Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Design and Implementation of a Web Editing and Publishing System Based on a Semantic Network Generation Algorithm

In order to solve the problem of web editing data mining effectively, a semantic network generation algorithm is proposed. First of all, on the basis of preprocessing the variant short text, the maximum matching distance between short text is calculated by using the dictionary to expand the semantics of the Chinese words, which is used as an index to measure the formal distance between short text. Finally, a weighted method is used to synthesize formal distance and unit semantic distance into text distance, which is applied to the clustering analysis of online comments. The length of the word list is used to punish the distance. Results show that the most popular query topics on the Internet are shopping 10%, entertainment 10%, pornography 12%, computer 9%, research 9%, healthy life 5%, travel 5%, games 5%, family medical 5%, sports 3%, personal economic plan 3%, holiday 1% and others. It is proved that the improved algorithm proposed in this paper is superior to other methods and the clustering performance is significantly improved.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

Research on information classification and storage in cloud computing data center based on group collaboration intelligent clustering

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%.

Volumetric Representation and Sphere Packing of Indoor Space for Three-Dimensional Room Segmentation

Room segmentation is a basic task for the semantic enrichment of point clouds. Recent studies have mainly projected single-floor point clouds to binary images to realize two-dimensional room segmentation. However, these methods have difficulty solving semantic segmentation problems in complex 3D indoor environments, including cross-floor spaces and rooms inside rooms; this is the bottleneck of indoor 3D modeling for non-Manhattan worlds. To make full use of the abundant geometric and spatial structure information in 3D space, a novel 3D room segmentation method that realizes room segmentation directly in 3D space is proposed in this study. The method utilizes volumetric representation based on a VDB data structure and packs an indoor space with a set of compact spheres to form rooms as separated connected components. Experimental results on different types of indoor point cloud datasets demonstrate the efficiency of the proposed method.