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Unmanned Aerial Vehicles, also known as UAVs, play an increasingly important part in daily life. However, the ever-increasing number of UAVs pose an ever-increasing threat to the transportation infrastructure. Despite their precision and general efficiency, infrastructural-scale Unmanned Aerial Systems (UASs) have a disadvantage regarding their capability of being implanted in the ecosystem. There are several reasons for this, but the primary bottleneck is that their systems are not transparent to society and have very complicated processes. As a result, the authors decided to investigate the functional properties of UASs and make improvements to those properties. Throughout the study, the authors' primary focus was on analysis, which boosts productivity and ensures a significant level of safety for routine flights. The amount of power that a UAV uses depends on several variables, including the amount of power that its individual components require, the temperature of its surroundings, and the condition of the battery that it is powered by. Therefore, critical parameters and interdependencies are taken into account in the risk assessment strategy for energy-efficient Unmanned Aerial Vehicles (UAVs). In the case of UAVs, the algorithm performs a risk calculation before take-off to estimate the amount of risk that can be associated with the given flight time when using the provided battery. On the one hand, several instances of the pre-take-off state and how its parameters interact are investigated. On the other hand, they demonstrate the calculation of the risk while in flight, which is based on actual flight data. [ABSTRACT FROM AUTHOR]

Aiming at the existing single-motor agricultural tractors, which often have a mismatch between power and working conditions and a poor operation effect under different tillage modes, this paper designs a torque allocation strategy for agricultural electric tractors under different tillage modes. Firstly, the torque is divided into basic and compensating, and a calculation model is established. Then, the Particle Swarm Optimization algorithm is used to find the optimal demand torque position, and fuzzy control rules allocate the motor torque in combination with the battery SOC. Finally, the strategy's effectiveness in different tillage modes is verified by MATLAB/Simulink simulation and bench test. The test results show that the strategy can distribute the motor torque stably according to the load torque change and pedal opening under three PTO transitions and the plowing and rotary tillage modes. The main and speed control motors respond in about 3 s with good real-time performance. The drive wheel torque can reach 1600 N·m during plowing and rotating operation. The PTO torque can reach 60 N·m during the rotating process. The maximum torque of the output shaft can reach 150 N·m with good plowing performance. During all operations, the SOC of the battery shows a steady linear decrease, and the battery discharge has stability. [ABSTRACT FROM AUTHOR]

A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning. [ABSTRACT FROM AUTHOR]