It is necessary to detect the target reflections in ground penetrating radar (GPR) images, so that surface metal targets can be identified successfully. In order to accurately locate buried metal objects, a novel method called the Multiresolution Monogenic Signal Analysis (MMSA) system is applied in ground penetrating radar (GPR) images. This process includes four steps. First the image is decomposed by the MMSA to extract the amplitude component of the B-scan image. The amplitude component enhances the target reflection and suppresses the direct wave and reflective wave to a large extent. Then we use the region of interest extraction method to locate the genuine target reflections from spurious reflections by calculating the normalized variance of the amplitude component. To find the apexes of the targets, a Hough transform is used in the restricted area. Finally, we estimate the horizontal and vertical position of the target. In terms of buried object detection, the proposed system exhibits promising performance, as shown in the experimental results.

In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy.

For three-dimensional pentamode metamaterials, it is of great significance to realize underwater low frequency acoustic wave control. Therefore, in order to compare with traditional double-cone pentamode metamaterials, two multilayer composite cylindrical three-dimensional pentamode metamaterials with low frequency and broad band gaps are proposed in this paper. By using pentamode metamaterials with lattice constants on the order of centimeters, the phononic band gaps below 60 Hz and the single-mode area below 30 Hz can be obtained. In addition, compared with asymmetrical double-cone locally resonant pentamode metamaterials, the lower edge frequency, relative bandwidth and figure of merit of the first phononic band gap can be reduced by up to 61.4%, 10.3% and 40.6%, respectively. It will provide reference and guidance for the engineering application of pentamode metamaterials in controlling the ultra-low frequency broadband acoustic waves, vibration and noise reduction.

Accurate prediction of battery state of health (SOH) is essential for optimizing recycling processes. However, existing deep learning models often struggle to adapt to batteries with diverse materials and operating conditions. Some studies have explored the use of large language models to improve model generalization, but there are still limitations in effectively embedding battery measurement data, incorporating degradation domain knowledge, and adapting model parameters to specific battery characteristics. Therefore, this article introduces a domain knowledge-guided industrial large model (DK-ILM) framework, consisting of sequence reprogramming embedding (SRE), domain knowledge embedding (DKE), and sparsity-aware adapter (SAA), to enhance the generalization capability of SOH prediction. The SRE jointly encodes battery discharge data and relaxation voltage to generate effective measurement embeddings. The DKE models battery degradation mechanisms as prompt information, building domain knowledge embeddings that guide the model in identifying key features of battery health. The SAA combines both embeddings and employs a sparse attention mechanism to focus on key information, improving the model’s adaptability to the target battery. Experimental results on five battery datasets demonstrate that DK-ILM achieves high accuracy and robust generalization in SOH prediction tasks.

The topological simulation of acoustic waves has induced unconventional propagation characteristics, thereby offering extensive application potential in the field of acoustics. In this paper, we propose a machine learning-assisted method for the inverse design of acoustic wave topological edge states and demonstrate its practical applicability. Leveraging the predictions from a trained artificial neural network algorithm, the design of wide-bandwidth topological insulators is achieved, with simulation results indicating an approximately 2.8-fold enlargement of the single-cell topological bandgap. Further investigation into their wide-bandwidth topological transport properties is conducted. Additionally, two distinct functional acoustic routing devices are devised. Superior performance of the wide-bandwidth acoustic topological devices has been verified through simulation experiments. This approach provides an efficient and viable avenue for the design and optimization of acoustic devices, with the potential to enhance the management and control efficiency of acoustic signal propagation.