Transparent flow field visualization techniques play a critical role in engineering and scientific applications.They provide a clear and intuitive means to understand fluid dynamics and its complex phenomena,such as l...Transparent flow field visualization techniques play a critical role in engineering and scientific applications.They provide a clear and intuitive means to understand fluid dynamics and its complex phenomena,such as laminar flow,turbulence,and vortices.However,achieving fully two-dimensional quantitative visualization of transparent flow fields under non-invasive conditions remains a significant challenge.Here,we present an approach for achieving flow field visualization by harnessing the synergistic effects of a dielectric metasurface array endowed with photonic spindecoupled capability.This approach enables the simultaneous acquisition of light-field images containing flow field information in two orthogonal dimensions,which allows for the real-time and quantitative derivation of multiple physical parameters.As a proof-of-concept,we experimentally demonstrate the applicability of the proposed visualization technique to various scenarios,including temperature field mapping,gas leak detection,visualization of various fluid physical phenomena,and 3D morphological reconstruction of transparent phase objects.This technique not only establishes an exceptional platform for advancing research in fluid physics,but also exhibits significant potential for broad applications in industrial design and vision.展开更多
Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77...Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.展开更多
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime...In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.展开更多
Although many of the first-generation Digital Earth systems have proven to be quite useful for the modeling and visualization of geospatial objects relevant to the Earth＇s surface and near-surface, they were not desi...Although many of the first-generation Digital Earth systems have proven to be quite useful for the modeling and visualization of geospatial objects relevant to the Earth＇s surface and near-surface, they were not designed for the purpose of modeling and application in geological or atmospheric space. There is a pressing need for a new Digital Earth system that can process geospatial information with full dimensionality. In this paper, we present a new Digital Earth system, termed SolidEarth, as an alternative virtual globe for the modeling and visualization of the whole Earth space including its surface, interior, and exterior space. SolidEarth consists of four functional components： modeling in geographical space, modeling in geological space, modeling in atmo- spheric space, and, integrated visualization and analysis. SolidEarth has a comprehensive treatment to the third spatial dimension and a series of sophisticated 3D spatial analysis functions. Therefore, it is well-suited to the volumetric representation and visual analysis of the inner/ outer spheres in Earth space. SolidEarth can be used in a number of fields such as geoscience research and education, the construction of Digital Earth applications, and other professional practices of Earth science.展开更多
By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or a...By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.展开更多
The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people an...The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people and the environment in which we live.Such datasets are often high dimensional and heterogeneous in nature,with complex geospatial relations.Analysing such data can be challenging,especially when there is a need to maintain spatial awareness as the non-spatial attributes are studied.Geo-Coordinated Parallel Coordinates(GCPC)is a geovisual analytics approach designed to support exploration and analysis within complex geospatial environmental data.Parallel coordinates are tightly coupled with a geospatial representation and an investigative scatterplot,all of which can be used to show,reorganize,filter,and highlight the high dimensional,heterogeneous,and geospatial aspects of the data.Two sets of field trials were conducted with expert data analysts to validate the real-world benefits of the approach for studying environmental data.The results of these evaluations were positive,providing real-world evidence and new insights regarding the value of using GCPC to explore among environmental datasets when there is a need to remain aware of the geospatial aspects of the data as the non-spatial elements are studied.展开更多
基金support from the Key Research and Development Program of the Ministry of Science and Technology of China(2022YFA1205000)the National Natural Science Foundation of China(12274217,12104225)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20220068)Fundamental Research Funds for the Central UniversitiesThe authors acknowledge the technique support from the microfabrication center of the National Laboratory of Solid-State Microstructures.
文摘Transparent flow field visualization techniques play a critical role in engineering and scientific applications.They provide a clear and intuitive means to understand fluid dynamics and its complex phenomena,such as laminar flow,turbulence,and vortices.However,achieving fully two-dimensional quantitative visualization of transparent flow fields under non-invasive conditions remains a significant challenge.Here,we present an approach for achieving flow field visualization by harnessing the synergistic effects of a dielectric metasurface array endowed with photonic spindecoupled capability.This approach enables the simultaneous acquisition of light-field images containing flow field information in two orthogonal dimensions,which allows for the real-time and quantitative derivation of multiple physical parameters.As a proof-of-concept,we experimentally demonstrate the applicability of the proposed visualization technique to various scenarios,including temperature field mapping,gas leak detection,visualization of various fluid physical phenomena,and 3D morphological reconstruction of transparent phase objects.This technique not only establishes an exceptional platform for advancing research in fluid physics,but also exhibits significant potential for broad applications in industrial design and vision.
基金by the National Natural Science Foundation of China(No.52174114)the State Key Laboratory of Hydroscience and Engineering of Tsinghua University(No.61010101218).
文摘Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.
基金the National Natural Science Foundation of China(No.61772417,61634004,61602377)Key R&D Program Projects in Shaanxi Province(No.2017GY-060)Shaanxi Natural Science Basic Research Project(No.2018JM4018).
文摘In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.
基金Acknowledgements This research was supported by the National Science and Technology Program of China （Grant No. SinoProbe-08）, the National Natural Science Foundation of China （Grant No. 40902093）, the National Social Science Foundation of China （Grant No. 07CZZ019）, the Development Foundation of Experimental Teaching Equipment in East China Normal University （Grant No. 64100010） and the Open Foundation of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration （Grant No. SHUES2011A06）. We would like to thank the Editor and two anonymous reviewers for their helpful and constructive suggestions for improving the paper.
文摘Although many of the first-generation Digital Earth systems have proven to be quite useful for the modeling and visualization of geospatial objects relevant to the Earth＇s surface and near-surface, they were not designed for the purpose of modeling and application in geological or atmospheric space. There is a pressing need for a new Digital Earth system that can process geospatial information with full dimensionality. In this paper, we present a new Digital Earth system, termed SolidEarth, as an alternative virtual globe for the modeling and visualization of the whole Earth space including its surface, interior, and exterior space. SolidEarth consists of four functional components： modeling in geographical space, modeling in geological space, modeling in atmo- spheric space, and, integrated visualization and analysis. SolidEarth has a comprehensive treatment to the third spatial dimension and a series of sophisticated 3D spatial analysis functions. Therefore, it is well-suited to the volumetric representation and visual analysis of the inner/ outer spheres in Earth space. SolidEarth can be used in a number of fields such as geoscience research and education, the construction of Digital Earth applications, and other professional practices of Earth science.
文摘By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.
基金This work was supported in part by grant from Social Sciences and Humanities Research Council of Canada(SSHRC)(895-2011-1011)held by the second author.
文摘The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people and the environment in which we live.Such datasets are often high dimensional and heterogeneous in nature,with complex geospatial relations.Analysing such data can be challenging,especially when there is a need to maintain spatial awareness as the non-spatial attributes are studied.Geo-Coordinated Parallel Coordinates(GCPC)is a geovisual analytics approach designed to support exploration and analysis within complex geospatial environmental data.Parallel coordinates are tightly coupled with a geospatial representation and an investigative scatterplot,all of which can be used to show,reorganize,filter,and highlight the high dimensional,heterogeneous,and geospatial aspects of the data.Two sets of field trials were conducted with expert data analysts to validate the real-world benefits of the approach for studying environmental data.The results of these evaluations were positive,providing real-world evidence and new insights regarding the value of using GCPC to explore among environmental datasets when there is a need to remain aware of the geospatial aspects of the data as the non-spatial elements are studied.
