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 r...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.展开更多
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 inter...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.展开更多
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 ...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.展开更多
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...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.展开更多
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...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。展开更多
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) ...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.展开更多
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 feat...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.展开更多
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 ...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.展开更多
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 principa...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.展开更多
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 exhib...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).展开更多
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...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.展开更多
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...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.展开更多
Annual and seasonal diurnal precipitable water vapor(PWV)variations over Central and South America are analyzed for the period 2007-2013.PWV values were obtained from Global Navigation Satellite Systems(GNSS)observati...Annual and seasonal diurnal precipitable water vapor(PWV)variations over Central and South America are analyzed for the period 2007-2013.PWV values were obtained from Global Navigation Satellite Systems(GNSS)observations of sixty-nine GNSS tracking stations.Histograms by climate categories show that PWV values for temperate,polar and cold dry climate have a positive skewed distribution and for tropical climates(except for monsoon subtype)show a negative skewed distribution.The diurnal PWV and surface temperatures(T)anomaly datasets are analyzed by using principal components analysis(PCA).The first two modes represent more than 90%of the PWV variability.The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature(T),therefore the temperature and the surface conditions(to yield evaporation)could be the main agents producing this variability;PWV variability in inland stations are mainly represented by this mode.The second mode of PWV variability shows a maximum amplitude at midnight,a possible explanation of this behavior is the effect of the sea/valley breeze.The coastal and valley stations are affected by this mode in most cases.Finally,the"undefined"stations,surrounded by several water bodies,are mainly affected by the second mode with negative eigenvectors.In the seasonal analysis,both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons,while the rest of the year they present a behavior according to their temperature and the surface conditions.As a result,the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.展开更多
In past few decades,climate has manifested numerous shifts in its trend.Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale.To understand the lo...In past few decades,climate has manifested numerous shifts in its trend.Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale.To understand the long term climate fluctuations,we have analyzed forty years(1978-2018)data of ten climatic parameters that are responsible to influence the climate dynamics.The parameters involved in the present study are total solar irradiance(TSI),ultra violet(UV)index,cloud cover,carbon dioxide(CO2)abundances,multivariate(ENSO)index,volcanic explosivity index(VEI),global surface temperature(GST)anomaly,global sea ice extent,global mean sea level and global precipitation anomaly.Using the above mentioned climate entities;we have constructed a proxy index to study the quantitative measure of the climate change.In this process these indicators were aggregated to a single proxy index as global climate index(GCI)that has measured the strength of present climate change in semblance with the past natural variability.To construct GCI,the principal component analysis(PCA)has been used on yearly based data for the period 1978-2018.Actually PCA is a statistical tool with which we can reduce the dimensionality of the data and it retains most of the variation in the new data set.Further,we have confined our study to natural climate drivers and anthropogenic climate drivers.Our result has indicated that the strongest climate change has been occurred globally by the end of the year 2018 in comparison to late 1970’s natural variability.展开更多
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...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。展开更多
文摘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.
文摘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.
文摘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.
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘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.
文摘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。
基金supported by the National Natural Science Foundation of China (No. 30370911)Education Department of Zhejiang Prov-ince, China (No. 20070147)
文摘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.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘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.
文摘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.
文摘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.
文摘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).
基金Supported by the National Natural Science Foundation of China(61573051,61472021)the Natural Science Foundation of Beijing(4142039)+1 种基金Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2015KF-01)Fundamental Research Funds for the Central Universities(PT1613-05)
文摘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.
文摘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.
基金supported by the National Scientific and Technical Council of Argentina(CONICET)PIP 112-201201-00292,ANPCyT grant PICT 20121484Universidad Nacional de La Plata(UNLP)project 11G/142
文摘Annual and seasonal diurnal precipitable water vapor(PWV)variations over Central and South America are analyzed for the period 2007-2013.PWV values were obtained from Global Navigation Satellite Systems(GNSS)observations of sixty-nine GNSS tracking stations.Histograms by climate categories show that PWV values for temperate,polar and cold dry climate have a positive skewed distribution and for tropical climates(except for monsoon subtype)show a negative skewed distribution.The diurnal PWV and surface temperatures(T)anomaly datasets are analyzed by using principal components analysis(PCA).The first two modes represent more than 90%of the PWV variability.The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature(T),therefore the temperature and the surface conditions(to yield evaporation)could be the main agents producing this variability;PWV variability in inland stations are mainly represented by this mode.The second mode of PWV variability shows a maximum amplitude at midnight,a possible explanation of this behavior is the effect of the sea/valley breeze.The coastal and valley stations are affected by this mode in most cases.Finally,the"undefined"stations,surrounded by several water bodies,are mainly affected by the second mode with negative eigenvectors.In the seasonal analysis,both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons,while the rest of the year they present a behavior according to their temperature and the surface conditions.As a result,the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.
基金AB is thankful to University Grants Commission(UGC),India for proving partial financial support(National Fellowship).
文摘In past few decades,climate has manifested numerous shifts in its trend.Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale.To understand the long term climate fluctuations,we have analyzed forty years(1978-2018)data of ten climatic parameters that are responsible to influence the climate dynamics.The parameters involved in the present study are total solar irradiance(TSI),ultra violet(UV)index,cloud cover,carbon dioxide(CO2)abundances,multivariate(ENSO)index,volcanic explosivity index(VEI),global surface temperature(GST)anomaly,global sea ice extent,global mean sea level and global precipitation anomaly.Using the above mentioned climate entities;we have constructed a proxy index to study the quantitative measure of the climate change.In this process these indicators were aggregated to a single proxy index as global climate index(GCI)that has measured the strength of present climate change in semblance with the past natural variability.To construct GCI,the principal component analysis(PCA)has been used on yearly based data for the period 1978-2018.Actually PCA is a statistical tool with which we can reduce the dimensionality of the data and it retains most of the variation in the new data set.Further,we have confined our study to natural climate drivers and anthropogenic climate drivers.Our result has indicated that the strongest climate change has been occurred globally by the end of the year 2018 in comparison to late 1970’s natural variability.
文摘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。
