Study in Driving Strategy and Analysis of Sustainable and Symbiosis Development Relationship between Agricultural Industrial Clusters and Agricultural Logistics Industry

Due to a lack of profound disclosure of the internal mechanism for the symbiotic development of agricultural industrial clusters and agricultural logistics industry, the current study finds it difficult to form specific and implementable driving countermeasures well. Quantitative research on their symbiotic development and evolution is an important method to promote the further development of agricultural industry and agricultural logistics industry. In this paper, the factors affecting the sustainable symbiotic development relationship are analyzed between agricultural industry clusters and agricultural logistics industry with explanatory structural equation, and a system-driving model is constructed for the symbiotic development of agricultural enterprise clusters and agricultural logistics industry. The analysis indicated that, for the symbiotic development of agricultural enterprise clusters and agricultural logistics industry, the macro policy orientation is the fundamental driving force and the symbiotic development effect is its final result. Seven driving paths are refined, and the relevant countermeasures to promote the sustainable development of agricultural industrial clusters and agricultural logistics industry are put forward one at a time.

Research on Low-Speed Driving Model of Ultrasonic Motor Based on Beat Traveling Wave Theory

This paper proposes a driving method, the superimposed pulse driving method, that can make an ultrasonic motor run at a low speed. Although this method solves the periodic oscillation of speed in a traditional low-speed driving motor, it still has a small periodic fluctuation, which affects the stability of the speed. To reduce the fluctuation rate of the motor speed, the structure model and driving model of the motor are established, based on the theory of a beat traveling wave, and the motion characteristics of the particle point are analyzed in this paper. The simulation curve of the motor speed is obtained according to the stator and rotor contact model and the transfer model. The research shows that the driving method introduced in this paper causes the stator surface to generate a traveling beat wave, and the driving end of the stator generates an intermittent reciprocating vibration and drives the rotor rotation, which is the mechanism of low-speed operation when the driving method is used to drive the motor, as well as the reason for the periodic fluctuation of the motor speed. To improve the speed stability, this paper controlled the output performance of the motor by changing the two control variables—prepressure and frequency difference—and concluded that the variation trend of the average speed and speed volatility were consistent with the variation trend of the motor’s average speed determinant and the speed volatility determinant, respectively, which is verified by the velocity measurement experiment and the vibration measurement experiment. These insights lay the theoretical foundation for the velocity adjustment and stability optimization and, finally, the application of the new driving method is prospected.

Jamming and Anti-Jamming Strategies of Mobile Vehicles

Anti-jamming games have become a popular research topic. However, there are not many publications devoted to such games in the case of vehicular ad hoc networks (VANETs). We considered a VANET anti-jamming game on the road using a realistic driving model. Further, we assumed the quadratic power function in both vehicle and jammer utility functions instead of the standard linear term. This makes the game model more realistic. Using mathematical methods, we expressed the Nash equilibrium through the system parameters in single-channel and multi-channel cases. Since the network parameters are usually unknown, we also compared the performance of several reinforcement learning algorithms that iteratively converge to the Nash equilibrium predicted analytically without having any information about the environment in the static and dynamic scenarios.

Object and Lane Detection Technique for Autonomous Car Using Machine Learning Approach

The main objective of this work is to develop a perception algorithm for self-driving cars which is based on pure vision data or camera data. The work is divided into two major parts. In part one of the work, we develop a powerful and robust lane detection algorithm which can determine the safely drive-able region in front of the car. In part two we develop and end to end driving model based on CNNs to learn from the drivers driving data and can drive the car with only the camera data from on-board cameras. Performance of the proposed system is observed by the implementation of the autonomous car that can be able to detect and classify the stop signs and other vehicles.

Multi-objective optimization for autonomous driving strategy based on Deep Q Network

Autonomous driving is an important development direction of automobile technology, and driving strategy is the core of the autonomous driving system. Most works in this area focus on single-objective tasks, such as maximizing vehicle speed or lane-keeping, and rare attention has been paid to the quality of driving skills. Therefore, a multi-objective learning method is proposed for autonomous driving strategy based on deep Q-network, where two optimization objectives are involved, i.e., vehicle speed and passenger comfort. An end-to-end autonomous driving model is designed by using vehicle front camera images as inputs to the Q-network and makes decisions based on the output Q values. Considering the vehicle speed and passenger comfort, the reward function is designed for multi-objective optimization. To evaluate the effectiveness of the method, training and testing are performed in a simulator, and a single-objective strategy with the goal of maximizing speed is designed for comparison. The results show that the proposed multi-objective autonomous driving strategy can strike a balance between vehicle speed and passenger comfort. Compared with the single-objective strategy, the multi-objective strategy has a significant improvement in comfort, while the average speed is only slightly reduced.

Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System

By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and European Centre for Medium-Range Weather Forecasting Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a four-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited area model (LAM) configuration of the UM – two regionally-operational single-moment schemes (UM_RA2M and UM_RA2T), and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with > 90 % mean cloud occurrence simulated between 0.15 and 1 km in all model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias simulated in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud top radiative cooling from these persistent modelled cloud layers and were interestingly consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid-scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the driving model, thus triggering too much cloud formation within the lower troposphere. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration, while improving cloud microphysical structure, does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.