The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory strea...The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.展开更多
A theory for the two-stream free-electron laser（TSFEL） with a helical wiggler and an axial guide magnetic field is developed.In the analysis,the effects of self-fields are taken into account.An analysis of the two-s...A theory for the two-stream free-electron laser（TSFEL） with a helical wiggler and an axial guide magnetic field is developed.In the analysis,the effects of self-fields are taken into account.An analysis of the two-stream steady-state electron trajectories is given by solving the equation of motion.Numerical calculations show that there are seven groups of orbits in the presence of self-fields instead of two groups reported in the absence of self-fields.The stability of the trajectories is studied numerically.展开更多
The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regio...The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.展开更多
The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based...The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches.However,these systems often utilize storage by point or storage by trajectory methods,both of which have drawbacks.In this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries.We develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in Beijing.Ourfindings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.展开更多
Purpose–The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.Design/methodology/approach–To deal...Purpose–The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.Design/methodology/approach–To deal with dynamic obstacles for autonomous vehicles during parking,a long-and short-term mixed trajectory planning algorithm is proposed in this paper.In long term,considering obstacle behavior,A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory.In short term,this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model.Moreover,the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver.Findings–Compared with the spline optimization method,the results show that the proposed method can generate efficient obstacle avoidance strategies,safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units.Originality/value–It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.展开更多
Recently, big trajectory data streams are generated in distributed environments with the popularity of smartphones and other mobile devices. Distributed top?k similarity query, which finds k trajectories that are most...Recently, big trajectory data streams are generated in distributed environments with the popularity of smartphones and other mobile devices. Distributed top?k similarity query, which finds k trajectories that are most similar to a given query trajectory from all remote sites, is critical in this field. The key challenge in such a query is how to reduce the communication cost due to the limited network bandwidth resource. Although this query can be solved by sending the query trajectory to all the remote sites, in which the pairwise similarities are computed precisely. However, the overall cost, O(n·m),is huge when nor mis huge, where n is the size of query trajectory and m is the number of remote sites. Fortunately, there are some cheap ways to estimate pairwise similarity, which filter some trajectories in advance without precise computation. In order to overcome the challenge in this query, we devise two general frameworks, into which concrete distance measures can be plugged. The former one uses two bounds (the upper and lower bound), while the latter one only uses the lower bound. Moreover, we introduce detailed implementations of two representative distance measures, Euclidean and DTW distance, after inferring the lower and upper bound for the former framework and the lower bound for the latter one. Theoretical analysis and extensive experiments on real-world datasets evaluate the efficiency of proposed methods.展开更多
Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).H...Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.展开更多
基金This work is supported by the National Natural Science Foundationof China under Grants No. 41471371.
文摘The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.
基金Project supported by the Plasma Physics Research Center,Science and Research Branch,Islamic Azad University
文摘A theory for the two-stream free-electron laser（TSFEL） with a helical wiggler and an axial guide magnetic field is developed.In the analysis,the effects of self-fields are taken into account.An analysis of the two-stream steady-state electron trajectories is given by solving the equation of motion.Numerical calculations show that there are seven groups of orbits in the presence of self-fields instead of two groups reported in the absence of self-fields.The stability of the trajectories is studied numerically.
基金funded by the National Natural Science Foundation of China(42471329,42101306,42301102)the Natural Science Foundation of Shandong Province(ZR2021MD047)+1 种基金the Scientific Innovation Project for Young Scientists in Shandong Provincial Universities(2022KJ224)the Gansu Youth Science and Technology Fund Program(24JRRA100).
文摘The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.
基金support from the National Natural Science Foundation of China(42271471,42201454,41830645)the International Research Center of Big Data for Sustainable Development Goals(CBAS2022GSP06).
文摘The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches.However,these systems often utilize storage by point or storage by trajectory methods,both of which have drawbacks.In this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries.We develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in Beijing.Ourfindings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.
基金the National Natural Science Foundation of China(Nos.51875184 and 52002163).
文摘Purpose–The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.Design/methodology/approach–To deal with dynamic obstacles for autonomous vehicles during parking,a long-and short-term mixed trajectory planning algorithm is proposed in this paper.In long term,considering obstacle behavior,A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory.In short term,this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model.Moreover,the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver.Findings–Compared with the spline optimization method,the results show that the proposed method can generate efficient obstacle avoidance strategies,safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units.Originality/value–It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.
文摘Recently, big trajectory data streams are generated in distributed environments with the popularity of smartphones and other mobile devices. Distributed top?k similarity query, which finds k trajectories that are most similar to a given query trajectory from all remote sites, is critical in this field. The key challenge in such a query is how to reduce the communication cost due to the limited network bandwidth resource. Although this query can be solved by sending the query trajectory to all the remote sites, in which the pairwise similarities are computed precisely. However, the overall cost, O(n·m),is huge when nor mis huge, where n is the size of query trajectory and m is the number of remote sites. Fortunately, there are some cheap ways to estimate pairwise similarity, which filter some trajectories in advance without precise computation. In order to overcome the challenge in this query, we devise two general frameworks, into which concrete distance measures can be plugged. The former one uses two bounds (the upper and lower bound), while the latter one only uses the lower bound. Moreover, we introduce detailed implementations of two representative distance measures, Euclidean and DTW distance, after inferring the lower and upper bound for the former framework and the lower bound for the latter one. Theoretical analysis and extensive experiments on real-world datasets evaluate the efficiency of proposed methods.
基金supports for this work,provided by the National Natural Science Foundation of China(Grant No.61972153)the National Key Research and Development Program(No.2018YFE0101000)+1 种基金the Key projects of the Ministry of Science and Technology(No.2020AAA0107800)are gratefully acknowledged.
文摘Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.
