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.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Biomass estimation of Shorea robusta with principal component analysis of satellite data

Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.

Cardiovascular age of aviation personnel: based on the principal component analysis of heart rate and blood pressure variability

Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P <0 05), whereas the PC3 orig was positively correlated with the chronological age ( P <0 01). The cardiovascular age thus calculated from the regression equation was significantly correlated with the chronological age among the 89 aviation personnel ( r =0.73, P <0 01). Conclusion: The cardiovascular age calculated based on a multi variate analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel.

Dimensioning a stockpile operation using principal component analysis

Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades.In this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input data.The study was conducted in three steps.First,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance.Next,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB.We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles.Finally,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal components.The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.

Improved performance of process monitoring based on selection of key principal components

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Study on Segmented Correlation in EEG Based on Principal Component Analysis

In order to explore the correlation between the adjacent segments of a long term EEG, an improved principal component analysis(PCA) method based on mutual information algorithm is proposed. A one-dimension EEG time series is divided equally into many segments, so that each segment can be regarded as an independent variables and multi-segmented EEG can be expressed as a data matrix. Then, we substitute mutual information matrix for covariance matrix in PCA and conduct the relevance analysis of segmented EEG. The experimental results show that the contribution rate of first principal component(FPC) of segmented EEG is more larger than others, which can effectively reflect the difference of epileptic EEG and normal EEG with the change of segment number. In addition, the evolution of FPC conduce to identify the time-segment locations of abnormal dynamic processes of brain activities,these conclusions are helpful for the clinical analysis of EEG.

Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition

Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.

Statistical Analysis of Process Monitoring Data for Software Process Improvement and Its Application

Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.

Study on mechanism and genetic analysis of lipid metabolism disorder in pregnant rats

Objective: To analyze the characteristics and possible mechanism of lipid metabolism in pregnant rats with intestinal flora imbalance. Methods: A total of 129 sexually mature female SD rats were divided into three groups: non-pregnant group (untreated healthy rats), healthy pregnant group (natural insemination pregnant rats), and pregnant microflora disorder group (pregnant rats were given mixed antibiotics by gavage to build the modeling), with 43 rats in each group. The contents of TG, LDL, HDL and TC were detected by automatic biochemical analyzer, and the contents of SCD1, PGC-1 alpha, PEPCK, ApoE and MTTP genes were detected by fluorescence quantitative PCR technology. Regression analysis was used to explore the comprehensive influence of each gene on total cholesterol expression in rats. Principal component analysis was used to explore the internal mechanism of lipid metabolism in pregnant rats with intestinal flora disorder. Results: The contents of TG, TC, LDL and HDL were compared among the three groups of rats and the differences were statistically significant (P<0.05) . The expression levels of related genes (SCD1, PGC-1, PEPCK, ApoE, MTTP) in the three groups were statistically significant (P<0.05) . SCD1 content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.92±0.12) μg/mL, (1.20±0.15)μg/mL, and (1.53±0.20) μg/mL, respectively. PGC-1 alpha content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (1.34±0.21) μg/mL, (0.93±0.12) micron /mL, and (0.41±0.08) μg/mL, respectively. PEPCK content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.48±0.06) μg/mL, (0.35±0.09)μg/mL, and (0.22±0.05) μg/mL, and the differences were statistically significant (P<0.05) . Multivariate linear regression analysis showed that the influence of gene content on The effect of each gene content on TC content was in order from large to small: SCD1 (OR=4.572) , PGC-1 (OR=3.387) , PEPCK (OR=3.935) , ApoE (OR=3.597) , MTTP (OR=3.096) . The principal component analysis showed that three principal components could be extracted from five related genes of lipid metabolism in pregnant rats with intestinal dysbiosis: SCD1/PEPCK pathway (contribution rate: 36.28%) , PGC-1 /ApoE pathway (contribution rate: 30.42%) , and MTTP pathway (contribution rate: 15.37%) . Conclusion: After pregnancy, blood lipids in rats are significantly increased while the imbalance of intestinal flora will lead to decreased blood lipids. The disorder of lipid metabolism in pregnant rats with intestinal flora imbalance is mainly related to the disorder of gene expression, which further affects the functions of SCD1/PEPCK, PGC-1 /ApoE and MTTP pathways.

机场能见度临近预测方法

能见度是保障机场航班安全、正常运行的重要标准之一。为精准预测能见度,使用2020年天津机场气象和常规空气质量监测数据,构建基于方差膨胀因子(Variance Inflation Factor,VIF)、主成分分析(Principal Components Analysis,PCA)和Informer的能见度预测模型,并将均方根误差、平均绝对误差、平均绝对百分比误差作为评价指标进行误差分析。结果显示,VIF PCA Informer模型比单一的Informer和简单组合模型效果更优,能更好地捕捉长时间序列特征的关系。相比于单一的Informer、长短期记忆神经网络和门控循环单元模型,VIF PCA Informer模型均方根误差下降了0.2141~0.3486,平均绝对误差下降了0.1842~0.2753,平均绝对百分比误差下降了0.3224~0.5270;VIF PCA Informer模型对能见度的临近预测(1 h)更为精准。使用高效的机场能见度预测模型可在保障航班安全高效运行方面发挥较大支撑作用。

基于主成分分析的多重定量PCR荧光串扰校正

聚合酶链式反应(PCR)是分子生物学常用的检测手段,主要用于对生物的DNA或RNA进行检测。由于荧光光谱重叠和滤光片过滤带宽限制,检测时所获得的荧光数据通常会包含荧光通道之间的串扰,串扰的存在使PCR结果分析变得复杂,并可能影响最终的检测结果。选择合适的光学元件,并确定通道间的补偿矩阵,可以降低甚至消除荧光串扰。目前荧光补偿矩阵大多通过迭代计算获得,还没有一种简单的方法可以从混合的多通道荧光数据中找到荧光补偿矩阵。为了快速获得荧光补偿矩阵,减小计算量,采用主成分分析法(PCA)中确定主成分的方式,基于搭建的测试平台进行单一染料实验,获得染料的荧光信号在各个检测通道的分布情况,计算得到荧光补偿矩阵。通过分析补偿矩阵,发现对于搭建的硬件系统,Cy5染料对Cy5.5通道串扰较大,串扰比例为8.76%,同时Cy5.5染料对Cy5通道串扰影响也相对较大,比例约为6.2%;其次是ROX染料对HEX通道串扰,比例约为2.68%;HEX染料对FAM通道串扰,比例约为1.58%;FAM染料对HEX通道串扰相对较小,比例约为0.25%,其余通道无明显串扰,与荧光光谱反映的结果一致。采用得到的荧光补偿矩阵对单一染料实验得到的原始荧光数据进行处理,有效去除了非目标通道的荧光串扰,实现了荧光通道数据的解耦,验证了方法的可行性。最后设计了染料颜色分辨实验,将不同浓度的多种染料进行组合测试,并采用所提出的方法将得到的数据进行荧光补偿。实验结果表明,荧光通道各自的线性相关性较高,五个荧光通道的线性相关系数r均大于0.99,该结果进一步验证了该补偿方法的有效性。

基于时域卷积网络与Transformer的茶园蒸散量预测模型

在茶园水资源管理中,蒸散量(Evapotranspiration,ET)是评估作物水分需求的关键指标,由于茶园蒸散量预测具有时序性、不稳定性以及非线性耦合等特点,目前的茶园蒸散量预测模型存在预测精度较低的问题,针对此问题本文提出了一种新型的茶园蒸散量预测模型。首先使用互信息算法(Mutual information,MI)与主成分分析算法(Principal component analysis,PCA)相融合的数据处理算法(MIPCA),筛选强相关的特征并提取主成分;其次将时域卷积网络(Temporal convolutional network,TCN)与Transformer融合,利用灰狼算法(Grey wolf optimization,GWO)优化超参数,捕捉茶园数据的全局依赖关系;最后整合2个网络构建了MIPCA-TCN-GWO-Transformer模型,通过消融试验和对比试验验证了模型性能,并对模型在不同时间步长下的性能进行测试。结果表明,该模型平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)和决定系数(Coefficient of determination,R^(2))3个评价指标分别为0.015 mm/d、0.312 mm/d和0.962,优于长短期记忆模型(Long short term memory,LSTM)等传统预测模型。在小时尺度、日尺度和月尺度下的R^(2)分别为0.986、0.978和0.946,在不同时间步长下展现了良好的适应性和准确性。本文构建的MIPCA-TCN-GWO-Transformer模型具有较高的预测精度和稳定性,可为茶园水资源优化管理和灌溉制度制定提供科学参考。

一种波段聚类和多尺度结构特征融合的高光谱图像分类模型

高光谱图像包含丰富的地物信息,在农业、工业和军事等领域应用广泛。因此,高光谱图像的识别与分类是一项重要的研究课题。然而,高光谱图像存在光谱维度高、噪声大、标记样本有限等问题,并未取得很好的分类效果。针对以上问题,提出一种波段聚类和多尺度结构特征融合的高光谱图像分类模型(ASPS-MRTV)。该方法主要包括以下几个步骤,首先,对高光谱数据进行归一化处理,将归一化后的三维图像按光谱维等分为n个子空间;其次,采用粗细划分策略构造自适应子空间光谱特征提取框架,将每个空间波段拉伸为一维向量后用信息散度构造波段的相似性矩阵,按照聚类的思想对n个子空间进行自适应;然后,将每个自适应子空间的光谱波段平均值进行叠加,形成光谱特征;最后,对所得到的光谱特征数据利用多尺度相对全变分技术提取结构特征。为了增强样本的线性可分性,在数据堆叠之后进行核主成分分析,最终形成空谱特征。对比实验中统一使用惩罚参数C和核参数σ都为24.5的SVM进行分类。经测试,ASPS-MRTV网络模型在Indian Pines、 University of Pavia两个数据集上分别以5%, 1%的训练样本达到了97.06%、 98.98%的总体分类精度。实验结果表明,该模型与SVM、 ASPS(ED)、 ASPS(ID)、 ASPS-LBP、 ASPS-GlCM、 ASPS-BF模型相比,在分类性能和计算效率方面都取得了更优的效果,有效提高小样本下高光谱图像的分类精度。

基于主成份分析法的老年人信息素养体系建构

为了构建老年人的信息素养体系,帮助他们提高信息素养水平,选取60岁以上的老年群体作为研究对象,并采用自制量表对他们的信息素养进行调查.然后,运用主成份分析法将23个老年人信息素养指标降维为3个主成份因子.这3个主成份因子既相互独立,又能够包含原有的23个老年人信息素养指标的大部分信息,从而使老年人的信息素养问题变得简洁明了.再结合问卷内容逐一阐述3个主成份即信息意识、信息技能和信息道德的构成要素,最终成功构建老年人的信息素养体系,为老年人在社区接受信息技术教育提供科学的指导.

武都地区初榨橄榄油酚类和脂肪酸组成对油脂氧化稳定性研究

对武都地区的白橄榄(U)、恩帕特雷(E)、奇迹(K)、阿斯(As)、中山24(Z)、云台14(Y)、皮瓜尔(P)、豆果(Arbe)、小苹果(M)、鄂植8(Ez)、阿尔伯萨拉(Arbo)、科拉蒂(C)、莱星(L)、佛奥(F)这14个品种初榨橄榄油的脂肪酸、酚类成分及油脂氧化稳定性进行检测和分析,单因素方差分析表明:品种间多酚和脂肪酸含量及油脂氧化稳定性存在显著性差异(p<0.05)。所有分析样品的脂肪酸组成均符合欧盟特级初榨橄榄油标准,初榨橄榄油中油酸质量分数在(56.12±0.24)%(豆果)和(71.45±0.42)%(科拉蒂)之间,亚油酸质量分数在(5.73±0.06)%(皮瓜尔)和(15.80±0.05)%(阿斯)之间,棕榈酸质量分数在(12.67±0.12)%(科拉蒂)和(18.76±0.04)%(豆果)之间。裂环烯醚萜类是主要的酚类成分,总酚质量分数最高为奇迹,为(471.35±29.34)mg/kg,最低为豆果,仅(165.65±8.08)mg/kg。主成分分析表明:富含橄榄苦苷苷元、女贞子苷元、橄榄裂环烯醚萜、油酸、芹菜素的品种氧化稳定性越高,而富含棕榈酸、亚油酸、酪醇、羟基酪醇、刺激醛的品种氧化稳定性越低。基于芹菜素、橄榄裂环烯醚萜、木犀草素和亚油酸建立的多元线性逐步回归模型可以预测90.70%的油脂氧化稳定性变化(p<0.001)。

无人机高光谱影像水面耀光去除及信息重构方法研究

抑制遥感影像水面耀光污染并重构影像信息,是改善无人机遥感信息质量、扩大水环境监测区域的有效途径.针对传统经典的耀光信息重构算法难以适用于无人机高光谱影像这一问题,提出了一种耀光自动检测去除与信息重构算法,即采用归一化水体指数提取水体,以全波段总和灰度图像的最低值为阈值对耀光进行分割,利用拉普拉斯算子提取水面耀光纹理,通过多轮形态学膨胀与阈值更新迭代计算出两者面积差值,以投票机制获得最小差值的出现频率,并逆向获取最佳阈值自动去除耀光.而后,基于主成分分析确定匹配波段,通过改进Criminisi算法对去除区域进行重构.去除算法应用于四个真实耀光场景,去除率均在99%以上.重构算法结果在主观和客观上均优于其他算法,耀光重构水体与正常水体各波段变异系数差值在1%以内,具有良好的光谱应用能力.

内蒙古赤峰柴胡栏子金矿田遥感地质解译和蚀变信息提取与找矿预测

内蒙古柴胡栏子金矿田位于赤峰—朝阳金成矿带的最西北侧,是我国重要的黄金产地,但其自然条件恶劣,遥感地质研究程度较低,对于最主要的控矿因素(地层、构造、岩浆岩)分布特征以及与本区金成矿相关的围岩蚀变信息研究相对较少,利用遥感技术对该矿田进行找矿勘查具有重要意义。本文利用Landsat 8和GF-2遥感影像,结合“主成分分析+最佳指数因子”的方法组合,对柴胡栏子金矿田进行遥感地质解译。根据蚀变矿物的波谱特征,设计去除干扰信息-异常信息提取-异常分级-异常信息处理的蚀变信息提取方案,对Landsat 8和Sentinel-2A遥感影像进行蚀变信息提取;在前人区域地质、矿田地质研究的基础上,依据蚀变信息特征,结合地质解译成果圈定3个找矿靶区。研究表明,利用多源遥感卫星影像对研究区进行地质解译与蚀变信息提取,基本能够满足辅助中比例尺找矿预测及地质综合调查的需要,并提升找矿效率。研究成果对柴胡栏子金矿田未来找矿勘查具有基础性指导意义。

时间特征与空间特征融合的轻量网络故障诊断方法

为了解决多传感器数据间存在信息交叉、特征重复,导致模型训练精度低的问题,对滚动轴承在声辐射信号下的故障诊断进行了研究,提出了一种时间特征与空间特征融合的轻量网络故障诊断(SF-TFNet)方法。首先,利用卷积神经网络提取了原始轴承声阵列信号的空间特征(SFs),使用长短时记忆网络(LSTM)提取了声阵列信号中的时域特征(TFs),并对提取的SFs和TFs进行了特征融合,生成了新的特征矩阵;然后,为了消除融合特征带来的重叠特征和信息冗余问题,引入了基于核的主成分分析(KPCA)方法对新生成的特征矩阵进行了非线性降维,去除了特征中的冗余成分,构建了滚动轴承新的时空特征数据集;最后,采用AdaBoost算法对新生成的数据集进行了故障分类,并得到了滚动轴承的最终故障诊断结果。研究结果表明:在半消声室滚动轴承故障实验台测试中,SF-TFNet方法的故障分类精度可以达到99.75%,其分类精度较高、聚类效果明显。在强背景噪声环境下与ResNet、ICNN和AlexNet三种方法进行比较,SF-TFNet方法不仅收敛速度快,而且故障识别精度高,诊断精度最高可达99.25%。为基于多通道的滚动轴承声辐射信号故障诊断提供了理论依据。