Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between vari...Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.展开更多
By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.展开更多
Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing th...Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.展开更多
Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse mult...Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.展开更多
A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define...A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.展开更多
Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations ...Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.展开更多
Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal...Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.展开更多
A novel study using LCeMS(Liquid chromatography tandem mass spectrometry)coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of...A novel study using LCeMS(Liquid chromatography tandem mass spectrometry)coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of Shixiao San.Batches of these two kinds of samples were subjected to analysis,and the datasets of sample codes,tR-m/z pairs and ion intensities were processed with principal component analysis(PCA).The result of score plot showed a clear classification of the aqueous and vinegar groups.And the chemical markers having great contributions to the differentiation were screened out on the loading plot.The identities of the chemical markers were performed by comparing the mass fragments and retention times with those of reference compounds and/or the known compounds published in the literatures.Based on the proposed strategy,quercetin-3-Oneohesperidoside,isorhamnetin-3-O-neohespeeridoside,kaempferol-3-O-neohesperidoside,isorhamnetin-3-O-rutinoside and isorhamnetin-3-O-(2G-a-l-rhamnosyl)-rutinoside were explored as representative markers in distinguishing the vinegar extract from the aqueous extract.The anti-hyperlipidemic activities of two processed extracts of Shixiao San were examined on serum levels of lipids,lipoprotein and blood antioxidant enzymes in a rat hyperlipidemia model,and the vinegary extract,exerting strong lipid-lowering and antioxidative effects,was superior to the aqueous extract.Therefore,boiling with vinegary was predicted as the greatest processing procedure for anti-hyperlipidemic effect of Shixiao San.Furthermore,combining the changes in the metabolic profiling and bioactivity evaluation,the five representative markers may be related to the observed antihyperlipidemic effect.展开更多
Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis...Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.展开更多
In this paper, a new approach for visualizing multivariate categorical data is presented. The approach uses a graph to represent multivariate categorical data and draws the graph in such a way that we can identify pat...In this paper, a new approach for visualizing multivariate categorical data is presented. The approach uses a graph to represent multivariate categorical data and draws the graph in such a way that we can identify patterns, trends and relationship within the data. A mathematical model for the graph layout problem is deduced and a spectral graph drawing algorithm for visualizing multivariate categorical data is proposed. The experiments show that the drawings by the algorithm well capture the structures of multivariate categorical data and the computing speed is fast.展开更多
The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they ofte...The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.展开更多
In the research of scientific field, it is often necessary to continuously observe different indicators of individuals at different times and analyze the observed results. Among them, variables are mainly of two types...In the research of scientific field, it is often necessary to continuously observe different indicators of individuals at different times and analyze the observed results. Among them, variables are mainly of two types: ordered variables and continuous variables. When analyzing data for different types of variables, it is necessary to consider the correlation between multiple indicators of an individual, and often perform joint analysis on variable observation data of multiple indicators of an individual at different times, in order to achieve more accurate and true analysis results. Joint analysis often yields more information than separate analysis of various variables. In this paper, the ordered variable and the continuous variable are numerically modeled. Based on the potential variable model, the multivariate longitudinal data containing the ordered variable and the continuous variable are jointly analyzed, and the approximate value of the edge likelihood can be obtained by using the method of numerical integration.展开更多
Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus th...Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.展开更多
Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most re...Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.展开更多
With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the...With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the lack of equipment and network impacts,highly distributed industrial applications cannot capture and transfer all production data to a distant cloud server in real time.Consequently,a portion of critical production data is lost,which poses the significant challenge of the timely replenishment of missing data.Employing deep learning in the cloud center for data trend prediction based on relevant data can solve this problem.The objective of this study was to develop a time-series prediction model that combines a Transformer model with a sparse Mixture of Experts(MoE).The model is designed specifically for an IIoT system that is used in oil-well operations.The proposed TransMoE prediction model combines the advantages of the MoE and the Transformer model.The MoE can effectively handle multiple subtasks while the Transformer algorithm can reflect the long-range dependency of the input data series.The proposed model was used to predict oil-well yields,and the predicted outcomes were compared with those obtained using a CNN-GRU and CNNLSTM models,as well as the actual recorded data.The experimental results indicated that the proposed TransMoE model can significantly increase the efficiency and accuracy of oil well production sequence data prediction,with an average relative error of 6.26%,which can satisfy the requirements of enterprise data usage.展开更多
In clinical and observational research,multivariate recurrent event data are commonplace,as people may encounter several kinds of repeating occurrences.While event times are consistently recorded,there are instances w...In clinical and observational research,multivariate recurrent event data are commonplace,as people may encounter several kinds of repeating occurrences.While event times are consistently recorded,there are instances where the corresponding event types might not be observed.For multivariate recurrent event data,the authors introduce a time-varying coefficients additive rate model which takes into consideration scenarios in which event types are missing at random(MAR).The authors employ an inverse probability-weighted estimating equation to derive inferences for the time-independent and time-dependent effects,with proofs for the estimators'asymptotic behavior.Moreover,the authors offer statistical tests to assess the temporal variation of covariate effects.The estimators'performance is evaluated by simulation experiments,and the authors apply this method to a platelet transfusion reactions dataset.展开更多
Designing relevant animal models in order to investigate the neurobiological basis for human mental disorders is an important challenge. The need for new tests to be developed and traditional tests to be improved has ...Designing relevant animal models in order to investigate the neurobiological basis for human mental disorders is an important challenge. The need for new tests to be developed and traditional tests to be improved has recently been em-phasized. The authors propose a multivariate test approach, the multivariate concentric square fieldTM (MCSF) test. To measure and evaluate variation in the behavioral traits, we here put forward a statistical procedure of which the working title is “trend analysis”. Low doses of the benzodiazepine agonist diazepam (DZP;1.0, 1.5, or 2.0 mg/kg) were used for exploring the use of the trend analysis in combination with multivariate data analysis for assessment of MCSF per-formance in rats. The commonly used elevated plus maze (EPM) test was used for comparison. The trend analysis comparing vehicle and the DZP1.5 groups revealed significantly higher general activity and risk-taking behavior in the DZP1.5 rats relative to vehicle rats. This finding was supported by multivariate data analysis procedures. It is concluded that the trend analysis together with multivariate data analysis procedures offers possibilities to extract information and illustrates effects obtained in the MCSF test. Diazepam in doses that have no apparent increase in open arm activity in the EPM was effective to alter the behavior in the MCSF test. The MCSF test and the use of multivariate data analysis and the proposed trend analysis may be useful alternatives to behavioral test batteries and traditionally used tests for the understanding of mechanisms underlying various mental states. Finally, the impact of an ethological reasoning and multivariate measures enabling behavioral profiling of animals may be a useful complementary methodology when phenotyping animals in behavioral neuroscience.展开更多
Bootstrap methods are considered in the application of statistical process control because they can deal with unknown distributions and are easy to calculate using a personal computer. In this study we propose the use...Bootstrap methods are considered in the application of statistical process control because they can deal with unknown distributions and are easy to calculate using a personal computer. In this study we propose the use of bootstrap-t multivariate control technique on the minimax control chart. The technique takes care of correlated variables as well as the requirement of the distributional assumptions needed for the operation of the minimax control chart. The bootstrap-t technique provides the mean θB of all the bootstrap estimators ** where θi is the estimate using the ith bootstrap sample and B is the number of bootstraps. The computation of the proposed bootstrap-t minimax statistic was performed on the values obtained from the bootstrap estimation. This method was used to determine the position of the four control limits of the minimax control chart. The bootstrap-t approach introduced to minimax multivariate control chart helps to detect shifts in the mean vector of a multivariate process and it overcomes the computational complexity of obtaining the distribution of multivariate data.展开更多
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
基金supported by Energy Cloud R&D Program(grant number:2019M3F2A1073184)through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT.
文摘Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
基金funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam "Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin" (Grant No. TNMT.2017.08.06)
文摘Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
基金supported by the National Key R&D Program of China(Project No.2016YFC0800200)the NRF-NSFC 3rd Joint Research Grant(Earth Science)(Project No.41861144022)+2 种基金the National Natural Science Foundation of China(Project Nos.51679174,and 51779189)the Shenzhen Key Technology R&D Program(Project No.20170324)The financial support is grateful acknowledged。
文摘Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.
文摘A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.
基金Supported by the National Natural Science Foundation of China (11171263)
文摘Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.
基金the Frontier Program of the Knowledge Innovation Program of Chinese Academy of Sciences
文摘Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.
基金Natural Science Foundation of China(T11036061/T0108).
文摘A novel study using LCeMS(Liquid chromatography tandem mass spectrometry)coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of Shixiao San.Batches of these two kinds of samples were subjected to analysis,and the datasets of sample codes,tR-m/z pairs and ion intensities were processed with principal component analysis(PCA).The result of score plot showed a clear classification of the aqueous and vinegar groups.And the chemical markers having great contributions to the differentiation were screened out on the loading plot.The identities of the chemical markers were performed by comparing the mass fragments and retention times with those of reference compounds and/or the known compounds published in the literatures.Based on the proposed strategy,quercetin-3-Oneohesperidoside,isorhamnetin-3-O-neohespeeridoside,kaempferol-3-O-neohesperidoside,isorhamnetin-3-O-rutinoside and isorhamnetin-3-O-(2G-a-l-rhamnosyl)-rutinoside were explored as representative markers in distinguishing the vinegar extract from the aqueous extract.The anti-hyperlipidemic activities of two processed extracts of Shixiao San were examined on serum levels of lipids,lipoprotein and blood antioxidant enzymes in a rat hyperlipidemia model,and the vinegary extract,exerting strong lipid-lowering and antioxidative effects,was superior to the aqueous extract.Therefore,boiling with vinegary was predicted as the greatest processing procedure for anti-hyperlipidemic effect of Shixiao San.Furthermore,combining the changes in the metabolic profiling and bioactivity evaluation,the five representative markers may be related to the observed antihyperlipidemic effect.
文摘Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
基金Supported by the National Natural Science Foundation of China (601133010)
文摘In this paper, a new approach for visualizing multivariate categorical data is presented. The approach uses a graph to represent multivariate categorical data and draws the graph in such a way that we can identify patterns, trends and relationship within the data. A mathematical model for the graph layout problem is deduced and a spectral graph drawing algorithm for visualizing multivariate categorical data is proposed. The experiments show that the drawings by the algorithm well capture the structures of multivariate categorical data and the computing speed is fast.
文摘The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.
文摘In the research of scientific field, it is often necessary to continuously observe different indicators of individuals at different times and analyze the observed results. Among them, variables are mainly of two types: ordered variables and continuous variables. When analyzing data for different types of variables, it is necessary to consider the correlation between multiple indicators of an individual, and often perform joint analysis on variable observation data of multiple indicators of an individual at different times, in order to achieve more accurate and true analysis results. Joint analysis often yields more information than separate analysis of various variables. In this paper, the ordered variable and the continuous variable are numerically modeled. Based on the potential variable model, the multivariate longitudinal data containing the ordered variable and the continuous variable are jointly analyzed, and the approximate value of the edge likelihood can be obtained by using the method of numerical integration.
基金Projects(61227006,61473206) supported by the National Natural Science Foundation of ChinaProject(13TXSYJC40200) supported by Science and Technology Innovation of Tianjin,China
文摘Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.
基金financial support of Isfahan University of Technology (IUT) for this research
文摘Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.
文摘With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the lack of equipment and network impacts,highly distributed industrial applications cannot capture and transfer all production data to a distant cloud server in real time.Consequently,a portion of critical production data is lost,which poses the significant challenge of the timely replenishment of missing data.Employing deep learning in the cloud center for data trend prediction based on relevant data can solve this problem.The objective of this study was to develop a time-series prediction model that combines a Transformer model with a sparse Mixture of Experts(MoE).The model is designed specifically for an IIoT system that is used in oil-well operations.The proposed TransMoE prediction model combines the advantages of the MoE and the Transformer model.The MoE can effectively handle multiple subtasks while the Transformer algorithm can reflect the long-range dependency of the input data series.The proposed model was used to predict oil-well yields,and the predicted outcomes were compared with those obtained using a CNN-GRU and CNNLSTM models,as well as the actual recorded data.The experimental results indicated that the proposed TransMoE model can significantly increase the efficiency and accuracy of oil well production sequence data prediction,with an average relative error of 6.26%,which can satisfy the requirements of enterprise data usage.
基金supported by the National Natural Science Foundation of China under Grant No.12301328the Talent Fund of Beijing Jiaotong University under Grant No.2023XKRC010。
文摘In clinical and observational research,multivariate recurrent event data are commonplace,as people may encounter several kinds of repeating occurrences.While event times are consistently recorded,there are instances where the corresponding event types might not be observed.For multivariate recurrent event data,the authors introduce a time-varying coefficients additive rate model which takes into consideration scenarios in which event types are missing at random(MAR).The authors employ an inverse probability-weighted estimating equation to derive inferences for the time-independent and time-dependent effects,with proofs for the estimators'asymptotic behavior.Moreover,the authors offer statistical tests to assess the temporal variation of covariate effects.The estimators'performance is evaluated by simulation experiments,and the authors apply this method to a platelet transfusion reactions dataset.
文摘Designing relevant animal models in order to investigate the neurobiological basis for human mental disorders is an important challenge. The need for new tests to be developed and traditional tests to be improved has recently been em-phasized. The authors propose a multivariate test approach, the multivariate concentric square fieldTM (MCSF) test. To measure and evaluate variation in the behavioral traits, we here put forward a statistical procedure of which the working title is “trend analysis”. Low doses of the benzodiazepine agonist diazepam (DZP;1.0, 1.5, or 2.0 mg/kg) were used for exploring the use of the trend analysis in combination with multivariate data analysis for assessment of MCSF per-formance in rats. The commonly used elevated plus maze (EPM) test was used for comparison. The trend analysis comparing vehicle and the DZP1.5 groups revealed significantly higher general activity and risk-taking behavior in the DZP1.5 rats relative to vehicle rats. This finding was supported by multivariate data analysis procedures. It is concluded that the trend analysis together with multivariate data analysis procedures offers possibilities to extract information and illustrates effects obtained in the MCSF test. Diazepam in doses that have no apparent increase in open arm activity in the EPM was effective to alter the behavior in the MCSF test. The MCSF test and the use of multivariate data analysis and the proposed trend analysis may be useful alternatives to behavioral test batteries and traditionally used tests for the understanding of mechanisms underlying various mental states. Finally, the impact of an ethological reasoning and multivariate measures enabling behavioral profiling of animals may be a useful complementary methodology when phenotyping animals in behavioral neuroscience.
文摘Bootstrap methods are considered in the application of statistical process control because they can deal with unknown distributions and are easy to calculate using a personal computer. In this study we propose the use of bootstrap-t multivariate control technique on the minimax control chart. The technique takes care of correlated variables as well as the requirement of the distributional assumptions needed for the operation of the minimax control chart. The bootstrap-t technique provides the mean θB of all the bootstrap estimators ** where θi is the estimate using the ith bootstrap sample and B is the number of bootstraps. The computation of the proposed bootstrap-t minimax statistic was performed on the values obtained from the bootstrap estimation. This method was used to determine the position of the four control limits of the minimax control chart. The bootstrap-t approach introduced to minimax multivariate control chart helps to detect shifts in the mean vector of a multivariate process and it overcomes the computational complexity of obtaining the distribution of multivariate data.
