Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned a...Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.展开更多
With the continuous growth of the demand for container transportation,the proportion of container trucks passing through ports and surrounding roads has increased significantly.Due to their large size and poor maneuve...With the continuous growth of the demand for container transportation,the proportion of container trucks passing through ports and surrounding roads has increased significantly.Due to their large size and poor maneuverability,once a truck accident occurs,it is often accompanied by serious casualties and property losses.Current research on traffic conflicts for container trucks is limited by the lack of high-quality data:first,publicly available container truck trajectory datasets are extremely rare;second,although drones have the ability to collect data over a large range,their shooting data have problems such as limited accuracy and discontinuous trajectories,which make it difficult to meet the high requirements of micro-modeling for data quality.This problem directly restricts the accuracy of conflict prediction and the credibility of causal analysis.To improve the accuracy and completeness of trajectory data,we introduce a trajectory reconstruction method to repair and complete the original trajectory.The experimental results show that the reconstructed trajectory is significantly better than the original data in terms of continuity and rationality.On this basis,a two-dimensional time to collision(2D-TTC)indicator was constructed to identify side-swipe conflict events,and based on the extraction of micro-behavior features,sample sets of sideswipe and rear-end conflicts were constructed,and a variety of machine learning models were introduced to carry out conflict prediction analysis.The results show that the gradient boosting decision tree(GBDT)model performs best in side-swipe conflict prediction,and the extreme gradient boosting(XGBoost)model in rear-end conflict prediction.By introducing the Shapley additive explanation(SHAP)method to improve the interpretability of the model,our analysis shows that the key factors influencing side-swipe conflict are the lateral speed and the average longitudinal speed within 5 s.The lateral speed reflects the lateral deviation of the vehicle,and the average longitudinal speed within 5 s reflects the driving stability and acceleration trend in a short time.The two together determine the lateral controllability of the vehicle in the dynamic process.Rear-end conflict is affected mainly by the change in longitudinal acceleration,revealing the instability of the vehicle during braking and the lack of control over the distance between the vehicle and the preceding vehicle.Finally,the model performance was optimized through feature ablation experiments,where,in the prediction of side-swipe conflicts,the GBDT achieved an accuracy of 0.911 and an area under the receiver operating characteristic curve(AUC)of 0.953.展开更多
The Tianwen-1 Mars entry vehicle successfully landed on the surface of Mars in southern Utopia Planitia on May 15,2021,at 7:18(UTC+8).To acquire valuable Martian flight data,a scientific instrumentation package consis...The Tianwen-1 Mars entry vehicle successfully landed on the surface of Mars in southern Utopia Planitia on May 15,2021,at 7:18(UTC+8).To acquire valuable Martian flight data,a scientific instrumentation package consisting of a flush air data system and a multilayer temperature-sensing system was installed aboard the entry vehicle.A combined approach was applied in the entry,descent,and landing trajectory reconstruction using all available data obtained by the inertial measurement unit and the flush air data system.An aerodynamic database covering the entire flight regime was generated using computational fluid dynamics methods to assist in the reconstruction process.A preliminary analysis of the trajectory reconstruction result,along with the atmosphere reconstruction and aerodynamic performance,was conducted.The results show that the trajectory agrees closely with the nominal trajectory and the wind-relative attitude.Suspected wind occurred at the end of the trajectory.展开更多
The effects of pressure oscillation on aerodynamic characteristics in an aero-engine combustor are investigated. A combustor test rig is designed to simulate the pressure drop characteristics of a practical annular co...The effects of pressure oscillation on aerodynamic characteristics in an aero-engine combustor are investigated. A combustor test rig is designed to simulate the pressure drop characteristics of a practical annular combustor. The pressure drop characteristics are firstly measured under atmosphere condition with non-reacting flow(or cold flow), and the air mass flow proportion of each component(dome/liner) are obtained;these properties are base lines for comparison with combustion state. The combustion tests are then carried out under conditions of inlet temperature 340–450 K, fuel air ratio 0.010–0.028. The stability map and the oscillation frequencies are obtained in the tests, the results show that pressure oscillation amplitude increases with the increase of fuel air ratio. Phase trajectory reconstruction is applied to classify the pressure oscillation motion;there are three motions captured in the tests including: ‘‘disk", ‘‘ring" and ‘‘cluster". The pressure drops across the dome under strong pressure oscillation are distinctly divergent from the cold flow, and the changes of pressure drops are mainly affected by pressure oscillation amplitude, but is less influenced by pressure oscillation motion nor oscillation frequencies. Based on the mass flow conservation, the reduction of effective flow area of combustor under strong pressure oscillation is demonstrated. Liner wall temperatures are analyzed through Multiple Linear Regression(MLR)method to estimate the reduction of the air mass flow proportion of the liner cooling under strong pressure oscillation. Finally, the air mass flow proportions of each component under strong pressure oscillation are estimated, the results show that the pressure oscillation motion also has influence on air mass flow proportion.展开更多
Sparse trajectory data with non-second-by-second sampling intervals are common.However,most carbon emission estimation models for vehicles require second-bysecond inputs.Additionally,some models ignore the emission ge...Sparse trajectory data with non-second-by-second sampling intervals are common.However,most carbon emission estimation models for vehicles require second-bysecond inputs.Additionally,some models ignore the emission generation principle,and some have complicated inputs.To address these limitations,this study proposes a vehicle carbon emission estimation method for urban traffic,based on sparse trajectory data.First,a trajectory reconstruction method based on interpolation of acceleration distribution is proposed.The results showed that the reconstructed trajectory was close to the real trajectory,and the accuracy was 2%-17%higher than that of other methods.Second,a carbon emission estimation model that considers both the emission generation principle and feasibility is proposed.The model with a goodness-of-fit of 0.887 had the best performance compared to the other models.The emission estimation results of the reconstructed sparse trajectories showed that the precision improved significantly for data with different frequencies compared to that of other reconstruction methods,e.g.,9%higher at a 30 s sampling interval.展开更多
The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper propos...The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive simulation.The proposed algorithm perform extrapolation using a number of candidate predictors.Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories.The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.展开更多
基金supported by the Hunan Provin〓〓cial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the National Natural Science Foundation of China(Grant No.12372189)。
文摘Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.
基金supported by the Program of Humanities and Social Science of the Education Ministry of China(No.24YJA630013)the Ningbo Natural Science Foundation of China(No.2024J125)the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H032),China.
文摘With the continuous growth of the demand for container transportation,the proportion of container trucks passing through ports and surrounding roads has increased significantly.Due to their large size and poor maneuverability,once a truck accident occurs,it is often accompanied by serious casualties and property losses.Current research on traffic conflicts for container trucks is limited by the lack of high-quality data:first,publicly available container truck trajectory datasets are extremely rare;second,although drones have the ability to collect data over a large range,their shooting data have problems such as limited accuracy and discontinuous trajectories,which make it difficult to meet the high requirements of micro-modeling for data quality.This problem directly restricts the accuracy of conflict prediction and the credibility of causal analysis.To improve the accuracy and completeness of trajectory data,we introduce a trajectory reconstruction method to repair and complete the original trajectory.The experimental results show that the reconstructed trajectory is significantly better than the original data in terms of continuity and rationality.On this basis,a two-dimensional time to collision(2D-TTC)indicator was constructed to identify side-swipe conflict events,and based on the extraction of micro-behavior features,sample sets of sideswipe and rear-end conflicts were constructed,and a variety of machine learning models were introduced to carry out conflict prediction analysis.The results show that the gradient boosting decision tree(GBDT)model performs best in side-swipe conflict prediction,and the extreme gradient boosting(XGBoost)model in rear-end conflict prediction.By introducing the Shapley additive explanation(SHAP)method to improve the interpretability of the model,our analysis shows that the key factors influencing side-swipe conflict are the lateral speed and the average longitudinal speed within 5 s.The lateral speed reflects the lateral deviation of the vehicle,and the average longitudinal speed within 5 s reflects the driving stability and acceleration trend in a short time.The two together determine the lateral controllability of the vehicle in the dynamic process.Rear-end conflict is affected mainly by the change in longitudinal acceleration,revealing the instability of the vehicle during braking and the lack of control over the distance between the vehicle and the preceding vehicle.Finally,the model performance was optimized through feature ablation experiments,where,in the prediction of side-swipe conflicts,the GBDT achieved an accuracy of 0.911 and an area under the receiver operating characteristic curve(AUC)of 0.953.
基金The authors are grateful to Ying Li for extraction of the raw pressure and temperature data,Fajun Yi for calibration of pressure sensors,Minwen Guo for providing inertial data,and Francois Forget and Millour Ehouarn for collaboration in the Martian atmospheric model.
文摘The Tianwen-1 Mars entry vehicle successfully landed on the surface of Mars in southern Utopia Planitia on May 15,2021,at 7:18(UTC+8).To acquire valuable Martian flight data,a scientific instrumentation package consisting of a flush air data system and a multilayer temperature-sensing system was installed aboard the entry vehicle.A combined approach was applied in the entry,descent,and landing trajectory reconstruction using all available data obtained by the inertial measurement unit and the flush air data system.An aerodynamic database covering the entire flight regime was generated using computational fluid dynamics methods to assist in the reconstruction process.A preliminary analysis of the trajectory reconstruction result,along with the atmosphere reconstruction and aerodynamic performance,was conducted.The results show that the trajectory agrees closely with the nominal trajectory and the wind-relative attitude.Suspected wind occurred at the end of the trajectory.
文摘The effects of pressure oscillation on aerodynamic characteristics in an aero-engine combustor are investigated. A combustor test rig is designed to simulate the pressure drop characteristics of a practical annular combustor. The pressure drop characteristics are firstly measured under atmosphere condition with non-reacting flow(or cold flow), and the air mass flow proportion of each component(dome/liner) are obtained;these properties are base lines for comparison with combustion state. The combustion tests are then carried out under conditions of inlet temperature 340–450 K, fuel air ratio 0.010–0.028. The stability map and the oscillation frequencies are obtained in the tests, the results show that pressure oscillation amplitude increases with the increase of fuel air ratio. Phase trajectory reconstruction is applied to classify the pressure oscillation motion;there are three motions captured in the tests including: ‘‘disk", ‘‘ring" and ‘‘cluster". The pressure drops across the dome under strong pressure oscillation are distinctly divergent from the cold flow, and the changes of pressure drops are mainly affected by pressure oscillation amplitude, but is less influenced by pressure oscillation motion nor oscillation frequencies. Based on the mass flow conservation, the reduction of effective flow area of combustor under strong pressure oscillation is demonstrated. Liner wall temperatures are analyzed through Multiple Linear Regression(MLR)method to estimate the reduction of the air mass flow proportion of the liner cooling under strong pressure oscillation. Finally, the air mass flow proportions of each component under strong pressure oscillation are estimated, the results show that the pressure oscillation motion also has influence on air mass flow proportion.
基金supported by the National Natural Science Foundation of China(No.52131204,52325210,and 52102415)the Fundamental Research Funds for the Central Universities(2023-4-YB-05).
文摘Sparse trajectory data with non-second-by-second sampling intervals are common.However,most carbon emission estimation models for vehicles require second-bysecond inputs.Additionally,some models ignore the emission generation principle,and some have complicated inputs.To address these limitations,this study proposes a vehicle carbon emission estimation method for urban traffic,based on sparse trajectory data.First,a trajectory reconstruction method based on interpolation of acceleration distribution is proposed.The results showed that the reconstructed trajectory was close to the real trajectory,and the accuracy was 2%-17%higher than that of other methods.Second,a carbon emission estimation model that considers both the emission generation principle and feasibility is proposed.The model with a goodness-of-fit of 0.887 had the best performance compared to the other models.The emission estimation results of the reconstructed sparse trajectories showed that the precision improved significantly for data with different frequencies compared to that of other reconstruction methods,e.g.,9%higher at a 30 s sampling interval.
基金the research Project of State Key Laboratory of High Performance computing of National University of Defense Technology(No.201303-05).
文摘The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive simulation.The proposed algorithm perform extrapolation using a number of candidate predictors.Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories.The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.
