Aiming at the problem of substation equipment point cloud recognition, this study proposed a new recognition method. Firstly, a local coordinate system of equipment point cloud was established. The symmetry and distribution density of equipment point cloud were used to determine the x and y axes of the local coordinate system. The coordinate system was invariant to translate and rotate, and was robust to noise. Then, a new feature descriptor was defined based on the difference between the shape and view of substation equipment point cloud, which was used to describe and recognize the point cloud of equipment. Also, a template library containing 54 kinds of electrical equipment such as lightning arrester, circuit breaker, and disconnecting switch was established, which contained the information of type, number and feature descriptor of each template device. The feature descriptor of the equipment point cloud to be identified was calculated and was used for matching the most similar model in the template library to realize the recognition of equipment point cloud. Finally, the method proposed in this study and another two substation point cloud recognition algorithms were tested on 90 equipment point clouds to be identified. The results showed that our method could achieve 90% recognition accuracy, and the average time to identify a device was 3.2 s, which could balance the recognition accuracy and recognition efficiency. And our method slightly outperformed the other two methods when equipment point cloud with noise and occlusion. Moreover, when the density of the point cloud to be identified was not uniform, our method could still maintain the recognition accuracy of more than 70%.
Addressing the issues of insufficient consideration for non-market ecosystem services and unclear definition of compensation control thresholds in forest ecosystem compensation, in this paper a regional ecological compensation mechanism oriented toward securing forest ecosystem service functions was proposed. Based on emergy analysis and integrated with multidisciplinary methods, a quantitative evaluation framework for forest ecosystem service functions was established. The M-K and Pettitt methods were jointly applied to the interannual sequence of functional benefits to identify abrupt changes and determine the compensation control threshold. A coupled humannature emergy system was constructed to trace ecological and economic flows within and beyond the system, compensation standards (i.e., the unit emergy value of forest resources) was defined, and the value compensation relationship between humans and forest systems was quantified with the constraint of functional benefit thresholds. This study provided a new perspective for forest ecological compensation research. The proposed theoretical framework and methodology were applied to Luoyang City in Henan Province. Results showed that the total emergy benefits of forest ecosystem functions exhibited a steady upward trend from 2010 to 2021, with a cumulative increase of 0.37%. The year 2016 was identified as the mutation point, with a compensation control threshold of 5.11E+22 sej. The years 2010—2015, during which the functional benefits were below the threshold, required compensation. During this period, the total compensation value showed a fluctuating decline, decreasing from 37.71 million yuan in 2010 to 12.64 million yuan in 2015. The compensation value was primarily driven by the magnitude of functional benefit reduction below the threshold, which was closely linked to the abundance of key ecological elements within the forest system. Therefore, continuously enhancing forest protection and sustainable management was essential to realize the goal of harmonious coexistence between humans and nature.
To address the challenges of capturing subtle features and the scarcity of data samples in learning emotion recognition, a facial emotion recognition method based on a generative diffusion model and multimodal multiscale visual encoding was proposed. Firstly, a learning emotion dataset integrating multi-scale global and local detailed features was constructed, and the generative diffusion model was used to augment scarce emotional samples, thereby alleviating data constraints in few-shot learning scenarios. Secondly, a multimodal multiscale visual encoding mechanism was designed, which achieved high-precision modeling and effective fusion of micro-expressions and fine-grained emotional features by combining global features of facial images with local details from salient regions. Finally, the experiments were conducted on various models, including CNNs, Vision Transformers, and hybrid architectures. The results showed that the proposed method achieved an overall recognition accuracy of 68.10%, with an average improvement of 2.98% and a maximum improvement of 5.30% compared with existing baseline methods. The ablation experiments further verified the effectiveness and synergistic contribution of the generative diffusion model and the multimodal multiscale fusion module in enhancing the model’s capability to capture micro-expression details and improving overall recognition robustness.
Involute gear transmissions faced challenges such as uneven distribution of contact stress on tooth surfaces that leading to pitting wear, and insufficient bending resistance when transmitting high torque and power. To address these issues, in this study a novel herringbone planetary gear transmission system, featuring a point-contact meshing form consisting of concave tooth profiles (central gear)-convex tooth profiles (planetary gear)-concave tooth profiles (internal ring gear) was proposed. Based on spatial geometric relationships, the equations for the convex and concave tooth profiles of each component were derived. The mathematical description of the conjugate tooth surfaces was completed using the helical motion method, and the geometric equations for the formed tooth surfaces were established. MATLAB and UG software were utilized to perform operations such as surface stitching, extrusion, merging, and Boolean operations, constructing a concave parabolic central gear, a convex circular-arc planetary gear, and a concave parabolic internal ring gear. The assembly of three transmission models—the novel herringbone planetary gear transmission, the involute gear transmission, and the involute-circular-arc gear transmission—was completed. Adaptive meshing technology was employed to gradually refine the mesh, and a detailed sensitivity analysis determined the relationship between mesh density and computational accuracy. A convergence curve of mesh density versus stress was plotted, showing that the equivalent stress stabilized when the mesh density reached 2.0. To balance computational accuracy and efficiency, a mesh density of 2.1 was selected for simulation analysis. With operating conditions of 360 kW input power and a sun gear speed of 2 000 r·min-1, finite element analysis was conducted to compare the meshing contact characteristics of the three configurations. Results demonstrated that compared with the involute gear system, the novel herringbone planetary gear system exhibited a 24.5% reduction in static equivalent stress, more uniform tooth surface stress distribution, and a 4.63% improvement in bending resistance, significantly outperforming standard involute gear transmissions.
Network node embedding is mapping nodes in a network to a low-dimensional vector representation, so that vector space-based learning methods can be directly applied to handle downstream tasks such as link prediction. Most of the existing network node embedding models were for unsigned networks and could not be directly used to deal with signed networks (usually need to be converted into unsigned networks for processing, thus discarding a lot of valuable information embedded in the positive and negative signs on the edges).A node embedding model (SNEDA)based on graphical neural networks was proposed that could directly deal with symbolic networks. Based on structural balance theory and status theory, the paths between nodes were divided into 20 different motif structures according to the direction and the positive and negative information on the edges. A network propagation model was designed with two levels of attention mechanism, which could capture the contribution and influence of different neighboring nodes to the vector representation of node i by node-level attention mechanism when aggregating the direct neighboring information of node i, and captured the vector representation of different motif to node i by path-level attention when aggregating the second-order and higher-order neighboring information of node i. A two-level attention mechanism was introduced to integrate different contributions and influences at the node level and path level, which could it not only improve the time efficiency of the algorithm but also make the final vector representation of node i more beneficial to improve the prediction accuracy of the downstream link prediction task. Through experiments conducted on four real social network datasets, the SNEDA model improved the AUC and F1 metrics by about 3.1% and 1.1%, respectively, compared with the benchmark model, and the results showed that the network representation obtained by the model could improve the accuracy of link prediction.
