All lunar theories within the framework of classical mechanics are based on the assumption of an inertial frame of reference,which is physically non-existent and has scarcely been examined in previous studies.This pap...All lunar theories within the framework of classical mechanics are based on the assumption of an inertial frame of reference,which is physically non-existent and has scarcely been examined in previous studies.This paper aims to examine the consequence of giving up the assumption of the existence of an inertial frame and the relevant influence on the measured moon trajectories.The general formulation of the lunar orbit is built from the perspective of the new general system theory(NGST),where the moon is modelled as a 6-DOF living body and active forces are introduced.This formulation can be retrograded to the framework of classical mechanics.Firstly,we calculate the active forces on the moon when a fixed earth movement and a fixed moon movement are assumed in the god-coordinate system.Then we investigate what will happen to the measured moon trajectory in the human-coordinate system when the earth movement and other parameters are perturbed in several cases.It is found that the results are sometimes inconsistent with the explanations using inertia forces in classical mechanics but can be well explained by NGST.We also discovered that the active forces on the moon should be adjusted constantly to maintain the stable measured moon trajectory.展开更多
Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigatio...Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigational efficiency.With the popularization of the Automatic Identification System(AIS),numerous studies utilized ship trajectories to identify maritime traffic patterns.However,the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior,such as ship static and maritime geospatial features,resulting in insufficient precision in ship traffic pattern recognition.This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity(STPMTS),which considers ship static feature,dynamic feature,port geospatial feature,as well as semantic relationships between these features.First,A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements.Subsequently,to quantify the degree of similarity of ship trajectories,a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features.Furthermore,trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters.The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns.Finally,the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China,ranging from September to November 2016.Compared with other methods,the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features,dynamic features,and adherence to navigation rules.This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.展开更多
Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths....Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.展开更多
A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a comm...A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens. In this work, the image pre-processing steps include stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. A top-hat transformation is found to be very effective for the binarization of images with non-uniform background illumination. Particle trajectories measured within a certain exposure time are used to directly obtain particle size and velocity. The particle concentration is calculated by using the statistics of recognized particles within the field of view. We validate our method by analyzing experiments in a gas-droplet cyclone separator. This in-line image processing method can significantly reduce the measurement cost and avoid the data inversion process involved in the light scattering method.展开更多
基金This work was supported by the Startup Funding of New-Joined PI of Westlake University with Grant No.041030150118 and Scientific Research Funding Project of Westlake University under Grant No.2021WUFP017.
文摘All lunar theories within the framework of classical mechanics are based on the assumption of an inertial frame of reference,which is physically non-existent and has scarcely been examined in previous studies.This paper aims to examine the consequence of giving up the assumption of the existence of an inertial frame and the relevant influence on the measured moon trajectories.The general formulation of the lunar orbit is built from the perspective of the new general system theory(NGST),where the moon is modelled as a 6-DOF living body and active forces are introduced.This formulation can be retrograded to the framework of classical mechanics.Firstly,we calculate the active forces on the moon when a fixed earth movement and a fixed moon movement are assumed in the god-coordinate system.Then we investigate what will happen to the measured moon trajectory in the human-coordinate system when the earth movement and other parameters are perturbed in several cases.It is found that the results are sometimes inconsistent with the explanations using inertia forces in classical mechanics but can be well explained by NGST.We also discovered that the active forces on the moon should be adjusted constantly to maintain the stable measured moon trajectory.
基金supported by the National Natural Science Foundation of China[grant number 52371359]the Dalian Science and Technology Innovation Fund[grant number 2022JJ12GX015].
文摘Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigational efficiency.With the popularization of the Automatic Identification System(AIS),numerous studies utilized ship trajectories to identify maritime traffic patterns.However,the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior,such as ship static and maritime geospatial features,resulting in insufficient precision in ship traffic pattern recognition.This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity(STPMTS),which considers ship static feature,dynamic feature,port geospatial feature,as well as semantic relationships between these features.First,A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements.Subsequently,to quantify the degree of similarity of ship trajectories,a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features.Furthermore,trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters.The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns.Finally,the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China,ranging from September to November 2016.Compared with other methods,the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features,dynamic features,and adherence to navigation rules.This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.
基金supported by the National Natural Science Foundation of China(Grant Nos.52105514,52075204)the Fundamental Research Funds for the Central Universities(Grant No.2042023kf0114)+1 种基金Wuhan Natural Science Foundation(Grant No.20240408010202220)Hubei Province Key R&D Program(Grant No.2022BAA067).
文摘Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.
基金support from the National Natural Science Foundation of China(51206112,51076106, 51176128)the Science and Technology Support Program in Shanghai(10540501000)
文摘A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens. In this work, the image pre-processing steps include stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. A top-hat transformation is found to be very effective for the binarization of images with non-uniform background illumination. Particle trajectories measured within a certain exposure time are used to directly obtain particle size and velocity. The particle concentration is calculated by using the statistics of recognized particles within the field of view. We validate our method by analyzing experiments in a gas-droplet cyclone separator. This in-line image processing method can significantly reduce the measurement cost and avoid the data inversion process involved in the light scattering method.
