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OCEANS Hampton Roads Conference

作者： Deng, Lihui Guo, Tingting Wang, Hongjian Chi, Zhikang Wu, Zhiqiang Yuan, Rubin Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Tianjin Nav & Instrument Inst Tianjin Peoples R China

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Currently, most algorithms are difficult to adapt to the real sea level scene point cloud target detection, in this paper, a navigation obstacle detection algorithm for unmanned surface vehicle (USV) is proposed, which can detect the obstacles during the navigation of USV and provide real-time and effective obstacle information for USV. The navigation obstacle detection algorithm includes four algorithms: laser radar point cloud preprocessing algorithm, forward looking grid network building method, improved RANSAC filtering algorithm, and improved dbscan clustering algorithm. According to the characteristics of the lidar point cloud data, the lidar point cloud preprocessing algorithm is adopted, which is named the multi frame point cloud data processing algorithm. Then the forward looking grid network is established, and the 3D point cloud data is placed on the forward looking grid network for processing, which greatly speeds up the processing speed of point cloud data and improves the real-time performance of obstacle detection. For the point cloud data of lidar that contains many clutter reflections, the point cloud that reflects due to wake waves generated by ship navigation is filtered (filtered), and the improved RANSAC algorithm is used to filter the point cloud to reduce the interference of clutter point cloud data. In this paper, a dbscan algorithm with adaptive parameters is proposed to solve the problem of point cloud target clustering, combined with the scanning characteristics of lidar. In this way, it retains the advantages of traditional dbscan in short distance point cloud clustering, and can correctly cluster long distance point clouds with good results.

关键词： unmanned surface vehicle lidar point cloud data obstacle detection dbscan algorithm

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Data Fusion Based Power Benchmarking for the Cement Manufacturing Process

Data Fusion Based Power Benchmarking for the Cement Manufact...

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Chinese Automation Congress (CAC)

作者： Liu, Zhao Wang, Xiaohong Zhang, Qiang Yuan, Zhugang Univ Jinan Sch Automat & Elect Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781728113128

In order to get the actual power consumption of the cement manufacturing process, the clustering algorithm and linear minimum variance fusion algorithm are proposed to establish the actual power benchmark of the cement manufacturing process. To manipulate the change of clustering number that is due to the variation of operation conditions and noise data, the dbscan algorithm is applied to cluster the power consumption data of each process unit in the cement manufacturing process. Classifications, clustering centers and their variances of power consumption data are obtained. The actual power consumption benchmark of each process unit in the cement manufacturing process is obtained by using the clustering centers and their variances with the linear minimum variance fusion algorithm. The actual power consumption benchmarking obtained by data fusion can reasonably reflect the actual level of power consumption, and reveal power saving potentialities.

关键词： Data Fusion dbscan algorithm Power Consumption Cement Manufacturing Process

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An Improved algorithm of Point Cloud Extraction for Automotive Radar 4

An Improved Algorithm of Point Cloud Extraction for Automoti...

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4th International Conference on Informatics Engineering and Information Science (ICIEIS)

作者： Huang, Fan Zhou, Jianjiang Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing Peoples R China

ISBN: (数字)9781510651999

ISBN: (纸本)9781510651999;9781510651982

In this paper, a point cloud extraction method based on short-range vehicle millimeter wave radar is proposed. This method is based on Cluster CLEAN algorithm. Compared with the traditional peak extraction algorithm, it can obtain more abundant point cloud data, which is conducive to the subsequent target classification. In this paper, the Cluster CLEAN algorithm is improved, and a improved incremental Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is proposed to improve the clustering process of the Cluster CLEAN algorithm. It is not necessary to repeatedly Cluster the past scattering point data in each iteration, and the computational efficiency of the algorithm can be improved by more than 98%. In addition, in this paper, a modified order statistics based multidimensional clustering (OSMC) algorithm is proposed to directly Cluster the clustering results of Cluster CLEAN algorithm in angle dimension, without the need to perform range-velocity-angle clustering after angle dimension estimation, which accelerates the efficiency of the algorithm and reduces the redundancy of the algorithm. It is verified that compared with the traditional peak extraction algorithm, the signal processing algorithm in this paper can increase the number of scattering points from the same target by 2-7 times while maintaining real-time performance.

关键词： Point cloud Clustering CLEAN algorithm Incremental clustering dbscan algorithm OSMC algorithm

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Defining the Boundaries of Urban Built-up Area Based on Taxi Trajectories: a Case Study of Beijing

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JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS 2020年第1期4卷 1-12页

作者： Li, Yuanfu Sun, Qun Ji, Xiaolin Xu, Li Lu, Chuanwei Zhao, Yunpeng Informat Engn Univ Zhengzhou 450001 Peoples R China

The urban built-up area is of great significance for urban planning, construction, and management, and is an important index to measure the degree of urbanization. This study reviews the definitions of the urban built-up area in different countries and analyzes its basic characteristics. The rational and advantages of defining the urban built-up area using taxi trajectory data are described. First, we preprocessed these data and used the density-based spatial clustering of applications with noise (dbscan) algorithm to cluster the trajectory points. The urban built-up area was defined by the boundaries of each cluster of trajectory points, generated using the Delaunay triangulation method. A parameter decision model was assessed to determine the best-suited parameter for the clustering algorithm. The taxi trajectory data in Beijing were taken as the experimental data, and the urban built-up area generated according to the proposed method shows high consistency with remote sensing images. The results with the proposed method show that the urban built-up area of Beijing comprises a main area and several suburban areas. The main part appears in block form, with some bulges distributed along the city's main roads. The suburban areas serve as special functional areas of the city and surrounding towns.

关键词： Urban built-up area Taxi trajectory dbscan algorithm Delaunay triangulation network

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Density Clustering Based on Border-Expanding 10

Density Clustering Based on Border-Expanding

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10th International Conference on Natural Computation (ICNC)

作者： Chen, Dongming Yan, Yun Wang, Dongqi Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781479951512

dbscan is a clustering algorithm based on density. It can divide regions which have a high density for clusters, shield the noise effectively and discover clusters of arbitrary shape and any size from dataset. However, dbscan algorithm needs to traverse dataset to find core objects, so it results in large amount of I/O cost when processing large-scale datasets. A fast algorithm (BEdbscan) is developed which expands the cluster by employing border objects as seeds. Experimental results show that BEdbscan performs obvious efficiency improvement than dbscan algorithm especially when processing large datasets.

关键词： density clustering dbscan algorithm

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Adaptive Block Online Learning Target Tracking Based on Super Pixel Segmentation 9

Adaptive Block Online Learning Target Tracking Based on Supe...

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： Cheng, Yue Li, Jianzeng Mech Engn Coll Dept UAV Shijiazhuang 050000 Hebei Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (dbscan) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

关键词： Target tracking super pixel division dbscan algorithm online learning

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A Lightning Location algorithm Based on Clustering Analysis 1

A Lightning Location Algorithm Based on Clustering Analysis

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1st International Cognitive Cities Conference (IC3)

作者： Li, Tao Zhang, Shuaichi Ren, Yongjun Li, Xiang Ren, Yongzhen Zhang, Can Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Coll Comp & Software Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538650592

Location accuracy is an important measurement for evaluation of lightning locating networks, and the location algorithm directly affect the accuracy of the lightning detection results. According to the existed weakness of arrival time difference in traditional location method such as poor ability of resisting error interference and low location accuracy, and iterative algorithms are easy to diverge, an improved dbscan algorithm is introduced in location calculation. The performance of the proposed location algorithm was confirmed by regional simulation The results showed that Adbscan does not require manual intervention, and has better clustering effect for lightning location results. The lightning location algorithm based on Adbscan overcomes the shortcomings that iterative algorithms are easy to diverge and fall into local optimum, and can solve the lightning points steadily and accurately.

关键词： lightning location clustering analysis time difference of arrival dbscan algorithm

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INVESTIGATION OF HYDROGEN EMBRITTLEMENT IN 304 AUSTENITIC STAINLESS STEEL THROUGH ACOUSTIC EMISSION MONITORING AND DEEP LEARNING

INVESTIGATION OF HYDROGEN EMBRITTLEMENT IN 304 AUSTENITIC ST...

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2024 Pressure Vessels and Piping Conference

作者： Hua, Zhengli Shen, Xi Sun, Chen Xu, Meng Li, Xing Yu, Wentao Zhejiang Univ Inst Proc Equipment Hangzhou Peoples R China Res Inst Co Ltd State Power Investment Corp Beijing 102209 Peoples R China

ISBN: (纸本)9780791888490

In this academic investigation, we employed Acoustic Emission (AE) monitoring and a Convolutional Neural Network (CNN) to scrutinize hydrogen-induced crack behavior in hydrogen-precharged 304 austenitic stainless steel during tensile stress. This study's pivotal findings reveal that AE monitoring adeptly captures sound wave signal alterations induced by material stress, especially during the critical phase of crack initiation, from the late stage of strengthening to the necking stage. Additionally, industrial-grade Computed Tomography (CT) scans corroborated the presence of a singular principal crack during these phases, aligning with the crack type identified through unsupervised clustering analysis of Short-Time Fourier Transform( STFT) -processed AE signals using the Density-Based Spatial Clustering of Applications with Noise( dbscan) algorithm. The developed CNN model, demonstrating a 98.32% accuracy rate in validation, effectively discriminated between the signals corresponding to distinct stages of material damage. This research underlines the efficacy and practicality of integrating AE monitoring with deep learning for hydrogen induced damage detection in materials.

关键词： Hydrogen Embrittlement Acoustic Emission Monitoring Deep Learning Austenitic Stainless Steel dbscan algorithm CNN Model

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An automatic method for identifying the microscopic feature of ray-cell number 20

An automatic method for identifying the microscopic feature ...

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20th Int Conf on Ubiquitous Comp and Communicat (IUCC) / 20th Int Conf on Comp and Information Technology (CIT) / 4th Int Conf on Data Science and Computational Intelligence (DSCI) / 11th Int Conf on Smart Computing, Networking, and Serv (SmartCNS)

作者： Lin, Qizhao Qiu, Jian Wang, Hong Chen, Wenyin Guo, Yong Zhang, Chaofeng Fujian Polytech Informat Technol Coll Internet Things & Artificial Intelligence Fuzhou Peoples R China Southwest Forestry Univ Coll Mat Sci & Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9781665466677

In order to improve the efficiency and objectivity of identification the characteristics of average cell number of coniferous wood and reduce the subjectivity of manual identification of the characteristics, an automatic identification method for the average number of ray cells in coniferous wood is proposed in this paper. Based on this automatic identification method, the microscopic image of coniferous wood is grayed, binarized and denoised successively. The cell number is obtained by labeling the connected region of the image. The Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is improved for counting the ray number. Finally, the average number of ray cells and its characteristics are obtained. Experimental results show that the accuracy of this method is 65%, and the method is feasible, but it still needs to improve the effect of binary image denoising and clustering accuracy, and improve accuracy rate of this method.

关键词： dbscan algorithm Image processing technology wood anatomy average ray number of cells

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An Experimental Comparison of algorithms for Nodes Clustering in a Neural Network of Caenorhabditis Elegans 21st

An Experimental Comparison of Algorithms for Nodes Clusterin...

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Hernandez, Jorge Florez, Hector Univ Dist Francisco Jose de Caldas Bogota Colombia

ISBN: (纸本)9783030870133;9783030870126

Caenorhabditis Elegans (C. Elegans) is a worm, which has had several studies to search its nerve paths. In a neuronal network simulation, it is util to know which is the first weight to assign in each link if it is not presented to determine other characteristics (e.g. distances by weights). Normally, the weight is a heuristic to solve a problem. There is a data set about connections of C. Elegans which is a result of other authors. The weights by the connection are not set in the data set. In this work, we use the data set to determine experimental weights for each connection with four cluster algorithms. The weights are to use in future work. To compare the algorithms, we created several models for each algorithm. We used metrics to evaluate the results for each model. A spectral clustering algorithm was chosen how the better algorithm to generate the weights.

关键词： Clustering Caenorhabditis Elegans MiniBatch K-means algorithm dbscan algorithm Spectral clustering algorithm Clustering feature tree algorithm

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