The Space and Time Features of Global SST Anomalies Studied by Complex Principal Component Analysis

In this paper, the variability characteristics of the global field of sea surface temperature (SST) anomaly are studied by complex principal component (c.p.c.) analysis, whose results are also compared with those of real p.c. analysis. The data consist of 40 years of global SST monthly averages over latitudes from 42 5°S to 67 5°N. In the spatial domain, it is found that the distribution of the first complex loading amplitude is characterized by three areas of large values: the first one in the eastern and central equatorial Pacific Ocean, the second one in the northern tropical Indian Ocean and South China Sea, the third one in the northern Pacific Ocean. As it will be explained, this pattern may be considered as representative of El Nio mode. The first complex loading phase pattern shows a stationary wave in the Pacific (also revealed by real p.c. analysis) superimposed to an oscillating disturbance, propagating from the Pacific to Indian or the opposite way. A subsequent correlation analysis among different spatial points allows revealing disturbances actually propagating westward from the Pacific to the Indian Ocean, which could therefore represent reflected Rossby waves, i.e. the west phase of the signals that propagate disturbances of thermal structure in the tropical Pacific Ocean. In the time domain, a relation between the trend of the first complex principal component and the ENSO cycle is also established.

Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ＂training events＂ are represented in matrix form and transpose-multiplied to generate time- domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window （approxi- mately the width of the training event） is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigen- vectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled （50 Hz） data from six observatories, each equipped with three- component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes

Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in- cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis. In this approach, mutual information estimation and Bayesian information criterion （BIC） are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation （BP） network is applied whose input is multivar- iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis （LKPCA） model for incipient fault prognosis. The new method has been exemplified in a sim- ple nonlinear process and the complicated Tennessee Eastman （TE） benchmark process. The results indicate that the new method has suoerioritv in the fault prognosis sensitivity over other traditional fault prognosis methods.

Functional Time Series Models to Estimate Future Age-Specific Breast Cancer Incidence Rates for Women in Karachi, Pakistan

Background： Breast cancer is the most common female cancer in Pakistan. The incidence of breast cancer in Pakistan is about 2.5 times higher than that in the neighboring countries India and Iran. In Karachi, the most populated city of Pakistan, the age-standardized rate of breast cancer was 69.1 per 100,000 women during 1998-2002, which is the highest recorded rate in Asia. The carcinoma of breast in Pakistan is an enormous public health concern. In this study, we examined the recent trends of breast cancer incidence rates among the women in Karachi. Methods： We obtained the secondary data of breast cancer incidence from various hospitals. They included Jinnah Hospital, KIRAN （Karachi Institute of Radiotherapy and Nuclear Medicine）, and Civil hospital, where the data were available for the years 2004-2011. A total of 5331 new cases of female breast cancer were registered during this period. We analyzed the data in 5-year age groups 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75＋. Nonparametric smoothing were used to obtained age-specific incidence curves, and then the curves are decomposed using principal components analysis to fit FTS （functional time series） model. We then used exponential smoothing statspace models to estimate the forecasts of incidence curve and construct prediction intervals. Results： The breast cancer incidence rates in Karachi increased with age for all available years. The rates increased monotonically and are relatively sharp with the age from 15 years to 50 years and then they show variability after the age of 50 years. 10-year forecasts for the female breast cancer incidence rates in Karachi show that the future rates are expected to remain stable for the age-groups 15-50 years, but they will increase for the females of 50-years and over. Hence in future, the newly diagnosed breast cancer cases in the older women in Karachi are expected to increase. Conclusion： Prediction of age related changes in breast cancer incidence rates will provide useful information for controlling the overall burden of cancer in Pakistan and also serve as a resource for health planning in future research. Moreover, these models will be the most useful for modeling and projecting future trends of other cancers and chronic diseases.

Global Warming in Japanese Cities from 1960 to 2019 Using Machine Learning

In this study, we investigated the variations in warming between Japanese cities for 1960-1989, and 1990-2019 using principal component analysis (PCA) and k-means clustering. The precipitation and sunshine hours exhibited opposite tendencies in the PCA results. It was found that 1960M and 1990M had a correlation (r = 0.51). The 1960M and 1990M are the mean temperature anomalies in Japanese cities for 1960-1989 and 1990-2019, respectively. There was a strong correlation between temperature and precipitation (r = 0.62). There was an inverse correlation between 1960M and sunshine hours (r = −0.25), but a correlation between 1990M and sunshine hours (r = 0.11). Sunshine hours had less effect on the 1960M but more impact on the 1990M. The k-means clustering for 1960M and 1990M can be classified into four types: high 1960M and high 1990M, which indicates that global warming is progressing rapidly (Sapporo, Tokyo, Kyoto, Osaka, Fukuoka, Nagasaki), low 1960M and low 1990M, global warming is progressing slowly (Nemuro, Ishinomaki, Yamagata, Niigata, Fushiki, Nagano, Karuizawa, Mito, Suwa, Iida, Hamada, Miyazaki, Naha), low 1960M and high 1990M, global warming has accelerated since 1990 (Utsunomiya, Kofu, Okayama, Hiroshima), and normal 1960M and normal 1990M, the rate of warming is normal among the 38 cities (Asahikawa, Aomori, Akita, Kanazawa, Maebashi, Matsumoto, Yokohama, Gifu, Nagoya, Hamamatsu, Kochi, Kagoshima). Higher annual temperatures were correlated with higher annual precipitation according to the k-means clustering of temperature and precipitation. Two of the four categories consisted of places with high annual temperatures and high precipitation (Fushiki, Kanazawa, Kochi, Miyazaki, Kagoshima, Naha, Ishigakijima), and places with low annual temperatures and low precipitation (Asahikawa, Nemuro, Sapporo, Karuizawa).

Principal component cluster analysis of ECG time series based on Lyapunov exponent spectrum

In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-tabase confirmed the validity of our approach. Another technique named improved teacher selecting student (TSS) algorithm is presented to analyze unknown samples by means of some known ones, which is of better accuracy. This technique combines the advantages of both statistical and nonlinear dynamical methods and is shown to be significant to the analysis of nonlinear ECG time series.

扇形段拉矫力数据突变特征提取方法

多维时序拉矫力的幅值突变表征着扇形段机组的故障,多维时序数据的时间和空间复杂度对突变特征提取方法的准确率和效率提出了更高的要求。利用多维时序数据的时间和空间相似性,提出小波相关滤波结合主成分分析的多维时序突变特征提取方法。首先,将每一维时域含噪拉矫力分别变换到小波域,利用拉矫力的自相似性进行降噪;然后,将去噪后的拉矫力映射到正交空间中,利用多维拉矫力之间的互相似性,通过线性变换将多维拉矫力映射到一维,再根据一维特征曲线的统计特征确定阈值,提取突变特征;最后,应用基于时间的滑动窗口动态更新数据,实现状态的连续在线识别;通过现场实测数据分析表明,小波相关滤波-主成分分析模型对多维时序数据的突变特征提取的准确率和效率分别达到90.39%和97.56%。

基于洪水退水过程的喀斯特小流域径流组分与含水层储水结构

岩溶含水层普遍发育复杂的管道和裂隙系统,对含水层储水结构的定量描述是岩溶水文地质研究中的一大难点,尤其是在小流域尺度上的挑战更为突出。以贵州省龙里县羊鸡冲喀斯特小流域为例,选取2019~2022年的4场典型洪水事件,通过水文时间序列分析和流量衰减分析对小流域洪水退水过程开展定量分析研究,识别水文过程中径流的组分及来源,进而揭示岩溶含水层的储水结构特征。结果表明:羊鸡冲小流域的洪峰流量衰减过程可以划分为坡面径流来水、岩溶管道来水、中宽裂隙来水和微裂隙-孔隙来水4个过程,各过程对应的径流成分占洪峰流量的比重分别为9.32%~10.32%、32.40%~36.35%、38.46%~40.87%、13.14%~19.41%,其中岩溶管道和中宽裂隙组分构成了流域洪峰流量的主要组成部分;该小流域岩溶含水层的储水介质对应划分为岩溶管道、中宽裂隙和微裂隙-孔隙3种不同类型,其中岩溶管道和中宽裂隙构成了含水层最主要的储水介质,占比为78%~85%。

基于太赫兹时域光谱技术的大鼠皮肤瘢痕无创检测

目的:利用太赫兹时域光谱技术,探索大鼠皮肤瘢痕与正常皮肤的太赫兹信号特征差异,为皮肤瘢痕的病理检测提供新型无创检测技术。方法:制备全层皮肤缺损大鼠模型,利用反射式太赫兹时域光谱系统获得大鼠正常皮肤组织以及瘢痕组织的太赫兹信号图。结果:大鼠正常皮肤太赫兹信号可见两个明显的反射时域信号峰,且不同反射点反射峰信号特征较为规律;大鼠皮肤瘢痕部位太赫兹信号也具有两个明显的反射时域信号峰,但最高峰峰值相比正常皮肤太赫兹信号低。此外,通过主成分分析法发现瘢痕部位信号聚集在一起,正常皮肤信号聚集在一起。不同部位的太赫兹信号差异有统计学意义(P<0.05)。结论:太赫兹时域光谱技术可应用于皮肤瘢痕的无创检测,在生物医学领域具有很好的应用前景。

Relationships between changes of kernel nutritive components and seed vigor during development stages of F_1 seeds of sh_2 sweet corn

The changes of kernel nutritive components and seed vigor in F1 seeds of sh2 sweet corn during seed development stage were investigated and the relationships between them were analyzed by time series regression (TSR) analysis. The results show that total soluble sugar and reducing sugar contents gradually declined, while starch and soluble protein contents increased throughout the seed development stages. Germination percentage, energy of germination, germination index and vigor index gradually increased along with seed development and reached the highest levels at 38 d after pollination (DAP). The TSR showed that, during 14 to 42 DAP, total soluble sugar content was independent of the vigor parameters determined in present experiment, while the reducing sugar content had a significant effect on seed vigor. TSR equations between seed reducing sugar and seed vigor were also developed. There were negative correlations between the seed reducing sugar content and the germination percentage, energy of germination, germination index and vigor index, respectively. It is suggested that the seed germination, energy of germination, germination index and vigor index could be predicted by the content of reducing sugar in sweet corn seeds during seed development stages.

The Regression Analysis between the Meteorological Synthetic Index Sequence and PM2.5 Concentration

Adapting daily meteorological data provided by China International Exchange Station, and using principal component analysis of meteorological index for dimension reduction comprehensive, the regression analysis model between PM2.5 and comprehensive index is established, by making use of Eviews time series modeling of the comprehensive principal component, finally puts forward opinions and suggestions aim at the regression analysis results of using artificial rainfall to ease haze.

Analysis of Chaotic Characters for the Monthly Runoff Se-ries at Fudedian Station in Liaohe Bain

The evolution of monthly runoff is affected both by climate environment and human activities, and its characteristics play an important role in runoff prediction and simulation. In this paper, the G-P and the principal component analysis method, which are both based on the reconstruction theory of the phase space, are used to study the chaos characteristics of the monthly runoff series at Fudedian station in Liaohe basin. The results show that the monthly runoff series have a large probability of chaos.

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。

不同风格驾驶员眼动特性分析——夜间快速路分流区

为提高驾驶员在夜间快速路分流区换道的安全性,以不同风格的驾驶员为研究对象,分析不同风格驾驶员换道时眼动参数的变化规律。首先在前人研究基础上自编夜间快速路驾驶风格量表,对量表进行信度和Pearson相关系数法的效度检验;然后通过主成分分析法对问卷调查法得出的结果进行分析,得出驾驶风格综合得分量化模型,并使用K均值聚类分析法划分驾驶风格;最后基于眼动仪进行实车试验,获取三种风格驾驶员换道的眼动数据。试验结果表明:谨慎型、一般型、激进型驾驶员对于后视镜平均注视次数占比为48%、27%、25%;对左右两侧的平均累计注视时间占比是31%、25%、19%;平均瞳孔面积占比为56.3%、27.6%、16.1%。试验结果可以为提高驾驶员换道的行车安全性研究提供一定的数据支持。

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。

基于UPLC-Q-TOF-MS分析江西特色炮制技术对中药升麻化学成分的影响

目的比较江西特色炮制技术对升麻化学成分的影响,筛选优质饮片品种。方法采用超高效液相色谱-四极杆-飞行时间串联质谱(ultra performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry,UPLC-Q-TOF-MS)技术,在正、负离子模式下分析升麻不同炮制品的化学成分,通过对照品、相对分子质量、质谱裂解规律和文献信息进行鉴定。利用SIMCA-P13.0软件建立升麻各炮制品主成分分析(principal component analysis,PCA)和偏最小二乘法-判别分析(partial least squares discriminant analysis,PLS-DA)模型,获取PCA得分图、PLA-DA得分图和变量重要性投影(variable importance plot,VIP)值,筛选造成升麻炮制前后主要差异的物质基础。利用MetaboAnatyst网页绘图工具,制作得到热图,可更直观地观察升麻化学成分经炮制后的变化趋势。结果鉴定出71个化学成分,PCA显示经不同方法炮制后升麻组间差异性大,PLS-DA筛选出VIP值>1的33个化学成分作为炮制前后差异性的主要化学标记物。其中生品和蜜炙升麻中三萜类含量较高,蜜麸、蜜糠炒升麻中酚酸类物质含量较高,蜜麸升麻中阿魏酸含量较高。结论酚酸类和三萜皂苷类是区分升麻不同炮制品最重要的化合物类别,为江西特色升麻饮片的药效物质基础及优势品种研究提供了依据。

基于模式识别技术的光电探测器故障辨识研究

当前光电探测器故障辨识错误率高,为提升光电探测器故障辨识效果,设计了基于模式识别技术的光电探测器故障辨识方法。首先采集光电探测器状态信号,并从光电探测器状态信号中提取特征,然后利用主成分分析算法对特征进行降维处理,得到最优光电探测器状态辨识特征,最后将光电探测器状态特征作为支持向量机的输入,光电探测器状态作为支持向量机输出,通过支持向量机学习设计光电探测器状态辨识器,实验结果表明,本方法可以有效辨识光电探测器辨识故障,光电探测器故障辨识正确率超过了90%,光电探测器故障辨识时间控制在20 ms以内,为光电探测器状态分析提供了理论依据。