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A three-stage automated modal identification framework for bridge parameters based on frequency uncertainty and density clustering

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ENGINEERING STRUCTURES 2022年 255卷 113891-113891页

作者： He, Yi Yang, Judy P. Li, Yi-Feng Chongqing Univ Res Ctr Engn Vibrat & Disaster Prevent Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Natl Yang Ming Chiao Tung Univ Dept Civil Engn Hsinchu 300093 Taiwan Xiamen Acad Bldg Res Grp Co Ltd Xiamen 361005 Fujian Peoples R China

As the automated modal analysis is crucial for a continuous monitoring system, this study proposes a framework for automated modal identification of bridge parameters based on the uncertainty of estimated frequencies and density-based clustering algorithm, which consists of the following three stages: First, the modal parameters and standard deviations of the estimated frequencies are calculated in a wide range of model orders to construct the stabilization diagram using the reference-based covariance-driven stochastic subspace identification algorithm. Second, the criteria of frequency uncertainty and stabilization are adopted to eliminate the spurious modes. Third, for present purpose, the modified version of an unsupervised density-based clustering algorithm is introduced to group physical modes and detect outliers to reach automated identification of bridge modal parameters. From the analysis, it has shown that the proposed framework is powerful in eliminating the spurious modes and robust in the presence of interference caused by spurious modes while a simple procedure for clustering physical modes with desired statistical reliability is employed.

关键词： Automated modal identification Frequency uncertainty dbscan algorithm Spurious mode Bridge modal analysis

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Designing Truss in Architecture: Method and Applications Based on Clustering algorithms and Principal Stress Lines

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COMPUTER-AIDED DESIGN 2022年 151卷

作者： Yu, Zhenya Dai, Hang Shi, Ziying Southeast Univ Sch Architecture Dept Landscape Architecture Nanjing 210096 Peoples R China Southeast Univ Sch Architecture Dept Bldg Technol & Sci Nanjing 210096 Peoples R China

Rationally and effectively balancing the design objectives of architecture and structure is challenging owing to the limitations of existing truss design methods. Inspired by visual design principles, this study proposes a non-black-box, parametric, and integrated solution based on principal stress lines (PSLs). The idea is to mine the potential data features of PSLs by the clustering algorithm. Clustering the endpoints and equipartition points of PSLs is the core operation of this method. The clustering results are then used to reflect the geometric features and distribution characteristics of PSLs. For this purpose, the framework of two clustering algorithms (dbscan and K-means) is adapted with additional functions for sorting, screening, and counting the sample size of specific clusters. Moreover, combined with the layout pattern of linear elements, the truss web number, size, and layout are defined based on the clustering results. In addition, to verify the feasibility of form-finding results, the method is applied to a cantilever truss and two practical projects: Taishan Bridge and the Wing Stairway. Finally, the application potential of this method is demonstrated by comparing its conventional trusses under the same load and support conditions (materials used, maximum displacement, and maximum axial stress).

关键词： Truss design Visual design principle Integrated solution Principal stress lines dbscan algorithm K-means algorithm

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A deep reinforcement learning based searching method for source localization

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INFORMATION SCIENCES 2022年 588卷 67-81页

作者： Zhao, Yong Chen, Bin Wang, XiangHan Zhu, Zhengqiu Wang, Yiduo Cheng, Guangquan Wang, Rui Wang, Rongxiao He, Ming Liu, Yu Natl Univ Def Technol Coll Syst Engn 109 Deya Rd Changsha Hunan Peoples R China Peoples Liberat Army Engn Univ Command & Control Engn Coll Zhengzhou Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China

The localization of hazardous sources (e.g. poisonous gas sources) is an important task regarding the security of human society. To find the unknown source in time, various autonomous source searching methods have mushroomed and been employed over the past decade. This paper designs a fresh source searching approach, namely particle clustering-deep Q-network, PC-DQN, which applies the deep reinforcement learning (DRL) techniques as a source searching approach for the first time. Specifically, the search process is formulated as the partially observable Markov decision process, then converted into the Markov decision process based on the belief state (represented by the particle fil-ter). PC-DQN leverages the density-based spatial clustering of applications with noise (dbscan) algorithm to extract the feature of belief state, and employ the deep Q-network (DQN) algorithm to find the optimal policy for the source searching task. Through the comparison with two baseline methods (i.e. RANDOM and Entrotaxis algo-rithm) under various experimental conditions, the viability of our proposed PC-DQN is tes-tified. Results explicitly reveal that the success rate of the PC-DQN maintains at a high level (beyond 99.6%) in all scenarios in this paper, and the mean search step shows evident supe-riority over baseline methods in most scenarios. Significantly, we also introduce the trans-fer learning concept to reuse the well-trained Q-network into new scenarios. These findings show important implications of the DRL-based approach as an alternative and more effective source searching approach.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Autonomous searching method Deep reinforcement learning dbscan algorithm Entrotaxis algorithm

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A Regionalization Approach Based on the Comparison of Different Clustering Techniques

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10563页

作者： Colmenero, Jose Luis Aguilar Garcia-Miguel, Javier Portela Univ Complutense Madrid UCM Grp Anal Secur & Syst GASS Dept Software Engn & Artificial Intelligence DISIA Fac Comp Sci & Engn Madrid 28040 Spain Univ Complutense Madrid UCM Fac Stat Studies Dept Stat & Data Sci Madrid 28040 Spain

For biodiversity conservation and the development of protected areas, it is essential to create strategic plans that ensure the preservation and sustainable use of natural resources. Biogeography plays a crucial role in supporting these efforts by identifying and categorizing geographic areas (regionalization) that represent different biotas, as well as recognizing patterns in biodiversity distribution. Another application of regionalization is in planning species sampling and inventories. Developing a species list is vital for monitoring and understanding diversity patterns. This study focuses on the Palearctic region, specifically the areas between Morocco, the Iberian Peninsula, and France. Its aim is to compare different clustering algorithms-such as K-means++, dbscan, PD-clustering, Infomap, and federated heuristic optimization based on fuzzy clustering-with a reference regionalization, using environmental and soil data. Various spatial contiguity approaches were applied, including the third-degree polynomial model and principal coordinates. The results demonstrated that the hybrid approach offers a robust solution in the construction of the regions and that K-means++ and PDC produced regions with strong spatial similarity to the reference regionalization, closely aligning with the expected number of regions, especially at the biome level. Our study shows that a purely statistical regionalization can approximate a global reference regionalization, making it reproducible.

关键词： biodiversity biogeography K-means algorithm dbscan algorithm PD clustering algorithm regionalization Infomap fuzzy c-means

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An Action Recognition Method Based on the Voxelization of Point Cloud From FMCW Radar

An Action Recognition Method Based on the Voxelization of Po...

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Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC)

作者： Zheng, Jinfu Xu, Zhimeng Li, Bin Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Fujian POST & TELECOM Planning & Designing Inst C Fuzhou Peoples R China

ISBN: (纸本)9781665460965

This paper proposes a human action recognition method by voxelizing the point clouds acquired from the frequency modulated continuous wave (FMCW) radar. The timecontinuous point clouds representing human action are converted into image sequences by voxelization and mapped to selected coordinate planes, and then identify the actions through a novel proposed neural network for image recognition, named Vision Transformer (ViT). To improve recognition accuracy, a Coordinate Attention (CA) module is used in the ViT, which can acquire more effective information from the processed image sequence. The experimental results show that the human action recognition method proposed in this paper can achieve an accuracy of 95.50% in seven different action scenes.

关键词： FMCW radar voxelization dbscan algorithm ViT human action recognition

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Obstacle Detection of Unmanned Surface Vehicle Based on Lidar Point Cloud Data

Obstacle Detection of Unmanned Surface Vehicle Based on Lida...

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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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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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A New Clustering algorithm for Live Road Surveillance on Highways based on dbscan and Fuzzy Logic

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第8期11卷 580-587页

作者： Alabbas, Hasanain Huszak, Arpad Budapest Univ Technol & Econ Dept Networked Syst & Serv H-1117 Budapest Hungary Al Qasim Green Univ Babylon Iraq

Video streaming over Vehicular Ad Hoc Networks is a promising technique (VANETs), and it has gained great importance in the last few years. The highly dynamic topology of VANETs makes high-quality video streaming very challenging. In order to provide the most useful camera views to the vehicles, clustering and cluster head selection techniques are used. Too frequent camera view changes can be annoying;therefore, we propose a new stable clustering algorithm to ensure a stable live road surveillance service without interruptions for vehicles that do not have enough vision area. In the proposed solution, we integrated Density-Based Spatial Clustering of Applications with Noise (dbscan) with Fuzzy Logic Control (FLC). dbscan is used to form the clusters, while FLC is used to find the best cluster head for the cluster. Different parameters are utilized like density parameters for dbscan, and relative speed, acceleration, leadership degree and vision area for fuzzy logic. Our proposed algorithm showed better results in terms of cluster lifetime and vehicle status change. Our proposed algorithm has been compared with another clustering scheme to prove the effectiveness of our proposed algorithm.

关键词： Vehicular ad hoc networks (VANETs) V2V intelligent transportation systems clustering algorithms road surveillance dbscan algorithm fuzzy logic control

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Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第5期25卷 1612-1623页

作者： Shen, Lan Liu, Min Wang, Chao Guo, Changhao Meijering, Erik Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Univ New South Wales Grad Sch Biomed Engn Sydney NSW 2052 Australia

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

关键词： Junctions Three-dimensional displays Two dimensional displays Biomedical imaging Neurons Biological system modeling Solid modeling Junction point detection tree-like structure reconstruction circular sampling model dbscan algorithm reverse mapping approach

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Design of intelligent acquisition system for moving object trajectory data under cloud computing

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JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 763-773页

作者： Zhang, Yang Asthana, Abhinav Asthana, Sudeep Khanna, Shaweta Mihai, Ioan-Cosmin JSS Acad Tech Educ Noida India Liaoning Equipment Mfg Vocat & Tech Coll Shenyang 110161 Peoples R China Liaoning Radio & TV Univ Shenyang 110034 Peoples R China HSBC Technol India Dept OSS Core Banking Pune Maharashtra India Natl Inst Technol Elect & Instrumentat Engn Dept Silchar Assam India Lovely Profess Univ Sch Chem Engn & Phys Sci Phagwara India Al I Cuza Police Acad Bucharest Romania

In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based dbscan algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

关键词： cloud computing clustering algorithm taxi data collection dbscan algorithm

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