The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.

Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs.

The development of artificial intelligence (AI) game agents that use deep reinforcement learning (DRL) algorithms to process visual information for decision-making has emerged as a key research focus in both academia and industry. However, previous game agents have struggled to execute multiple commands simultaneously in a single decision, failing to accurately replicate the complex control patterns that characterize human gameplay. In this paper, we utilize the ViZDoom environment as the DRL research platform and transform the agent–environment interactions into a Partially Observable Markov Decision Process (POMDP). We introduce an advanced multi-agent deep reinforcement learning (DRL) framework, specifically a Multi-Agent Proximal Policy Optimization (MA-PPO), designed to optimize target acquisition while operating within defined ammunition and time constraints. In MA-PPO, each agent handles distinct parallel tasks with custom reward functions for performance evaluation. The agents make independent decisions while simultaneously executing multiple commands to mimic human-like gameplay behavior. Our evaluation compares MA-PPO against other DRL algorithms, showing a 30.67% performance improvement over the baseline algorithm.

In recent times, multi-phase induction motor drives have attracted attention where high overall device performance and a decrease in total power per phase are desired. The extra phases in conjunction with normal 3-phase drive ensure that the mechanism continues to function without torque ripple, disturbance and vibration even under unstable circumstances. To overcome this problem, a suitable terminal sliding mode control system may achieve a ripple-less high torque in a five-phase induction motor. The current control loop offers significant benefits to enhance the stability of the drive system. With outstanding transient responses, this device can conduct strong dynamic current management. The Terminal Sliding Mode Control based on MPCC is used to optimize the motor control system. Whereas, HOSM is introduced to eliminate the chattering phenomenon.

Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants.