Acetabular reamer is one of the most important surgical tools in hip replacement surgery. The milling quality of acetabular reamer on acetabulum is affected by the dimension change of cutting edges. The wear of acetabular reamer can be examined by processing 3D point cloud of acetabular reamer, so a dimensional analysis algorithm for the cutting edges of acetabular reamer based on 3D point cloud processing was proposed. Frist, an algorithm with tangency plane and maximum angle criterion were introduced in the proposed algorithm to obtain the boundary point cloud of acetabular reamer based on boundary characteristics of the tooth holes. Second, the boundary point cloud was partitioned into individual tooth hole point clouds by K-means clustering algorithm, and then the point cloud of each tooth hole boundary was searched by radius nearest neighbor search algorithm to obtain the point cloud of cutting edges belonging to different tooth holes. Finally, RANSAC (RANdom SAmple Consensus) algorithm was used to fit the point cloud of acetabular reamer to a sphere, and Euclidean distance from the point cloud of cutting edges to the center of the fitted sphere was calculated to analyze cutting edge dimensions of acetabular reamer. PCL (Point Cloud Library) was used as a development framework to process the point cloud of acetabular reamer. The accuracy of hole segmentation of the point cloud of acetabular reamer is 100%, and the accuracy of spherical fitting radius of the point cloud of the acetabular reamer is 0.004 mm. Experimental results show that the proposed algorithm has a good effect on the point cloud processing of acetabular reamer, and can effectively realize the dimensional analysis of the cutting edges of acetabular reamer.
Domain adaptation algorithms are widely used for cross-corpus speech emotion recognition. However, many domain adaptation algorithms lose the discrimination of target domain samples while pursuing the minimization of domain discrepancy, resulting in their presence at the decision boundary of the model in a high-density form, which degrades the performance of the model. Based on the above problem, a Decision Boundary Optimized Domain Adaptation (DBODA) method based cross-corpus speech emotion recognition was proposed. Firstly, the features were processed by using convolutional neural networks. Then, the features were fed into the Maximum Nuclear-norm and Mean Discrepancy (MNMD) module to maximize the nuclear norm of the sentiment prediction probability matrix of the target domain while reducing the inter-domain discrepancy, thereby enhancing the discrimination of the target domain samples and optimize the decision boundary. In six sets of cross-corpus experiments set up on the basis of Berlin, eNTERFACE and CASIA speech databases, the average recognition accuracy of the proposed method is 1.68 to 11.01 percentage points ahead of those of the other algorithms, indicating that the proposed model effectively reduces the sample density around the decision boundary and improves the prediction accuracy.
In order to mine the semantic relationships and spatial distribution among features, and further improve the semantic segmentation results of point cloud through multi-feature enhancement, a Multi-Feature Fusion based point cloud scene semantic segmentation Network (MFF-Net) was proposed. In the proposed network, the 3D coordinates and improved edge features were used as input, firstly, the neighbor points of the point were searched by using K-Nearest Neighbor (KNN) algorithm, and the geometric offsets were calculated based on 3D coordinates and coordinate differences among neighbor points, which enhanced the local geometric feature representation of points. Secondly, the distance between the central point and its neighbor points were used to as weighting information to update the edge features, and the spatial attention mechanism was introduced to obtain the semantic information among features. Thirdly, the spatial distribution information among features was further extracted by calculating the differences among neighbor features and using mean pooling operation. Finally, the trilateral features were fused based on attention pooling. Experimental results demonstrate that on S3DIS (Stanford 3D large-scale Indoor Spaces) dataset, the mean Intersection over Union (mIoU) of the proposed network is 67.5%, and the Overall Accuracy (OA) of the proposed network is 87.2%. These two values are 10.2 and 3.4 percentage points higher than those of PointNet++ respectively. It can be seen that MFF-Net can achieve good segmentation results in both large indoor and outdoor scenes.
Aiming at the problems such as long task completion time, high task execution cost and unbalanced system load in task scheduling, a new cloud computing task scheduling method based on Orthogonal Adaptive Whale Optimization Algorithm (OAWOA) was proposed. Firstly, the Orthogonal Experimental Design (OED) was applied to the population initialization and global search stages to improve and maintain the population diversity, avoid the algorithm from falling into local convergence too early. Then, the adaptive exponential decline factor and bidirectional search mechanism were used to further strengthen the global search ability of the algorithm. Finally, the fitness function was optimized to enable the algorithm to achieve multi-objective optimization. Through the simulation experiments, the proposed algorithm was compared with Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) algorithm, Bat Algorithm (BA) and two other improved WOAs. Experimental results show that, when the task scale is 50 and 500, the proposed algorithm achieves better convergence effect, has the total time and total cost of the obtained system executing tasks lower than those of other algorithms, and has the load balancing degree only lower than that of BA. In conclusion, the proposed algorithm shows significant advantages in reducing the total time and cost of system executing tasks and improving the system load balancing.
The main task of text segmentation is to divide the text into several relatively independent text blocks according to the topic relevance. Aiming at the shortcomings of the existing text segmentation models in extracting fine-grained features such as text paragraph structural information, semantic correlation and context interaction, a text segmentation model TS-GCN (Text Segmentation-Graph Convolutional Network) based on Graph Convolutional Network (GCN) was proposed. Firstly, a text graph based on the structural information and semantic logic of text paragraphs was constructed. Then, the semantic similarity attention was introduced to capture the fine-grained correlation between text paragraph nodes, and the information transmission between high-order neighborhoods of text paragraph nodes was realized with the help of GCN, so that the model ability of multi-granularity extraction of text paragraph topic feature representations was enhanced. The proposed model was compared with the representative model CATS (Coherence-Aware Text Segmentation), and its basic model TLT-TS (Two-Level Transformer model for Text Segmentation), which were commonly used as benchmarks for text segmentation task. Experimental results show that TS-GCN’s evaluation index Pk is 0.08 percentage points lower than that of TLT-TS without any auxiliary module on Wikicities dataset. And the proposed model has the Pk value decreased by 0.38 percentage points and 2.30 percentage points respectively on Wikielements dataset compared with CATS and TLT-TS. It can be seen that TS-GCN achieves good segmentation effect.
The subgraph isomorphism problem is a Non-deterministic Polynomial (NP)-complete problem, and the pivoted subgraph isomorphism is a special subgraph isomorphism problem. There are many existing efficient subgraph isomorphism algorithms, but there is no GPU-based search algorithm for the pivoted subgraph isomorphism problem at present, and a large number of unnecessary intermediate results will be generated when the pivoted subgraph matching problem is solved by the existing subgraph isomorphism algorithms. Therefore, a GPU-based pivoted subgraph isomorphism algorithm was proposed. Firstly, through a novel coding tree method, nodes were encoded by the combination of node labels, degrees and the structural features of node neighbors. And the query graph nodes were pruned on GPU in parallel, so that the size of search space tree generated by the data graph candidate nodes was significantly reduced. Then, the candidate nodes of the query graph node were visited level by level, and the unsatisfied nodes were filtered out. Finally, the obtained subgraph was verified whether it was an isomorphic subgraph of the query graph, and the search of pivoted subgraph isomorphism was realized efficiently. Experimental results show that compared with GPU-friendly Subgraph Matching (GpSM) algorithm, the proposed algorithm has the execution time reduced by one-half, and the proposed algorithm can efficiently perform the pivoted subgraph isomorphism search with scalability. The proposed pivoted subgraph isomorphism algorithm can reduce the time required to solve the pivoted subgraph isomorphism problem, while reducing GPU memory consumption and improving the performance of algorithm.