Aircraft icing has been proven to be one of the most serious threats to flight safety. During the analysis of flight risk under icing conditions, quantitative assessment and visualization of flight risk are quite esse...Aircraft icing has been proven to be one of the most serious threats to flight safety. During the analysis of flight risk under icing conditions, quantitative assessment and visualization of flight risk are quite essential as they provide safe manipulation strategies in intricate conditions.However, they are rarely studied. Since the icing flight accidents are the result of the coupling of multiple unfavorable factors, in present study, we have proposed a method to quantitatively assess flight risk induced by multi-factor coupling under icing conditions by Monte-Carlo simulation and multivariate extreme value theory. The results demonstrate that the flight risk probability increases with the rise of unfavorable factors. Besides, a flight risk visualization method named flight safety window has been presented to build the flight risk distribution cloud maps in different complex conditions. The cloud maps show that the icing would give rise to atrophy of the safety scope, and the consequence would be even more severe when coupled with other more unfavorable factors. The proposed methods in this study would be useful in flight risk analysis under icing conditions and can enhance the pilot's situational awareness in selecting correct strategies within the safety zone to avoid unsafe manipulation.展开更多
Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a mul...Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.展开更多
There are many sources of geographic big data,and most of them come from heterogeneous environments.The data sources obtained in this case contain attribute information of different spatial scales,different time scale...There are many sources of geographic big data,and most of them come from heterogeneous environments.The data sources obtained in this case contain attribute information of different spatial scales,different time scales and different complexity levels.It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult.In this case,visualizing high-dimensional spatiotemporal trajectory data is extremely challenging.Therefore,i-tStar and its extension i-tStar(3D)proposed,a trajectory behavior feature formoving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions,and evaluate our approach through case studies of an open-pit mine truck dataset.The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.展开更多
Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interest...Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.展开更多
We propose a new model based on the convolutional networks and SAX(Symbolic Aggregate Approximation)discretization to learn the representation for multivariate time series.The deep neural networks has excellent expres...We propose a new model based on the convolutional networks and SAX(Symbolic Aggregate Approximation)discretization to learn the representation for multivariate time series.The deep neural networks has excellent expressiveness,which is fully exploited by the convolutional networks with means of unsupervised learning.We design a network structure to obtain the cross-channel correlation with means of convolution and deconvolution,the pooling operation is utilized to perform the dimension reduction along each position of the channels.Discretization which based on the Symbolic Aggregate Approximation is applied on the feature vectors to extract the bag of features.We collect two different representations from the convolutional networks,the compression from bottle neck and the last convolutional layers.We show how these representations and bag of features can be useful for classification.We provide a full comparison with the sequence distance based approach on the standard datasets to demonstrate the effectiveness of our method.We further build the Markov matrix according to the discretized representation abstracted from the deconvolution,time series is visualized to complex networks through Markov matrix visualization,which show more class-specific statistical properties and clear structures with respect to different labels.展开更多
基金supported by the National Key Basic Research Program of China (No. 2015CB755802)。
文摘Aircraft icing has been proven to be one of the most serious threats to flight safety. During the analysis of flight risk under icing conditions, quantitative assessment and visualization of flight risk are quite essential as they provide safe manipulation strategies in intricate conditions.However, they are rarely studied. Since the icing flight accidents are the result of the coupling of multiple unfavorable factors, in present study, we have proposed a method to quantitatively assess flight risk induced by multi-factor coupling under icing conditions by Monte-Carlo simulation and multivariate extreme value theory. The results demonstrate that the flight risk probability increases with the rise of unfavorable factors. Besides, a flight risk visualization method named flight safety window has been presented to build the flight risk distribution cloud maps in different complex conditions. The cloud maps show that the icing would give rise to atrophy of the safety scope, and the consequence would be even more severe when coupled with other more unfavorable factors. The proposed methods in this study would be useful in flight risk analysis under icing conditions and can enhance the pilot's situational awareness in selecting correct strategies within the safety zone to avoid unsafe manipulation.
基金Partial support for this research was provided by the US National Science Foundation (Nos. 1050477, 0959979, and 1117132)by a Brookhaven National Lab LDRD grant+2 种基金by the US Department of Energy (DOE) Office of Basic Energy Sciences, Division of Chemical Sciences, GeosciencesBiosciences and by the IT Consilience Creative Project through the Ministry of Knowledge Economy, Republic of Korea national scientific user facility sponsored by the DOE's OBER at Pacific Northwest National Laboratory (PNNL)PNNL is operated by the US DOE by Battelle Memorial Institute under contract No.DE-AC06-76RL0 1830
文摘Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.
基金Beijing Key Laboratory of Urban Spatial Information Engineering,Grant No.20220105Ningxia Natural Science Foundation,No.2021AAC03060。
文摘There are many sources of geographic big data,and most of them come from heterogeneous environments.The data sources obtained in this case contain attribute information of different spatial scales,different time scales and different complexity levels.It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult.In this case,visualizing high-dimensional spatiotemporal trajectory data is extremely challenging.Therefore,i-tStar and its extension i-tStar(3D)proposed,a trajectory behavior feature formoving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions,and evaluate our approach through case studies of an open-pit mine truck dataset.The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.
文摘Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
基金the International Cooperation Project of the Department of Science&Technology of Henan Province under Grant No.172102410065Basic Research Project of the Education Department of Henan Province under Grant No.17A520057Frontier Interdisciplinary Project of Zhengzhou University under Grant No.XKZDQY202010.
文摘We propose a new model based on the convolutional networks and SAX(Symbolic Aggregate Approximation)discretization to learn the representation for multivariate time series.The deep neural networks has excellent expressiveness,which is fully exploited by the convolutional networks with means of unsupervised learning.We design a network structure to obtain the cross-channel correlation with means of convolution and deconvolution,the pooling operation is utilized to perform the dimension reduction along each position of the channels.Discretization which based on the Symbolic Aggregate Approximation is applied on the feature vectors to extract the bag of features.We collect two different representations from the convolutional networks,the compression from bottle neck and the last convolutional layers.We show how these representations and bag of features can be useful for classification.We provide a full comparison with the sequence distance based approach on the standard datasets to demonstrate the effectiveness of our method.We further build the Markov matrix according to the discretized representation abstracted from the deconvolution,time series is visualized to complex networks through Markov matrix visualization,which show more class-specific statistical properties and clear structures with respect to different labels.
