A precise friction model is essential for the prediction of tyre wet grip performance and optimization of pavement surface texture design.A mechanical system for predicting the wet grip potential of asphalt pavement w...A precise friction model is essential for the prediction of tyre wet grip performance and optimization of pavement surface texture design.A mechanical system for predicting the wet grip potential of asphalt pavement was systematically presented using an extended rubber material model by a time step integration scheme.This analytical approach was transferred to a 2D numerical multi-body system consisting of interconnected masses,coupling spring and elementary rubber element of a generalized Maxwell model of rubber tyre tread.The system consists of two basic modules with the same program structure and algorithm,considering the frequency-,temperature-,and strain-dependency behaviors of the complex dynamic modulus of rubber element.The dependence of penetration depth and friction coefficient on the velocity was simulated and validated.It can be concluded that this system can be used for predicting the wet grip potential of asphalt pavements.展开更多
Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of ...Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.展开更多
基金Project(FP6-PL-0506437) supported by European CommissionProject(50908053) supported by the National Natural Science Foundation of China
文摘A precise friction model is essential for the prediction of tyre wet grip performance and optimization of pavement surface texture design.A mechanical system for predicting the wet grip potential of asphalt pavement was systematically presented using an extended rubber material model by a time step integration scheme.This analytical approach was transferred to a 2D numerical multi-body system consisting of interconnected masses,coupling spring and elementary rubber element of a generalized Maxwell model of rubber tyre tread.The system consists of two basic modules with the same program structure and algorithm,considering the frequency-,temperature-,and strain-dependency behaviors of the complex dynamic modulus of rubber element.The dependence of penetration depth and friction coefficient on the velocity was simulated and validated.It can be concluded that this system can be used for predicting the wet grip potential of asphalt pavements.
文摘Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.
