64%、5.46%、8.18%和10.91%,验证了本文所提方法能够有效提高变压器故障诊断性能。

HPLC多指标成分定量、化学计量学及熵权-TOPSIS分析在龙葵综合质量评价中的应用

目的建立不同产地龙葵中11个成分含量同步检测方法,并采用化学计量学和熵权-TOPSIS法对其质量进行评价。方法收集8省17个批次龙葵样品,采用HPLC法同时检测龙葵中梣皮树脂醇、松脂素、槲皮素、芦丁、澳洲茄碱、澳洲茄边碱、客西茄碱、澳洲茄胺、去半乳糖替告皂苷、薯蓣皂苷元和β-谷甾醇的含量,建立龙葵多组分定量控制模式;采用化学识别模式和熵权-TOPSIS法建立龙葵质量优劣评价模型,对其整体质量进行综合评价。结果11个成分分别在0.78~39.00,0.55~27.50,0.34~17.00,0.21~10.50,41.87~2093.50,60.95~3047.50,2.58~129.00,1.02~51.00,0.46~23.00,1.05~52.50,0.42~21.00μg/mL(r>0.9990)范围内线性关系良好,平均加样回收率在96.81%~100.28%范围内(RSD<2.0%,n=9);17批样品聚为3类;澳洲茄边碱、澳洲茄碱、去半乳糖替告皂苷和梣皮树醇可能是影响龙葵产品质量主要潜在标志物;熵权-TOPSIS法分析结果显示17批龙葵质量评价贴近度分别为0.4336、0.4168、0.6242、0.5008、0.4791、0.6361、0.5683、0.2500、0.1909、0.2221、0.1707、0.7200、0.6983、0.7447、0.7179、0.7209、0.7183,辽宁、吉林和黑龙江产地龙葵整体质量较好,其次为江苏、河南和安徽产地。结论建立的同时测定龙葵中11种成分含量的HPLC法,操作便捷,结果准确;化学计量学及熵权-TOPSIS法客观全面,可用于龙葵的整体质量评价。

基于PCA-GWR方法探究建成环境对轨道站点客流的影响

轨道站点的客流吸引能力与周边建成环境条件有紧密联系,聚焦轨道站点周边建成环境,从步行可达范围识别、5Ds建成环境要素数据提取与量化描述、相关客流模型选取与分析,形成一套数据驱动的建成环境与轨道客流量化建模方法,并以重庆市轨道站点为案例分析。研究结果表明:(1)以轨道站点为中心,步行10 min可达范围面积为半径500 m缓冲区面积的74.2%;(2)主成分解释变量能够克服解释变量间的多重共线性问题,基于主成分分析的地理加权回归模型(PCA-GWR)较地理加权回归模型拟合效果更好;(3)居住丰富性、出行便利性、绿化效果、站点可达性与轨道出站客流密切相关,主成分解释变量对轨道出站客流的影响程度与空间变化存在显著关系。

基于GA-IPSO-KPCA和变权组合模型的电动汽车充电方法

需求电压和需求电流是充电桩对电动汽车安全充电的重要依据。然而,随着电池的老化,电池管理系统的数据可能出现错误,使得电动汽车在充电时存在安全隐患。针对该问题,建立最小二乘支持向量机和深度置信网络的组合预测模型,提出一种基于变权组合模型的电动汽车充电方法。首先,针对数据掉线缺失问题,使用K均值和反距离加权方法对数据进行插值;然后,使用改进的混合核主成分分析算法对完整数据进行主成分提取,并使用改进粒子群优化算法自动确定混合核函数的权重。基于真实电动汽车数据的实验结果表明,所提方法能够准确地预测需求电压和需求电流,具有实际意义和可行性。

基于综合判定-TOPSIS法的站场加热炉能效水平评价

为提高油田站场加热炉的能效水平,在全面构建评价指标体系的基础上,通过熵权法、主成分分析法、灰色关联分析对各项指标进行赋权,结合三种方法对指标权重实施综合判定,并利用TOPSIS法完成加热炉能效水平的综合评价。结果表明,不同赋权方法得到的权重结果有所不同,负荷率、过剩空气系数、排烟热损失是影响加热炉运行状态和能效水平的主控因素;TOPSIS法的评价结果与规范要求和实际情况相符,考虑的因素更全面。根据评价结果,加热炉优化调整后,日燃气量下降15~184 m^(3)。研究结果可用于评价站场加热炉的用能情况,为油田节能降耗提供实际参考。

基于一测多评、化学计量学和EW-TOPSIS法的三叶青药材质量差异评价研究

目的采用一测多评(QAMS)、化学计量学和熵权-优劣解距离(EWTOPSIS)法对不同产地三叶青进行质量差异评价,为其道地研究提供依据。方法收集重庆、广西、湖南、广东、浙江、江苏等6省18批次三叶青样品,采用HPLC-QAMS法同时检测三叶青中绿原酸、咖啡酸、儿茶素、原花青素B1、荭草苷、芦丁、虎杖苷、异槲皮素、山奈酚-3-O-芸香糖苷、紫云英苷、白藜芦醇和山柰酚的含量,建立三叶青多组分定量控制模式;采用主成分分析和正交偏最小二乘法-判别分析等化学模式识别筛选影响三叶青药材质量的主要潜在标志物;利用EW-TOPSIS法建立三叶青质量优劣评价模型,对其质量差异性进行综合评价。结果12个成分在各自范围内线性关系良好,平均加样回收率为96.89%~100.13%,RSD<2.0%(n=9);HPLC-QAMS法计算值与外标法实测值之间无明显差异;前2个主成分累计方差贡献率为90.15%;原花青素B1、儿茶素、芦丁、荭草苷和绿原酸可能是影响三叶青产品质量主要潜在标志物;EW-TOPSIS法分析结果显示18批三叶青的相对关联度为0.1549~0.7320。结论所建立的方法操作便捷、结果准确,可作为三叶青多组分定量控制方法;化学计量学及EW-TOPSIS法客观全面,可用于三叶青的质量差异评价。

数字经济差异化发展对就业质量的影响研究--基于2013-2022年省域面板数据的实证分析

就业是民生之本、发展之源。文章使用熵权法构建了数字经济与就业质量测度指标体系,并利用2013—2022年我国31个省级行政区的面板数据,在克服内生性的基础上,实证分析了数字经济差异化发展对就业质量的影响,并探讨了产业结构的中介作用,使用主成分分析进行稳健性检验。研究发现,数字经济的差异化发展对就业质量有显著负向影响;就机制而言,资源配置与地区产业结构转型升级是数字经济影响就业质量的两个渠道。文章的结论为评估数字经济差异化发展对就业质量的影响提供了实证支撑与分析视角,也为相关部门提供了政策参考。

Estimation of crop water requirement based on principal component analysis and geographically weighted regression

In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macroand micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement.

Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis

This paper attempts to evaluate the coordinated development state of the subsystems within the internet financial ecosystem in China from 2011 to 2016.Focusing on the main business modes,technological innovation,and the external environment,we select 29 indicators to construct an index system and adopt a coupling coordination degree model for evaluation.Furthermore,we use two weight calculation methods,entropy weight and principal component analysis,to ensure the robustness of the results.The empirical results show that China’s internet financial ecosystem experienced five development stages from 2011 to 2016,which are moderate disorder,near disorder,weak coordination,intermediate coordination,and good coordination.Different methods of obtaining weights have little effect on the empirical results.These findings suggest that at the beginning,the coordinated development of China’s internet financial ecosystem was hindered by factors including the scarcity of main business modes and the defect of technological innovation;then,with the rapid development of China’s internet industry,the external environment became another drawback in coordinated development.Finally,based on the findings,we give some policy recommendations from a global perspective to achieve a sustainable internet financial ecosystem.

Multi-response optimization of Ti-6A1-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis

Ti-6A1-4V has a wide range of applications, especially in the aerospace field;however, it is a difficultto- cut material. In order to achieve sustainable machining of Ti?6A1-4V, multiple objectives considering not only economic and technical requirements but also the environmental requirement need to be optimized simultaneously. In this work, the optimization design of process parameters such as type of inserts, feed rate, and depth of cut for Ti-6A1-4V turning under dry condition was investigated experimentally. The major performance indexes chosen to evaluate this sustainable process were radial thrust, cutting power, and coefficient of friction at the toolchip interface. Considering the nonlinearity between the various objectives, grey relational analysis (GRA) was first performed to transform these indexes into the corresponding grey relational coefficients, and then kernel principal component analysis (KPCA) was applied to extract the kernel principal components and determine the corresponding weights which showed their relative importance. Eventually, kernel grey relational grade (KGRG) was proposed as the optimization criterion to identify the optimal combination of process parameters. The results of the range analysis show that the depth of cut has the most significant effect, followed by the feed rate and type of inserts. Confirmation tests clearly show that the modified method combining GRA with KPCA outperforms the traditional GRA method with equal weights and the hybrid method based on GRA and PCA.