The display of commodity in the e-commerce field requires a large amount of labor to carry out image cutting. How to pick out the interested objects quickly and accurately from the images of commodity is an arduous challenge for commodity segmentation. Although the development of neural network has improved the accuracy of commodity segmentation, false negative results are still inevitable. In order to promote the performance of commodity segmentation, an UNet-based neural network, MR-UNet (more-residual UNet), for commodity segmentation is proposed. By improving the encoder of UNet, deepening the network layers of the encoder and adding residual blocks, the MR-Module is constructed to enhance the ability of the model to extract details of goods. In order to reduce the checkerboard effect, bilinear interpolation is utilized instead of deconvolution in UNet model. By migrating the weight of ResNet and generalizing the model on the enhanced data set, MR-UNet shows better performance in commodity semantic segmentation. The experiments were carried out on the labeled Fashion Product Images Dataset, and the results show that MR-UNet model acquired outstanding results in segmenting hollow commodities. Compared with several popular deep learning models UNet, PSPNet, FCN, DeepResUNet, UNet3+ in terms of commodity semantic segmentation, MR-UNet performs better in all indicators such as Dice Coefficient, MIoU, Matthews Coefficient, and Pixel Accuracy.

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R² values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

With the increased penetration of wind energy into the power system, the fault ride-through ability of doubly fed generator (DFIG) has got more and more attention. Recent grid codes request DFIG stay connected to grid when system fault occurs and play an active role in grid voltage recovery. In this paper, a new voltage control strategy without crowbar is to improve fault ride-through characteristic. The transient characteristics of DFIG are analyzed. The fault current contributions DFIG with crowbar protection and proposed voltage control strategy are analyzed and compared based on mathematical expressions and equivalent circuits. The conclusion that fault current contributions of DFIG under two control strategies are different in amplitude and phase is obtained. At last, a 10KV distribution network with DFIG model under two control strategies set up in PowerFactory DIgSILENT. Simulation results show the different behavior of DFIG under different control strategies which will impact distribution network protection in different way.