The existing image retrieval methods struggle to distinguish and extract similar structural information and texture details of power equipment effectively， resulting in low retrieval accuracy and efficiency. To solve these problems， a Power Image Retrieval method based on improved Swin Transformer （PIR-iSwinT） was proposed. Firstly， a Multi-Feature Structure Cross-Enhancement module （MFSCE） was introduced to enhance the model's perception ability of equipment structural and edge features by combining cross-attention mechanism of the gradient magnitude map. Secondly， an Adaptive Inter-class Difference Center Loss module （AIDCL） was designed to strengthen the model's ability to distinguish between similar and dissimilar samples. Finally， a Hierarchical Clustering Retrieval module （HCR） was constructed to optimize the sample matching strategy during retrieval and reduce computational complexity， thereby further enhancing retrieval accuracy and efficiency. Experimental results on the self-built power scenario dataset and the NUS-WIDE dataset show that PIR-iSwinT achieves the mean Average Precision （mAP） of 96.76% and 92.68%， respectively， at a 32 bit hash code length， outperforming HRMPA （Hash image Retrieval based on Mixed attention and Polarization Asymmetric loss） by 2.35% and 0.56%， respectively. It can be seen that PIR-iSwinT extracts and distinguishes detailed structural features of power equipment effectively， enhances retrieval efficiency， and demonstrates good generalization capability， verifying effectiveness of the proposed method.

Considering the difficulty of modeling the interaction between labels and solidification of global label relationship in multi-label image classification tasks， a new Multiple-Label image classification method based on Global and Local Label Relationship （ML-GLLR） was proposed by combining self-attention mechanism and Knowledge Distillation （KD） method. Firstly， Convolutional Neural Network （CNN）， semantic module and Dual Layer Self-Attention （DLSA） module were used by the Local Label Relationship （LLR） model to model local label relationship. Then， the KD method was used to make LLR learn global label relationship. The experimental results on the public datasets of MicroSoft Common Objects in COntext （MSCOCO） 2014 and PASCAL VOC challenge 2007 （VOC2007） show that， LLR improves the mean Average Precision （mAP） by 0.8 percentage points and 0.6 percentage points compared with Multiple Label classification based on Graph Convolutional Network （ML-GCN） respectively， and the proposed ML-GLLR increases the mAP by 0.2 percentage points and 1.3 percentage points compared with LLR. Experimental results show that， the proposed ML-GLLR can not only model the interaction between labels， but also avoid the problem of global label relationship solidification.

To deal with broadband signal processing, a new algorithm for the Direction Of Arrival (DOA) estimation of wideband Linear Frequency Modulation (LFM) signal was introduced. In this paper, the broadband LFM signal was transformed into a narrowband signal by Discrete Polynomial-phase Transform (DPT). After that, the wideband LFM signal could be transformed into a single sinusoidal signal and the new noise. And the time-varying direction vector in time domain was changed to the time-invariant vector. Then, the conventional narrow band signal processing method, Multiple Signal Classification (MUSIC) algorithm, was used to estimate the DOA. The theoretical analysis and simulation results show that the proposed method can accurately estimate DOA of the signal. The scheme is easy to implement with less computation, and it has good estimation performance.