Xi'an University of Science and Education Foundation

The rapid development of artificial intelligence (AI) technology has sparked global ethical controversies.This study proposes a tripartite "Deconstruction-Translation-Embedding" model to systematically integrate Confucian benevolence, Daoist harmony, and the Doctrine of the Mean into AI ethics frameworks.Through computational hermeneutics of classical texts and technical implementation in real-world applications, we demonstrate how traditional Chinese wisdom addresses critical challenges including algorithmic bias (reduced by 32% in tested scenarios), decision transparency (improved interpretability scores by 1.8), and cross-cultural compatibility.

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

Coal particle characteristic extraction is essential to prevent dust explosions. It is traditionally analyzed by manual screening, which is relatively time-consuming and cannot automatically capture outlining. To address this issue, an accurate and automatic particle image segmentation method is highly demanded in the smart mine. Thus, a novel characteristic learning approach that applied simplified VGGNet as a backbone network is investigated to learn the feature details of particle image sample. The sample set includes 3000 dust images captured from coal preparation plants. First, hierarchical features are extracted step by step on the improved VGGNet. Meanwhile, the feature maps obtained via convolution are sent to the dual attention mechanism module to determine the global feature weights, and the particle characteristic information is optimized. Afterward, an erosion-dilation module is applied to achieve dense texture separation in the deep feature map. Finally, the particles are segmented by upsampling on unpooling. The experimental results show that the proposed method achieves better precision, recall, and ${F}1$ with 0.8743, 0.8351, and 0.8543, respectively, than other previous methods. Compared with laser diffractometry, the maximum error $\varepsilon $ is 5.115% in the range of $R \le 75~\mu \text{m}$ , and this is consistent with the expected. The proposed method outperformed other state of the arts on segmentation results and can be applied in coal dust detection for enterprise as a viable alternative.