Characteristics and influencing factors of greenhouse gas emissions from reservoirs in the Yellow River Basin:A Meta-analysis

Reservoir construction and operation profoundly alter the hydrological,hydrodynamic,and carbon and nitrogen cycling processes of rivers.However,current research still lacks a systematic understanding of the characteristics of greenhouse gas(GHG)emissions from reservoirs in arid/semi-arid regions.This study integrates existing monitoring data to discuss the characteristics of GHG emissions from reservoirs in the Yellow River Basin and illustrate the controlling factors and underlying mechanism of these processes.The results indicate that while CO_(2) emission flux from reservoirs is lower than that from river channels,the emission fluxes of CH_(4) and N_(2)O are 1.9 times and 10 times those from rivers,respectively,indicating that the emission of GHG with stronger radiative effect is significantly enhanced in reservoirs.Compared to the reservoirs in humid climates(e.g.,the Three Gorges Reservoir),reservoirs in the Yellow River Basin exhibit relatively lower emissions of CO_(2) and CH_4 due to lower organic matter concentrations,but significantly higher N_(2)O emissions due to higher nitrogen loads.Monte Carlo simulations for 237 reservoirs in the Yellow River Basin showed that total emission of the three GHGs is 3.05 Tg CO_(2)-eq yr^(-1),accounting for 0.39% of the total emission from global reservoirs and lower than the area percentage of the basin(0.53%).This study has important implications on revealing the GHG emission characteristics and control mechanisms of reservoirs in arid/semi-arid regions.

Optimization and experimental analysis of sweet potato ship-shaped transplanting trajectory using particle swarm algorithm

The mechanization of ship-shaped transplanting is currently an urgent problem that should be solved.The movement trajectory of the transplanting mechanism is the key technology to perform ship-shaped transplanting.In this study,a ship-shaped transplanting trajectory was built and a mathematical model of a four-link transplanting mechanism was developed based on the requirements of the sweet potato transplanting agronomic technology.The particle swarm optimization algorithm was used,with the length of the four-bar mechanism and the installation angle of the fixed bar serving as the variables to optimize.The objective was to minimize the deviation of the ship-shaped transplanting trajectory,yielding an iterative optimization solution.The MATLAB simulation results showed that the penalty factors of different proportions in the adaptive particle swarm optimization algorithm affected the transplanting trajectory.The optimal penalty factor parameters areα=0.6,β=0.4.They ensure that the transplanting trajectory fulfills the agronomic requirements,and limit the deviation in the return trajectory of the mechanism.The sizes of the optimized four-bar mechanism were 110,312,245,160,360,and,160 mm.The determined installation angle was 100°.The results of the field experiments demonstrated that the optimized four-bar transplanting mechanism can better fulfill the agronomic technical requirements of sweet potato ship-shaped transplanting.For a transplanting speed of 0.2 m/s,the average qualified rates of insertion depth,insertion length,and tail height were equal to 94.00%,93.83%,and 91.67%.The results obtained in this study provide a theoretical basis and technical support for studying and developing sweet potato ship-shaped transplanting machinery.

Trajectory tracking control of agricultural vehicles based on disturbance test

To improve the trajectory tracking robust stability of agricultural vehicles,a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented.Through the proposed method,the path tracking problem can be divided into two problems with speed and steering angle constraints:the trajectory planning problem,and the trajectory tracking optimization problem.Firstly,the nonlinear kinematics model of the agricultural vehicle was discretized,then the derived model was inferred and regarded as the prediction function plant for the designed controller.Second,the objective function characterizing the tracking performance was put forward based on system variables and control inputs.Therefore,the objective function optimization problem,based on the proposed prediction equation plant,can be regarded as the nonlinear constrained optimization problem.What’s more,to enhance the robust stability of the system,a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control.To validate the theoretical analysis before,the Matlab simulation was performed to investigate the path tracking performance.The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability.Meanwhile,the corresponding experiments were conducted.When the test vehicle tracked the reference track with a speed of 3 m/s,the maximum lateral deviation was 13.36 cm,and the maximum longitudinal deviation was 34.61 cm.When the added horizontal deviation disturbance Yr was less than 1.5 m,the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive.Finally,to better highlight the controller proposed in this paper,a comparison experiment with a linear model predictive controller was performed.Compared to the conventional linear model predictive controller,the horizontal off-track distance reduced by 36.8%and the longitudinal deviation reduced by 32.98%when performing circular path tracking at a speed of 3 m/s.

Design of obstacle avoidance controller for agricultural tractor based on ROS

The obstacle avoidance controller is a key autonomous component which involves the control of tractor system dynamics,such as the yaw lateral dynamics,the longitudinal dynamics,and nonlinear constraints including the speed and steering angles limits during the path-tracking process.To achieve the obstacle avoidance ability of control accuracy,an independent path re-planning controller is proposed based on ROS(Robot Operating System)nonlinear model prediction in this paper.In the design process,the obstacle avoidance function and an objective function are introduced.Based on these functions,the obstacle avoidance maneuvering performance is transformed into a nonlinear quadratic optimization problem with vehicle dynamic constraints.Moreover,the tractor dynamics maneuvering performance can be effectively adjusted through the proposed objective function.To validate the proposed algorithm,a ROS based tractor dynamics model and the SLAM(Simultaneous Localization and Mapping)are established for numerical simulations under different speed.The maximum obstacle avoidance deviation in the simulation is 0.242 m at 10 m/s,and 0.416 m at 30 m/s.The front-wheel rotation angle and lateral velocity are within the constraint range during the whole tracking process.The numerical results show that the designed controller can achieve the tractor obstacle avoidance ability with good accuracy under different conditions.