In this paper, the developed system for detecting problematic areas of agricultural fields by using computer vision in unmanned aerial vehicle (UAV) photography was investigated. The structures of the neural networks ...
In this paper, the developed system for detecting problematic areas of agricultural fields by using computer vision in unmanned aerial vehicle (UAV) photography was investigated. The structures of the neural networks of the YOLOv5 and YOLOv8 family were considered to find a solution to the task of detecting problem areas. The application of the developed software will reduce labour and time spent on image analysis, which in turn will reduce the response time in detecting problem areas in agricultural fields to achieve higher yields.
The distribution of power flow can be effectively improved by optimizing the configuration of Distributed Generators (DGs) and Shunt Capacitors (SCs). Proper placements and capacities of DGs or SCs for real power loss...
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A wheelchair is a specialized form of Personal Mobility Vehicle (PMV) designed to help people with disabilities move safely to their desired locations. Unlike conventional PMVs, wheelchairs for people with disabilitie...
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Scaled-down vehicles provide a controlled and repeatable environment for testing autonomous driving algorithms, expediting development while mitigating risks associated with full-scale vehicle testing. This paper intr...
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The knowledge of the underlying topology is essential for understanding and manipulating power grids, water distribution networks, biological networks. At times, the topology may be reported (or recorded) erroneously,...
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The knowledge of the underlying topology is essential for understanding and manipulating power grids, water distribution networks, biological networks. At times, the topology may be reported (or recorded) erroneously, mostly owing to human mistakes in reporting. The networks can be represented as a graph in which entities are represented as nodes and interactions among nodes as edges. This work focuses on the study of a specific type of error in topology that occurs when the incidence of an edge in the network is incorrectly reported. We propose a methodology to detect, isolate, and rectify this type of error using a single noisy measurement of flows along all the edges of a conserved network. We first show that this type of error generates specific error signatures, which enables error diagnosis, when the data is noise-free. An approach based on a series of statistical tests is developed to handle noisy data for online error detection and rectification. Simulation studies are performed to test the robustness of the proposed methodology.
Recently, action recognition based on graph data has received widespread attention. Graph convolutional neural networks need a large amount of graph data support. Therefore, graph data augmentation has significant res...
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Estimating Neural Radiance Fields (NeRFs) from images captured under optimal conditions has been extensively explored in the vision community. However, robotic applications often face challenges such as motion blur, i...
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With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data...
With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system.
This paper introduces a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each playe...
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Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers po...
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