With economic development and the increasing electricity demand, distribution network operation has become indispensable for maintaining the power system reliability. However, fault recovery planning for distribution network still faces challenges such as human error, redundant workflows, and duplicated work. Large language models (LLMs), which have exceptional semantic understanding and automated generation capabilities, have recently attracted more and more attention. In this paper, we propose a novel conversational agent based on the mainstream LLMs for fault recovery plan generation. Besides, we introduce a novel tool-learning method that integrates various functionalities, encompassing topology querying, power flow calculations, and formatted text generation. Experiments demonstrate that the fault recovery plan generation agent can effectively leverage the integrated tools, achieving an average success rate of 99.25% in tool invocation.

Millimeter-wave (mmW) automotive synthetic aperture radar (Auto-SAR) technology holds significant promise for advanced driver assistance systems (ADASs). Sparse imaging methods can improve the quality of Auto-SAR images, such as suppressing sidelobes and noise. However, the$l_{1}$convex regularization-based sparse imaging methods suffer from the bias estimation, which reduces the target amplitude and ignores the association between scatterers, weakening the target structure. To address these issues, we proposed joint generalized$l_{q}$and convolutional (Glq-Con) regularization to enhance mmW Auto-SAR sparse imaging in this article. First, to improve the target amplitude, we propose utilizing the nonconvexity of Glq to reduce the bias effect; meanwhile, the global convergence of Glq ensures the imaging accuracy. Then, considering the continuity of the imaging target in driving scenes, we propose to utilize convolution regularization to modify the previously reconstructed amplitude of Glq to improve the target structure. Besides, to reduce computational complexity, we establish an efficient sparse imaging model. In this model, the fast Fourier transform (FFT) operator is employed to approximate complex matrix operation in the iterative process. We also use an efficient optimizer to solve the imaging model. Finally, both simulations and measured typical driving scenario experiments demonstrate that the proposed method significantly enhanced the Auto-SAR image, especially for the targets of weak scatterers.

In this study, we address the challenges associated with Video Synthetic Aperture Radar (Video SAR) shadow tracking, a technique used for continuous monitoring of ground moving targets. Due to challenges such as changes in shadow appearance, low contrast between shadow and background, and scene occlusion in Video SAR, existing methods often encounter extensive matching errors in the data association process, resulting in unsatisfactory tracking performance. To overcome these issues, we propose a novel method, GNN-JFL, which is based on joint motion-appearance feature extraction and graph neural data association. This method uses the detector as a flexible plugin and introduces two key improvements in the tracker section to enhance tracking accuracy. Firstly, we introduce joint feature learning to extract the complementary appearance and motion features from shadow shapes and positions, obtaining more robust feature representations to improve tracking performance under intricate challenges. Secondly, by organically integrating Multi-object Tracking (MOT) problems and Graph Neural Networks (GNN), we propose a novel GNN-based shadow tracking architecture, which utilizes graph relationships to learn the associations between shadows for more accurate tracking predictions. Our method is validated using two measured datasets and demonstrate superior performance in terms of multi-object tracking accuracy (MOTA). It outperforms the suboptimal method by 4.2% and 3.6% in the two datasets, respectively. This research contributes to the advancement of continuous monitoring techniques employing Video SAR shadow tracking.

The near-field SAR technology has shown great application value in many fields, such as security inspection, and radar cross section (RCS) measurement. However, due to side-lobe crosstalk and near-field spherical wave effect, the near-field SAR image has high clutter and side-lobe, which leads to serious image degradation. Complex image degradation results in the loss of target structure and contour, which limits the further application of near-field SAR technology. Due to the complex degradation, current restoration methods are not effective enough in terms of weak scattering center and target shape (geometry and structure) restoration. In this article, we first analyze near-field SAR image degradation. Then, utilizing the recent promising deep learning, we propose a novel near-field SAR image restoration framework. In this framework, we construct model-driven 2D CNN for 2D image restoration and 3D CNN for 3D image restoration, respectively. To validate the proposed framework, we construct experiments on simulated 2D and 3D test set, respectively. The experimental results prove the effectiveness of the proposed framework for both 2D and 3D situations.

Target areas of low resolution SAR images usually have blurred edge and large background noise, so most common object detection methods based on deep learning have obvious errors in this occasion. In this paper, we propose a high precision and lightweight network for low resolution SAR image detection. We take generalized distribution to model bounding box in training and predicting to better indicate target area boundaries in low resolution SAR images, improving detection accuracy. Moreover, we introduce “teacher-student” knowledge distilling method, which greatly reduces model parameters and further enhances the detection accuracy. Compared with conventional deep learning net-works(Faster R-CNN, SSD, CenterNet, FCOS and YOLOv3) on low resolution SAR images, the results show that our method has not only the best performance in target area extractionn, but rather light weight.

Images of near-field SAR contains spatial-variant sidelobes and clutter, subduing the image quality. Current image restoration methods are only suitable for small observation angle, due to their assumption of 2D spatial-invariant degradation operation. This limits its potential for large-scale objects imaging, like the aircraft. To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed. First, the image degradation is seen as a complex convolution process with 2D spatial-variant operations. Then, to restore the image, the process of deconvolution is performed by cyclic coordinate descent algorithm. Experiments on simulation and measured data validate the effectiveness and superiority of the proposed method. Compared with current methods, higher precision estimation of the targets' amplitude and position is obtained.