The participation of renewable energy resources in modern power systems has been increasing due to their positive impacts on diversifying power supplies with environmental benefits. Despite these advantages, the syste...
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UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgro...
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UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.
Gallium Nitride power devices, with an energy bandgap roughly three times larger than that of silicon, provide lower specific conduction resistance and faster switching speeds. These characteristics enable the design ...
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Device-free wireless sensing (DFWS) has gained significant attention due to its high accuracy and privacy-preserving capabilities. DFWS systems work by analyzing the influence pattern of targets on the surrounding wir...
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Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the...
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Autonomous intersection management (AIM) presents significant challenges due to the complexity of real-world traffic scenarios and the reliance on a costly centralized server to manage and coordinate all vehicles simu...
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This study uses quantum-inspired techniques to ad-dress the DC optimal power flow problem considering frequency constraints. Although numerous analytical and data-driven meth-ods have been developed to solve DC-OPF un...
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The balance of supply and demand is pivotal in ensuring efficient and reliable power grid utilization. With the growth of demand, the integration of renewable energy resources (RERs) into the power grid is increasing ...
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Optimizing the charging protocol for large-scale electric vehicles is complex and computationally costly. Therefore, this paper proposes an advanced approach using machine learning-assisted mean field game theory to h...
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Loss functions are essential for optimizing the imaging performance of trained deep learning models. Thus, the design of tailored loss functions defines the effectiveness of deep learning imaging models. A loss functi...
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