The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the m...The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.展开更多
This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obsta...This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obstacles and in narrow passages,which can result in time consumption.To alleviate oscillation,we modified the AP method using the Levenbery-Marquardt(LM)algorithm.In the modified AP method,we altered the original directions of AP forces to the Newton direction,and adjust the parameter by the LM algorithm.A series of comparative experimental results show that the modified AP method can achieve smoother trajectories with less time consumption.This demonstrates the feasibility and effectiveness of our proposed approach.展开更多
基金CRC Mining and The University of Queensland for their financial support for this study
文摘The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054 and 61333004)the National Key Basic Research Program of China(Grant No.2014CB046401)+2 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Top-Notch Young Talents Program of China,Graduate Innovation Foundation for Beihang University(Grant No.YCSJ-01-201206)Aeronautical Foundation of China(Grant No.20135851042)
文摘This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obstacles and in narrow passages,which can result in time consumption.To alleviate oscillation,we modified the AP method using the Levenbery-Marquardt(LM)algorithm.In the modified AP method,we altered the original directions of AP forces to the Newton direction,and adjust the parameter by the LM algorithm.A series of comparative experimental results show that the modified AP method can achieve smoother trajectories with less time consumption.This demonstrates the feasibility and effectiveness of our proposed approach.
