This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, ha...This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, has been implemented in the MATLAB environment and was developed based on a John Deere tractor. The simulation results from this MATLAB model was validated through field navigation tests. The accuracy of the trajectory estimation is strongly affected by the determination of the cornering stiffness of the tractor. In this simulation, the tractor cornering stiffness analysis was identified during simulation analysis using the MATLAB model based on the recorded trajectory data. The obtained data was used in simulation analyses for various navigation operations in the field of interest. The analysis on field validation test results indicated that the developed tractor system could accurately estimate wheel trajectories of a tractor system while operating in agricultural fields at various speeds. The results also indicated that the developed system could accurately determine tractor velocity and steering angle while the tractor operates in curved fields.展开更多
System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software m...System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification(low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 30270773)the Teaching and Research Award Program forOutstanding Young Teachers in Higher Education Institutions of MOE,Chinaand the Natural Science Foundation of Zhejia
文摘This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, has been implemented in the MATLAB environment and was developed based on a John Deere tractor. The simulation results from this MATLAB model was validated through field navigation tests. The accuracy of the trajectory estimation is strongly affected by the determination of the cornering stiffness of the tractor. In this simulation, the tractor cornering stiffness analysis was identified during simulation analysis using the MATLAB model based on the recorded trajectory data. The obtained data was used in simulation analyses for various navigation operations in the field of interest. The analysis on field validation test results indicated that the developed tractor system could accurately estimate wheel trajectories of a tractor system while operating in agricultural fields at various speeds. The results also indicated that the developed system could accurately determine tractor velocity and steering angle while the tractor operates in curved fields.
基金the FIST project,of DST, government of India for sponsoring the work on web engineering and cloud based computing
文摘System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification(low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.
