Fourier series analysis is proposed as a new technique to address the problem of“sub-pixel motion”in deriving cloud motion winds(CMW)from high temporal resolution images.Based on a concept different from that of max...Fourier series analysis is proposed as a new technique to address the problem of“sub-pixel motion”in deriving cloud motion winds(CMW)from high temporal resolution images.Based on a concept different from that of maximum correlation matching technique,the Fourier technique computes phase speed as an estimate of cloud motion.It is very effective for tracking small cellular clouds in 1-min interval images and more efficient for computation than the maximum correlation technique because only two templates in same size are involved in primary tracking procedure. Moreover it obtains not only CMW vectors but potentially also velocity spectrum and variance.A practical example is given to show the cloud motion winds from 1-min interval images with the Fourier method versus those from traditional 30-min interval images with maximum correlation technique.Problems that require further investigation before the Fourier technique can be regarded as a viable technique,especially for cloud tracking with high temporal resolution images,are also revealed.展开更多
In quantum optics, unitary transformations of arbitrary states are evaluated by using the Taylor series expansion. However, this traditional approach can become cumbersome for the transformations involving non-commuti...In quantum optics, unitary transformations of arbitrary states are evaluated by using the Taylor series expansion. However, this traditional approach can become cumbersome for the transformations involving non-commuting operators. Addressing this issue, a nonstandard unitary transformation technique is highlighted here with new perspective. In a spirit of “quantum” series expansions, the transition probabilities between initial and final states, such as displaced, squeezed and other nonlinearly transformed coherent states are obtained both numerically and analytically. This paper concludes that, although this technique is novel, its implementations for more extended systems are needed.展开更多
We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes.A data structure representing a single episode is a m...We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes.A data structure representing a single episode is a multivariate time series.To analyse collections of episodes,we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes.Each episode is thus represented by a combination of patterns.Using this representation,we apply visual analytics techniques to fulfil a set of analysis tasks,such as investigation of the temporal distribution of the patterns,frequencies of transitions between the patterns in episode sequences,and co-occurrences of patterns of different variables within same episodes.We demonstrate our approach on two examples using real-world data,namely,dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover.展开更多
This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon.The pa...This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon.The paper focuses on two core challenges:identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior.The proposed approach combines existing methods for univariate pattern extraction,computation of temporal relations according to the Allen’s time interval algebra,visual displays of the temporal relations,and interactive query operations into a cohesive visual analytics workflow.The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match,illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.展开更多
Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as lo...Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.展开更多
气象数据是光伏出力预测的重要依据,气象数据的质量对预测的准确性至关重要。但某些分布式光伏系统缺乏数值天气预报,难以得到准确的气象信息预测。针对这一问题,提出一种基于多元时间序列图神经网络(multivariate time series graph ne...气象数据是光伏出力预测的重要依据,气象数据的质量对预测的准确性至关重要。但某些分布式光伏系统缺乏数值天气预报,难以得到准确的气象信息预测。针对这一问题,提出一种基于多元时间序列图神经网络(multivariate time series graph neural networks,MTGNN)的多元气象信息多步长预测方法,将多个种类的气象信息当作多元时间序列处理,每一类气象信息视作图的一个节点,利用图卷积模块负责将节点的信息与其邻居的信息融合,以处理空间依赖关系;利用时域卷积模块负责提取时间特征,最终实现多步长预测。最后利用某地气象装置采集的数据进行仿真验证,结果表明MTGNN的预测精度和稳定性相比于传统LSTM模型均有显著提高。展开更多
In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time se...In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time series data. Four study areas located in California,Texas, Kansas, and Minnesota, USA were selected for image compositingand land cover classification. Results indicated that images compositedusing weighted scoring-based algorithms have the best spatial fidelitycompared to other three algorithms. In addition, the weighted scoringbasedalgorithms have superior classification accuracy, followed bymedian, maximum NDVI, and medoid in descending order. However, themedian algorithm has a significant advantage in computational efficiencywhich was ~70 times that of weighted scoring-based algorithms, andwith overall classification accuracy just slightly lower (~0.13% onaverage) than weighted scoring-based algorithms. Therefore, werecommended the weighted scoring-based compositing algorithms forsmall area land cover mapping, and median compositing algorithm forthe land cover mapping of large area considering the balance betweencomputational complexity and classification accuracy. The findings of thisstudy provide insights into the performance difference between variouscompositing algorithms, and have potential uses for the selection ofpixel-based image compositing technique adopted for land covermapping based on Landsat time series data.展开更多
基金This study was partly supported by the National Basic Research of China:Project G1998040907.
文摘Fourier series analysis is proposed as a new technique to address the problem of“sub-pixel motion”in deriving cloud motion winds(CMW)from high temporal resolution images.Based on a concept different from that of maximum correlation matching technique,the Fourier technique computes phase speed as an estimate of cloud motion.It is very effective for tracking small cellular clouds in 1-min interval images and more efficient for computation than the maximum correlation technique because only two templates in same size are involved in primary tracking procedure. Moreover it obtains not only CMW vectors but potentially also velocity spectrum and variance.A practical example is given to show the cloud motion winds from 1-min interval images with the Fourier method versus those from traditional 30-min interval images with maximum correlation technique.Problems that require further investigation before the Fourier technique can be regarded as a viable technique,especially for cloud tracking with high temporal resolution images,are also revealed.
文摘In quantum optics, unitary transformations of arbitrary states are evaluated by using the Taylor series expansion. However, this traditional approach can become cumbersome for the transformations involving non-commuting operators. Addressing this issue, a nonstandard unitary transformation technique is highlighted here with new perspective. In a spirit of “quantum” series expansions, the transition probabilities between initial and final states, such as displaced, squeezed and other nonlinearly transformed coherent states are obtained both numerically and analytically. This paper concludes that, although this technique is novel, its implementations for more extended systems are needed.
基金supported by Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)EU in projects SoBigData++and CrexData,and by DFG within priority research program SPP VGI(project EVA-VGI).
文摘We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes.A data structure representing a single episode is a multivariate time series.To analyse collections of episodes,we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes.Each episode is thus represented by a combination of patterns.Using this representation,we apply visual analytics techniques to fulfil a set of analysis tasks,such as investigation of the temporal distribution of the patterns,frequencies of transitions between the patterns in episode sequences,and co-occurrences of patterns of different variables within same episodes.We demonstrate our approach on two examples using real-world data,namely,dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover.
基金supported by Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)by EU in projects SoBigData++and CrexData(grant agreement 101092749).
文摘This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon.The paper focuses on two core challenges:identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior.The proposed approach combines existing methods for univariate pattern extraction,computation of temporal relations according to the Allen’s time interval algebra,visual displays of the temporal relations,and interactive query operations into a cohesive visual analytics workflow.The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match,illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.
基金supported by the National Key R&D Program of China(grant number 2019YFC0507401)the National Natural Science Foundation of China(grant number 42371325).
文摘Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.
文摘气象数据是光伏出力预测的重要依据,气象数据的质量对预测的准确性至关重要。但某些分布式光伏系统缺乏数值天气预报,难以得到准确的气象信息预测。针对这一问题,提出一种基于多元时间序列图神经网络(multivariate time series graph neural networks,MTGNN)的多元气象信息多步长预测方法,将多个种类的气象信息当作多元时间序列处理,每一类气象信息视作图的一个节点,利用图卷积模块负责将节点的信息与其邻居的信息融合,以处理空间依赖关系;利用时域卷积模块负责提取时间特征,最终实现多步长预测。最后利用某地气象装置采集的数据进行仿真验证,结果表明MTGNN的预测精度和稳定性相比于传统LSTM模型均有显著提高。
基金supported by the National Natural Science Foundation of China[grant number 42271412].
文摘In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time series data. Four study areas located in California,Texas, Kansas, and Minnesota, USA were selected for image compositingand land cover classification. Results indicated that images compositedusing weighted scoring-based algorithms have the best spatial fidelitycompared to other three algorithms. In addition, the weighted scoringbasedalgorithms have superior classification accuracy, followed bymedian, maximum NDVI, and medoid in descending order. However, themedian algorithm has a significant advantage in computational efficiencywhich was ~70 times that of weighted scoring-based algorithms, andwith overall classification accuracy just slightly lower (~0.13% onaverage) than weighted scoring-based algorithms. Therefore, werecommended the weighted scoring-based compositing algorithms forsmall area land cover mapping, and median compositing algorithm forthe land cover mapping of large area considering the balance betweencomputational complexity and classification accuracy. The findings of thisstudy provide insights into the performance difference between variouscompositing algorithms, and have potential uses for the selection ofpixel-based image compositing technique adopted for land covermapping based on Landsat time series data.
