With rapid development of air transportation,the airspace structure of the future will need to be flexible and dynamic to accommodate the increase in traffic demand.The corridors-in-the-sky has become a new technology...With rapid development of air transportation,the airspace structure of the future will need to be flexible and dynamic to accommodate the increase in traffic demand.The corridors-in-the-sky has become a new technology to support the full exploitation and utilization of airspace resources.This paper proposes a method of designing corridor,identifying congestion state,and analyzing the influence of air routes’traffic flow.From this,we have reached a number of conclusions.(1)The congestion periods present the multi-peak"wavy"scattered distributions and the peaks back-end agglomeration characteristics in the whole day.(2)The congestion segments present the structural characteristics of unbalanced coverage and concentrated distribution to the crossing points.The corridors with high congestion level present as an italic"N-shaped"frame,which presents incomplete penetration of short segments.(3)For the temporal and spatial interaction,there are two types of congestion segments,and there are some common congestion periods in different congestion segments of multiple corridors.The high-density air route plays a relatively decisive role in corridor congestion,and the influence of two directions is unbalanced.This research can provide a basis for the dynamic evaluation of China’s airspace resources and corridors construction in the future.展开更多
In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into s...In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.展开更多
Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory r...Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory records generated each month in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate accurate movement-level trajectory-based performance estimations is the most time-consuming aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create maps;however, most proposed mapping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal performance measures. This paper presents a technique that uses commercial CV trajectory and open-source OpenStreetMap (OSM) data to automatically map intersection centers and approach areas of interest to estimate signal performance. OSM traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geometry characteristics surrounding the intersection. To demonstrate the proposed technique, intersection geometry is mapped at 500 locations from which trajectory-based traffic signal performance measures are estimated. The results are compared to those obtained from manual geometry definitions. Statistical tests found that at a 99% confidence level, upstream-focused performance estimations are strongly correlated between both methodologies. The presented technique will aid agencies in scaling traffic signal assessment as it significantly reduces the amount of manual labor required.展开更多
In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emissi...In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.展开更多
We generalized an constructing method of noncoherent unitary space time codes (N-USTC) over Rayleigh flat fading channels. A family of N-USTCs with T symbol peroids, M transmit and N receive antennas was constructed b...We generalized an constructing method of noncoherent unitary space time codes (N-USTC) over Rayleigh flat fading channels. A family of N-USTCs with T symbol peroids, M transmit and N receive antennas was constructed by the exponential mapping method based on the tangent subspace of the Grassmann manifold. This exponential mapping method can transform the coherent space time codes (C-STC) into the N-USTC on the Grassmann manifold. We infered an universal framework of constructing a C-STC that is designed by using the algebraic number theory and has full rate and full diversity (FRFD) for t symbol periods and same antennas, where M, N, T, t are general positive integer. We discussed the constraint condition that the exponential mapping has only one solution, from which we presented a approach of searching the optimum adjustive factor αopt that can generate an optimum noncoherent codeword. For different code parameters M, N, T, t and the optimum adjustive factor αopt, we gave the simulation results of the several N-USTCs.展开更多
Unmanned Aerial Vehicle(UAV) trajectory prediction is an important research topic in the field of UAV air combat. In order to address the problem of single-feature extraction scale and scene adaptability in UAV air co...Unmanned Aerial Vehicle(UAV) trajectory prediction is an important research topic in the field of UAV air combat. In order to address the problem of single-feature extraction scale and scene adaptability in UAV air combat trajectory prediction algorithms, this paper proposes an innovative UAV trajectory prediction method QCNet-3D, which can predict the future trajectory of the target UAV and provide the corresponding possibility. Firstly, the UAV trajectory prediction is modeled based on the mixture of Laplace distributions, and the UAV's kinetic equations are employed to construct the UAV trajectory prediction dataset(UAVTP dataset), ensuring high reliability. Secondly, two improvement methods are proposed on the basis of QCNet: multi-scale Fourier mapping and three-dimensional adaptation. The ablation study shows that the improvement methods have reduced the minimum average displacement error, minimum final displacement error, and missing rate by 55.4%, 54.3%, and 68.1% respectively. Finally, QCNet-3D is proposed based on the two improvement methods, and the simulation experiment confirm the proposed algorithm's capability to predict both simple and complex UAV maneuvers, offering the possibility for each predicted trajectory under various prediction future steps and output modes.展开更多
The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe.By July 2022,the number of cumulative confirmed cases reported to the World Health Organization(WHO)has ...The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe.By July 2022,the number of cumulative confirmed cases reported to the World Health Organization(WHO)has risen to 550 million,with more than 6 million deaths in total.The analysis of its epidemic risk remains the focus of attention all over the world for a long time.The Self-organizing feature map(SOM),a vector quantization method,offers a data mapping approach to tracking the response of time series data on a well-trained map.This study aims at a trajectory tracking of COVID-19 epidemic risk in 237 countries measured by the number of new confirmed cases and deaths per day for over one year.A hybrid clustering method uses SOM and K-means to generate a risk map and then displays the trajectory of daily risk on the map.The experimental results demonstrate the promising functionality of SOM for trajectory tracking and give experts insights into the dynamic changes of COVID-19 risk.展开更多
A force with an acceleration that is equal to multiples greater than the speed of light per unit time is exerted on a cloud of charged particles. The particles are resultantly accelerated to within an infinitesimal fr...A force with an acceleration that is equal to multiples greater than the speed of light per unit time is exerted on a cloud of charged particles. The particles are resultantly accelerated to within an infinitesimal fraction of the speed of light. As the force or acceleration increases, the particles’ velocity asymptotically approaches but never achieves the speed of light obeying relativity. The asymptotic increase in the particles’ velocity toward the speed of light as acceleration increasingly surpasses the speed of light per unit time does not compensate for the momentum value produced on the particles at sub-light velocities. Hence, the particles’ inertial mass value must increase as acceleration increases. This increase in the particles’ inertial mass as the particles are accelerated produce a gravitational field which is believed to occur in the oscillation of quarks achieving velocities close to the speed of light. The increased inertial mass of the density of accelerated charged particles becomes the source mass (or Big “M”) in Newton’s equation for gravitational force. This implies that a space-time curve is generated by the accelerated particles. Thus, it is shown that the acceleration number (or multiple of the speed of light greater than 1 per unit of time) and the number of charged particles in the cloud density are surjectively mapped to points on a differential manifold or space-time curved surface. Two aspects of Einstein’s field equations are used to describe the correspondence between the gravitational field produced by the accelerated particles and the resultant space-time curve. The two aspects are the Schwarzchild metric and the stress energy tensor. Lastly, the possibility of producing a sufficient acceleration or electromagnetic force on the charged particles to produce a gravitational field is shown through the Lorentz force equation. Moreover, it is shown that a sufficient voltage can be generated to produce an acceleration/force on the particles that is multiples greater than the speed of light per unit time thereby generating gravity.展开更多
Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the s...Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the states and behavior intentions of surrounding pedestrians.However,existing trajectory prediction methods remain failing to effectively model the diverse and complex interactions in the real world,including pedestrian-pedestrian interactions and pedestrian-environment interactions.Besides,these methods are not effective in capturing and characterizing the multimodal property of future trajectories.To address these challenges above,we propose to devise a handdesigned graph convolution and spatial cross attention to dynamically capture the diverse spatial interactions between pedestrians.To effectively explore the impact of scenarios on pedestrian trajectory,we build a pedestrian map,which can reflect the scene constraints and pedestrian motion preferences.Meanwhile,we construct a trajectory multimodality-aware module to capture the different potential mode implicit in diverse social behaviors for pedestrian future trajectory uncertainty.Finally,we compared the proposed method with trajectory prediction baselines on commonly used public pedestrian benchmarks,demonstrating the superior performance of our approach.展开更多
A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and contr...A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.展开更多
Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analy...Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.展开更多
A short note based on the homogeneous 5D space-time topological mappings is extended to cover DNAs of viruses and how the body’s immune system can be enhanced to recognize and remove it.
It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series...It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series involving all-atomic details and sub-picosecond time resolution, a few collective variables which characterizing the motions in longer than nanoseconds are needed to be chosen for an intuitive understanding of the dynamics of the system. The trajectory map (TM) was presented in our previous works to provide an efficient method to find the low-dimensional slow dynamic collective-motion modes from high-dimensional time series. In this paper, we present a more straight understanding about the principle of TM via the slow-mode linear space of the conformational probability distribution functions of MD trajectories and more clearly discuss the relation between the TM and the current other similar methods in finding slow modes.展开更多
基金National Natural Science Foundation of China,No.41671121
文摘With rapid development of air transportation,the airspace structure of the future will need to be flexible and dynamic to accommodate the increase in traffic demand.The corridors-in-the-sky has become a new technology to support the full exploitation and utilization of airspace resources.This paper proposes a method of designing corridor,identifying congestion state,and analyzing the influence of air routes’traffic flow.From this,we have reached a number of conclusions.(1)The congestion periods present the multi-peak"wavy"scattered distributions and the peaks back-end agglomeration characteristics in the whole day.(2)The congestion segments present the structural characteristics of unbalanced coverage and concentrated distribution to the crossing points.The corridors with high congestion level present as an italic"N-shaped"frame,which presents incomplete penetration of short segments.(3)For the temporal and spatial interaction,there are two types of congestion segments,and there are some common congestion periods in different congestion segments of multiple corridors.The high-density air route plays a relatively decisive role in corridor congestion,and the influence of two directions is unbalanced.This research can provide a basis for the dynamic evaluation of China’s airspace resources and corridors construction in the future.
基金supported in part by National Natural Science Foundation of China under Grants(61525101,61227801 and 61601055)in part by the National Key Technology R&D Program of China under Grant 2015ZX03002008
文摘In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.
文摘Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory records generated each month in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate accurate movement-level trajectory-based performance estimations is the most time-consuming aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create maps;however, most proposed mapping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal performance measures. This paper presents a technique that uses commercial CV trajectory and open-source OpenStreetMap (OSM) data to automatically map intersection centers and approach areas of interest to estimate signal performance. OSM traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geometry characteristics surrounding the intersection. To demonstrate the proposed technique, intersection geometry is mapped at 500 locations from which trajectory-based traffic signal performance measures are estimated. The results are compared to those obtained from manual geometry definitions. Statistical tests found that at a 99% confidence level, upstream-focused performance estimations are strongly correlated between both methodologies. The presented technique will aid agencies in scaling traffic signal assessment as it significantly reduces the amount of manual labor required.
文摘In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.
文摘We generalized an constructing method of noncoherent unitary space time codes (N-USTC) over Rayleigh flat fading channels. A family of N-USTCs with T symbol peroids, M transmit and N receive antennas was constructed by the exponential mapping method based on the tangent subspace of the Grassmann manifold. This exponential mapping method can transform the coherent space time codes (C-STC) into the N-USTC on the Grassmann manifold. We infered an universal framework of constructing a C-STC that is designed by using the algebraic number theory and has full rate and full diversity (FRFD) for t symbol periods and same antennas, where M, N, T, t are general positive integer. We discussed the constraint condition that the exponential mapping has only one solution, from which we presented a approach of searching the optimum adjustive factor αopt that can generate an optimum noncoherent codeword. For different code parameters M, N, T, t and the optimum adjustive factor αopt, we gave the simulation results of the several N-USTCs.
基金National Natural Science Foundation (NSF) of China (No.61976014)the Aeronautical Science Foundation of China (2022Z071051001)。
文摘Unmanned Aerial Vehicle(UAV) trajectory prediction is an important research topic in the field of UAV air combat. In order to address the problem of single-feature extraction scale and scene adaptability in UAV air combat trajectory prediction algorithms, this paper proposes an innovative UAV trajectory prediction method QCNet-3D, which can predict the future trajectory of the target UAV and provide the corresponding possibility. Firstly, the UAV trajectory prediction is modeled based on the mixture of Laplace distributions, and the UAV's kinetic equations are employed to construct the UAV trajectory prediction dataset(UAVTP dataset), ensuring high reliability. Secondly, two improvement methods are proposed on the basis of QCNet: multi-scale Fourier mapping and three-dimensional adaptation. The ablation study shows that the improvement methods have reduced the minimum average displacement error, minimum final displacement error, and missing rate by 55.4%, 54.3%, and 68.1% respectively. Finally, QCNet-3D is proposed based on the two improvement methods, and the simulation experiment confirm the proposed algorithm's capability to predict both simple and complex UAV maneuvers, offering the possibility for each predicted trajectory under various prediction future steps and output modes.
基金National Office of Philosophy and Social Sciences(19AZD019)National Ethnic Affairs Commission(2020-GMB-015).
文摘The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe.By July 2022,the number of cumulative confirmed cases reported to the World Health Organization(WHO)has risen to 550 million,with more than 6 million deaths in total.The analysis of its epidemic risk remains the focus of attention all over the world for a long time.The Self-organizing feature map(SOM),a vector quantization method,offers a data mapping approach to tracking the response of time series data on a well-trained map.This study aims at a trajectory tracking of COVID-19 epidemic risk in 237 countries measured by the number of new confirmed cases and deaths per day for over one year.A hybrid clustering method uses SOM and K-means to generate a risk map and then displays the trajectory of daily risk on the map.The experimental results demonstrate the promising functionality of SOM for trajectory tracking and give experts insights into the dynamic changes of COVID-19 risk.
文摘A force with an acceleration that is equal to multiples greater than the speed of light per unit time is exerted on a cloud of charged particles. The particles are resultantly accelerated to within an infinitesimal fraction of the speed of light. As the force or acceleration increases, the particles’ velocity asymptotically approaches but never achieves the speed of light obeying relativity. The asymptotic increase in the particles’ velocity toward the speed of light as acceleration increasingly surpasses the speed of light per unit time does not compensate for the momentum value produced on the particles at sub-light velocities. Hence, the particles’ inertial mass value must increase as acceleration increases. This increase in the particles’ inertial mass as the particles are accelerated produce a gravitational field which is believed to occur in the oscillation of quarks achieving velocities close to the speed of light. The increased inertial mass of the density of accelerated charged particles becomes the source mass (or Big “M”) in Newton’s equation for gravitational force. This implies that a space-time curve is generated by the accelerated particles. Thus, it is shown that the acceleration number (or multiple of the speed of light greater than 1 per unit of time) and the number of charged particles in the cloud density are surjectively mapped to points on a differential manifold or space-time curved surface. Two aspects of Einstein’s field equations are used to describe the correspondence between the gravitational field produced by the accelerated particles and the resultant space-time curve. The two aspects are the Schwarzchild metric and the stress energy tensor. Lastly, the possibility of producing a sufficient acceleration or electromagnetic force on the charged particles to produce a gravitational field is shown through the Lorentz force equation. Moreover, it is shown that a sufficient voltage can be generated to produce an acceleration/force on the particles that is multiples greater than the speed of light per unit time thereby generating gravity.
文摘Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the states and behavior intentions of surrounding pedestrians.However,existing trajectory prediction methods remain failing to effectively model the diverse and complex interactions in the real world,including pedestrian-pedestrian interactions and pedestrian-environment interactions.Besides,these methods are not effective in capturing and characterizing the multimodal property of future trajectories.To address these challenges above,we propose to devise a handdesigned graph convolution and spatial cross attention to dynamically capture the diverse spatial interactions between pedestrians.To effectively explore the impact of scenarios on pedestrian trajectory,we build a pedestrian map,which can reflect the scene constraints and pedestrian motion preferences.Meanwhile,we construct a trajectory multimodality-aware module to capture the different potential mode implicit in diverse social behaviors for pedestrian future trajectory uncertainty.Finally,we compared the proposed method with trajectory prediction baselines on commonly used public pedestrian benchmarks,demonstrating the superior performance of our approach.
基金supported in part by the Scientific Research Project of the Education Department of Jilin Province (JJKH20221020KJ)the National Natural Science Foundation of China (51408257)the Graduate Innovation Fund of Jilin University (101832020CX150)。
文摘A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.
文摘Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.
文摘A short note based on the homogeneous 5D space-time topological mappings is extended to cover DNAs of viruses and how the body’s immune system can be enhanced to recognize and remove it.
基金Project supported by the National Natural Science Foundation of China (Grant No. 11904086).
文摘It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series involving all-atomic details and sub-picosecond time resolution, a few collective variables which characterizing the motions in longer than nanoseconds are needed to be chosen for an intuitive understanding of the dynamics of the system. The trajectory map (TM) was presented in our previous works to provide an efficient method to find the low-dimensional slow dynamic collective-motion modes from high-dimensional time series. In this paper, we present a more straight understanding about the principle of TM via the slow-mode linear space of the conformational probability distribution functions of MD trajectories and more clearly discuss the relation between the TM and the current other similar methods in finding slow modes.
