Real-time optimal operation and control acts as the intelligent core of renewable energy systems, yet intermittent generation and time-varying loads introduce uncertainties that hinder secure and efficient operation. To address this issue, this paper proposes a digital-twin-driven cyber–physical framework for real-time energy management and secure operation, enabling tight synchronization and closed-loop interaction between physical assets and their digital counterparts. A simplified multi-source data fusion Transformer is developed to improve forecasting accuracy by continuously integrating historical power data and key environmental factors (e.g., temperature, wind speed, and solar radia... More >

To alleviate the computational and memory pressure faced by resource-constrained edge platforms in large-scale homogeneous multi-agent visual SLAM tasks, this paper designs and validates a centralized homogeneous multi-agent visual SLAM system based on a cloud-native architecture. The system adopts a cloud-edge functional decoupling strategy: real-time perception modules, such as visual odometry, keyframe extraction, and local tracking, are retained on the edge side, while computationally intensive tasks, including loop-closure detection, pose graph optimization, global map management, and multi-source map fusion, are centrally executed in the cloud. To accommodate bandwidth fluctuations, tr... More >

The increasing popularity of civilian unmanned aerial vehicles (UAVs) poses serious safety challenges to low-altitude airspace near power transmission and distribution lines. Existing monitoring technologies rely heavily on expensive radar or radio frequency devices that are susceptible to interference, while mainstream vision schemes are typically limited to object detection and fail to analyze flight intentions. To address these problems, this paper proposes a UAV maneuver recognition method for transmission and distribution line protection, providing precise semantic support for action-based risk warnings through multi-dimensional visual feature fusion. First, a YOLO object detection mode... More >

Virtual Reality (VR) systems require extremely low latency and accurate user intent prediction to ensure immersion. However, traditional visual modulation techniques primarily rely on single-sensor inputs (e.g., IMU angular velocity), which suffer from inherent mechanical delays and fail to capture complex spatial-temporal dynamics, ultimately leading to severe visually induced motion sickness (VIMS). Existing methods lack effective fusion of multi-source heterogeneous data, resulting in delayed responses and abrupt visual transitions. To address this, this study proposes a novel spatio-temporal information fusion framework for adaptive visual modulation in VR. The proposed architecture deep... More >

To address the safety hazards posed by unauthorized "black flight" of unmanned aerial vehicle (UAV) clusters, as well as the limitations of existing detection methods in tiny target recognition and complex environment adaptability, this paper proposes a real-time detection and counting method based on a feature fusion framework. The core contribution of this work is a novel multi-level fusion architecture that aggregates multi-scale visual features to enhance detection robustness under challenging conditions. First, we construct a multi scene UAV data set containing 7,113 images, and design a dedicated feature fusion module based on improved YOLOv9 backbone. The Haar Wavelet Down-sampling (H... More >

To address severe fire safety risks caused by electric motorcycles (EMs) and their batteries being illegally brought into building elevators, this paper presents a real-time EM detection and alarm system for elevator environments, built upon a multi-source information fusion framework and an improved YOLOv9. To elevate detection accuracy for EMs in confined elevator spaces, two core optimizations are embedded into the network: the Programmable Gradient Information (PGI) training strategy, and a lightweight Generalized Efficient Layer Aggregation Network (GELAN) backbone enhanced with depthwise separable convolution (DSConv). A dedicated dataset consisting of roughly 2,000 images is establish... More >

Ship detection in Synthetic Aperture Radar (SAR) images remains challenging due to coherent speckle noise, complex inshore clutter, and large variations in target scale, especially for tiny ships. To address these issues, this paper proposes a lightweight SAR ship detection network based on YOLOv11n. The proposed model introduces a high-resolution P2 detection branch to preserve fine spatial details that may be weakened during repeated downsampling. To improve multi-scale feature representation, a Four-Head Adaptive Spatial Feature Fusion (FASFF) structure is adopted to adaptively combine features from P2, P3, P4, and P5. In addition, the Squeeze-and-Excitation (SE) attention module is inser... More >

Ship detection in Synthetic Aperture Radar (SAR) imagery is crucial for maritime surveillance. However, it faces significant challenges, including small target sizes, complex sea clutter interference, and stringent requirements for computational efficiency in on-board processing. While detection frameworks like YOLOv12 have achieved a favorable balance between speed and accuracy by integrating attention mechanisms with convolutional neural networks (CNNs), their generic architectures are not optimized for the unique physical characteristics of SAR imagery and the scattering properties of ship targets. To develop a more suitable lightweight and high-precision model for SAR ship detection, thi... More >