Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the ...Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the polarization pattern in night environment with noise effects and model uncertainties is a less explored area.Although several decades have passed since the first publication about the polarization of the moonlit night sky,the usefulness of nocturnal polarization navigation is only sporadic in previous researches.This study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor environment.Based on the statistical characteristics of Angle of Polarization(Ao P)error,a probability density estimation method is proposed for heading determination.To illustrate the application potentials,the simulation and outdoor experiments are performed.Resultingly,the proposed method robustly models the distribution of Ao P error and gives accurate heading estimation evaluated by Standard Deviation(STD)which is 0.32°in a clear night sky and 0.47°in a cloudy night sky.展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probabilit...In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.展开更多
Supply–demand analysis is an important part of the planning of urban emergency shelters.Using Pudong New Area,Shanghai,China as an example,this study estimated daytime and nighttime population of the study area based...Supply–demand analysis is an important part of the planning of urban emergency shelters.Using Pudong New Area,Shanghai,China as an example,this study estimated daytime and nighttime population of the study area based on fine-scale land use data,census data,statistical yearbook information,and Tencent user-density big data.An exponential function-based,probability density estimation method was used to analyze the spatial supply of and demand for shelters under an earthquake scenario.The results show that even if all potential available shelters are considered,they still cannot satisfy the demand of the existing population for evacuation and sheltering,especially in the northern region of Pudong,under both the daytime and the nighttime scenarios.The proposed method can reveal the spatiotemporal imbalance between shelter supply and demand.We also conducted a preliminary location selection analysis of shelters based on the supply–demand analysis results.The location selection results demonstrate the advantage of the proposed method.It can be applied to identify the areas where the supply of shelters is seriously inadequate,and provide effective decision support for the planning of urban emergency shelters.展开更多
Background:Living cells need to undergo subtle shape adaptations in response to the topography of their substrates.These shape changes are mainly determined by reorganization of their internal cytoskeleton,with a majo...Background:Living cells need to undergo subtle shape adaptations in response to the topography of their substrates.These shape changes are mainly determined by reorganization of their internal cytoskeleton,with a major contribution from filamentous(F)actin.Bundles of F-actin play a major role in determining cell shape and their interaction with substrates,either as“stress fibers,”or as our newly discovered“Concave Actin Bundles”(CABs),which mainly occur while endothelial cells wrap micro-fibers in culture.Methods:To better understand the morphology and functions of these CABs,it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles,which is a demanding and time-consuming task.In this study,we present a novel algorithm to automatically recognize CABs without further human intervention.We developed and employed a multilayer perceptron artificial neural network(“the recognizer”),which was trained to identify CABs.Results:The recognizer demonstrated high overall recognition rate and reliability in both randomized training,and in subsequent testing experiments.Conclusion:It would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.展开更多
The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high dimensional climate models is an important topic for atmospheric low-frequency variability,climat...The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high dimensional climate models is an important topic for atmospheric low-frequency variability,climate sensitivity,and improved extended range forecasting.Recently,techniques from applied mathematics have been utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables.It was shown that dyad and multiplicative triad interactions combine with the climatological linear operator interactions to produce a normal form with both strong nonlinear cubic dissipation and Correlated Additive and Multiplicative(CAM) stochastic noise.The probability distribution functions(PDFs) of low frequency climate variables exhibit small but significant departure from Gaussianity but have asymptotic tails which decay at most like a Gaussian.Here,rigorous upper bounds with Gaussian decay are proved for the invariant measure of general normal form stochastic models.Asymptotic Gaussian lower bounds are also established under suitable hypotheses.展开更多
基金supported by National Natural Science Foundation of China(Nos.61627810,61751302,61833013 and 61973012)。
文摘Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the polarization pattern in night environment with noise effects and model uncertainties is a less explored area.Although several decades have passed since the first publication about the polarization of the moonlit night sky,the usefulness of nocturnal polarization navigation is only sporadic in previous researches.This study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor environment.Based on the statistical characteristics of Angle of Polarization(Ao P)error,a probability density estimation method is proposed for heading determination.To illustrate the application potentials,the simulation and outdoor experiments are performed.Resultingly,the proposed method robustly models the distribution of Ao P error and gives accurate heading estimation evaluated by Standard Deviation(STD)which is 0.32°in a clear night sky and 0.47°in a cloudy night sky.
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
基金supported by National High-tech Research and Development Program of China (No.2011AA7014061)
文摘In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.
基金funded by the National Natural Science Foundation of China(Grant Nos.41201548 and 5161101688)National Social Science Foundation of China(Grant No.18ZDA105)。
文摘Supply–demand analysis is an important part of the planning of urban emergency shelters.Using Pudong New Area,Shanghai,China as an example,this study estimated daytime and nighttime population of the study area based on fine-scale land use data,census data,statistical yearbook information,and Tencent user-density big data.An exponential function-based,probability density estimation method was used to analyze the spatial supply of and demand for shelters under an earthquake scenario.The results show that even if all potential available shelters are considered,they still cannot satisfy the demand of the existing population for evacuation and sheltering,especially in the northern region of Pudong,under both the daytime and the nighttime scenarios.The proposed method can reveal the spatiotemporal imbalance between shelter supply and demand.We also conducted a preliminary location selection analysis of shelters based on the supply–demand analysis results.The location selection results demonstrate the advantage of the proposed method.It can be applied to identify the areas where the supply of shelters is seriously inadequate,and provide effective decision support for the planning of urban emergency shelters.
文摘Background:Living cells need to undergo subtle shape adaptations in response to the topography of their substrates.These shape changes are mainly determined by reorganization of their internal cytoskeleton,with a major contribution from filamentous(F)actin.Bundles of F-actin play a major role in determining cell shape and their interaction with substrates,either as“stress fibers,”or as our newly discovered“Concave Actin Bundles”(CABs),which mainly occur while endothelial cells wrap micro-fibers in culture.Methods:To better understand the morphology and functions of these CABs,it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles,which is a demanding and time-consuming task.In this study,we present a novel algorithm to automatically recognize CABs without further human intervention.We developed and employed a multilayer perceptron artificial neural network(“the recognizer”),which was trained to identify CABs.Results:The recognizer demonstrated high overall recognition rate and reliability in both randomized training,and in subsequent testing experiments.Conclusion:It would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.
基金Project supported by the National Science Foundation Grant(No.DMS-0456713)the Office of Naval Research Grant(No.N0014-05-1-1064)
文摘The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high dimensional climate models is an important topic for atmospheric low-frequency variability,climate sensitivity,and improved extended range forecasting.Recently,techniques from applied mathematics have been utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables.It was shown that dyad and multiplicative triad interactions combine with the climatological linear operator interactions to produce a normal form with both strong nonlinear cubic dissipation and Correlated Additive and Multiplicative(CAM) stochastic noise.The probability distribution functions(PDFs) of low frequency climate variables exhibit small but significant departure from Gaussianity but have asymptotic tails which decay at most like a Gaussian.Here,rigorous upper bounds with Gaussian decay are proved for the invariant measure of general normal form stochastic models.Asymptotic Gaussian lower bounds are also established under suitable hypotheses.
