Collision Detection Algorithm of Belt Grinding of the Blisk Based on Improved Octree Segmentation Method

2021 ◽  
Author(s):  
Zhi Huang ◽  
Xing Yang ◽  
Jie Min ◽  
Hongyan Wang ◽  
Pengxuan Wei
Keyword(s):  
Collision Detection ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Belt Grinding ◽  
Detection Model ◽  
Detection Algorithms ◽  
Bladed Disk ◽  
Abrasive Belt ◽  
Algorithm Verification

Abstract In the process of belt grinding aero-engine Blisk(Bladed Disk), the abrasive belt can easily interfere with the Blisk, which will damage the valuable Blisk. Therefore, it is indispensable and significant to study the collision detection of belt grinding the Blisk. However, the application of traditional collision detection algorithms in this complicated realistic scene is difficult to obtain satisfactory results. In order to improve the accuracy and efficiency of the collision detection of grinding the Blisk, a collision detection algorithm based on the improved octree segmentation method is proposed in this paper. Firstly, the Oriented Bounding Box (OBB) is applied to establish the collision detection model for the abrasive belt. Secondly, the traditional octree segmentation method is optimized based on the k-means clustering algorithm, and an improved octree segmentation method is presented, in addition, the flow chart of the collision detection algorithm for belt grinding of the Bliskis given. Finally, algorithm verification and experimental verification are carried out based on a certain type of the Blisk. The results suggest that compared with the traditional method, the method in this paper not only promotes the accuracy of collision detection, but also promotes the efficiency of collision detection, and meets the requirements of object collision detection in this tanglesome scene with both accuracy and speed.

scholarly journals An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 296 ◽  
Author(s):  
Yingying Wang ◽  
Chengsong Yang ◽  
Changqing Zhu ◽  
Kaimeng Ding
Keyword(s):  
Detection Efficiency ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Multiple Watermarking ◽  
Detection Model ◽  
Detection Algorithms ◽  
Geographic Data ◽  
Multiple Watermarks ◽  
Watermark Algorithm ◽  
Made In

Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency.

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

DESIGN AND APPLICATION OF BOUNDING VOLUME HIERARCHY COLLISION DETECTION ALGORITHM BASED ON VIRTUAL SPHERE

2019 ◽  
Vol 19 (07) ◽  
pp. 1940044
Author(s):  
MONAN WANG ◽  
SHAOYONG CHEN ◽  
QIYOU YANG
Keyword(s):  
Collision Detection ◽  
Detection Efficiency ◽  
Vastus Lateralis ◽  
Detection Algorithm ◽  
Virtual Surgery ◽  
Vastus Lateralis Muscle ◽  
Detection Algorithms ◽  
Bounding Volume ◽  
High Detection Efficiency ◽  
Bounding Volume Hierarchy

The result of collision detection is closely related to the further deformation or cutting action of soft tissue. In order to further improve the efficiency and stability of collision detection, in this paper, a collision detection algorithm of bounding volume hierarchy based on virtual sphere was proposed. The proposed algorithm was validated and the results show that the detection efficiency of the bounding volume hierarchy algorithm based on virtual sphere is higher than that of the serial hybrid bounding volume hierarchy algorithm and the parallel hybrid bounding volume hierarchy algorithm. Different collision detection algorithms were tested and the results show that the collision detection algorithm based on virtual sphere has high detection efficiency and good stability. As the number of triangular patches increased, the advantage was more and more obvious. Finally, the proposed algorithm was applied to two large and medium-sized virtual scenes to implement the collision detection between the vastus lateralis muscle, thigh and surgical instrument. Based on the virtual sphere, the collision detection algorithm of bounding volume hierarchy can implement efficient and stable collision detection in a virtual surgery system. Meanwhile, the algorithm can be combined with other acceleration algorithms (such as the multithread acceleration algorithm) to further improve detection efficiency.

Study Based on Hybrid Bounding Volume Hierarchy for Collision Detection in the Virtual Manipulator

2013 ◽  
Vol 454 ◽  
pp. 74-77 ◽  
Author(s):  
Hong Yu Wu ◽  
Zhi Meng Shu ◽  
Yong Guang Liu
Keyword(s):  
Collision Detection ◽  
Simple Algorithm ◽  
Detection Algorithm ◽  
Complex Objects ◽  
Detection Model ◽  
Bounding Volume ◽  
Hydraulic Servo ◽  
On Line ◽  
Simulation Problem ◽  
Bounding Volume Hierarchy

A collision detection algorithm based on hybrid bounding volume hierarchy was proposed using k-DOPs and sphere between complex objects. A simple algorithm to the particular structure of hydraulic servo manipulator was introduced based on the relation of line to line and line to plane to deal with real time collision detection between graphicrobot and operation task in consideration of the bounding volume hierarchy method.Validity of thisalgorithm was proved through experiments . The experimental results show that the proposed collision detection model is simple and fast in calculation,and easy for realization.it can solve on line simulation problem in remote operation for construction robot,and could be applied in simple virtual reality system.

Hierarchy Topic Detection and Hot Topic Identification

2014 ◽  
Vol 701-702 ◽  
pp. 180-186
Author(s):  
Xue Mei Zhou ◽  
Shan Ying Cheng
Keyword(s):  
Public Opinion ◽  
Real Time ◽  
Hierarchical Structure ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Topic Detection ◽  
Topic Identification ◽  
Evaluation Indexes ◽  
Detection Algorithms ◽  
The Hierarchical Structure

Due to the problem that the existing topic detection algorithms can not satisfy accuracy,real time and topic hierarchical clustering at the same time, this article builds a hierarchy topic detection algorithm based on improved single pass clustering algorithm. In addition, using public opinion evaluation indexes to analyze topic temperature,the method proposed in this paper can detect hot topics accurately and timely while showing the hierarchical structure of the topic .

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

Collision Detection Using the GJK Algorithm

2012 ◽  
pp. 253-288 ◽  
Author(s):  
William N. Bittle
Keyword(s):  
Collision Detection ◽  
Detection Algorithm ◽  
Ray Casting ◽  
Detection Algorithms ◽  
Continuous Collision Detection

GJK is a fast and elegant collision detection algorithm. Originally designed to determine the distance between two convex shapes, it has been adapted to collision detection, continuous collision detection, and ray casting. Its versatility, speed, and compactness have allowed GJK to be one of the top choices of collision detection algorithms in a number of fields.

scholarly journals Edge Detection Model Based on Involuntary Tremors and Drifts of the Eye

2007 ◽  
Vol 11 (6) ◽  
pp. 648-654 ◽  
Author(s):  
András Róka ◽  
◽  
Ádám Csapó ◽  
Barna Reskó ◽  
Péter Baranyi
Keyword(s):  
Eye Movements ◽  
Edge Detection ◽  
Ganglion Cells ◽  
Dynamic Properties ◽  
Receptive Fields ◽  
Detection Algorithm ◽  
Point Of View ◽  
Detection Model ◽  
Detection Algorithms

Recent results in retinal research have shown that ganglion cell receptive fields cover the mammalian retina in a mosaic arrangement, with insignificant amounts of overlap in the central fovea. This means that the biological relevance of traditional and widely adapted edge-detection algorithms with convolution-based overlapping operator architectures has been disproved. However, using traditional filters with non-overlapping operator architectures leads to considerable losses in contour information. This paper introduces a novel, tremor- and drift-based edge-detection algorithm that reconciles these differences between the physiology of the retina and the overlapping architectures used by today’s widely adapted algorithms. The algorithm takes into consideration data convergence, as well as the dynamic properties of the retina, by incorporating a model of involuntary eye tremors and drifts and the impulse responses of ganglion cells. Based on the evaluation of the model, two hypotheses are formulated on the highly debated role of involuntary eye tremors: 1) The role of involuntary eye movements has information theoretical implications 2) From an information processing point of view, the functional role of involuntary eye movements extends to more than just the maintenance of action potentials. Involuntary eye-movements may be responsible for the compensation of information losses caused by a non-overlapping receptive field architecture.

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 59
Author(s):  
Gowri Jeyaraman ◽  
Janakiraman Subbiah
Keyword(s):  
Edge Detection ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Cluster Center ◽  
Detection Techniques ◽  
Fuzzy C Means ◽  
Classical Study ◽  
Detection Algorithms ◽  
Initial Cluster ◽  
Canny Algorithm

<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision.  Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

Sign in / Sign up

Export Citation Format

Share Document