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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...

引用

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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Flight Operations Quality Assurance Based on Clustering Analysis

Flight Operations Quality Assurance Based on Clustering Anal...

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1st Symposium on Aviation Maintenance and Management

作者： Sun, Zhuo Ma, Cunbao Li, Wen Shen, Chunnan Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9783642542336;9783642542329

Flight operations quality assurance (FOQA) can be applied to monitoring and analyzing the data recorded in a flight to improve line operations and safety. In view of the requirements of FOQA and the shortage of general data processing algorithms, clustering analysis and density-based spatial clustering of applications with noise (dbscan) algorithm are studied depending on data mining, and FOQA based on clustering analysis is presented. The process of flight data analysis is discussed with this algorithm. The practicability and universality of dbscan algorithm is validated by the clustering analysis instance of an abnormal data during takeoff.

关键词： Flight operations quality assurance Clustering analysis dbscan algorithm

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Exploring Traffic Patterns of Bicycle Sharing System 18

Exploring Traffic Patterns of Bicycle Sharing System

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Xue, Caoyi Li, Yanxi Univ Elect Sci & Technol China Sch Resources & Environm Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450365123

The bicycle(1) sharing system provides a convenient transportation mode for urban traffic travel. Analyzing the operation mode of the bicycle sharing system through the bicycle station clustering can help the bicycle sharing system operate more efficiently. In this paper, New York City bicycle sharing system data were used. Combined with station location and traffic interaction information, a two-tier clustering method that combines dbscan and K-means clustering algorithms was proposed to achieve bicycle station clustering, to analyze the traffic patterns of bicycle sharing system in different geographical areas. This is used to reflect the user's social and economic activities at different times and locations and it provides technical support for the effective operation of the bicycle sharing system.

关键词： Bike-sharing system clustering analysis K-means algorithm dbscan algorithm

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A Multi-Agent Vehicle Routing System for Garbage Collection 5

A Multi-Agent Vehicle Routing System for Garbage Collection

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5th International Conference on Advanced Computing (ICoAC)

作者： Nambiar, Sindhya K. Idicula, Sumam Mary SCMS Sch Engn & Technol Dept Comp Sci Emakulam India Cochin Univ Sci & Technol Dept Comp Sci Emakulam India

ISBN: (纸本)9781479934485

Vehicle Routing Problem existed in the world for decades and there are different variants of the problem depending upon various scenarios. Attempts have been made to arrive at a generic solution applicable to all scenarios with limited success. Moreover, most of the existing solutions deals with static problems where all data are known a priori and the optimal route are arrived using this base data. But in reality, most of the data used to compute the optimal route varies in time and the result need not necessarily be the optimal route. With the advancement of technology, it is possible to capture these time varying data on a real time basis using Agents and can be incorporated to evolve an optimal route as time progress - transforming the static vehicle routing problem into a dynamic vehicle routing problem. In this project, a garbage management system is taken for study. An overall improvement in the performance of the system can be achieved by having an efficient collection and transportation mechanism. Modified Ant Colony Optimization based approach was used to solve the dynamic vehicle routing problem to finally arrive at the optimal routes to visit these collection points and back to the depot.

关键词： Vehicle Routing Problem (VRP) Static VRP (SVRP) Dynamic VRP (DVRP) Clustering dbscan algorithm K-Means algorithm Ant Colony Optimisation

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Mining Taxi Pick-Up Hotspots Based On Spatial Clustering 4

Mining Taxi Pick-Up Hotspots Based On Spatial Clustering

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IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

作者： Qi, Hong Liu, Panpan Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781538693803

Mining useful information from taxi operation data in order to optimize the dynamic scheduling of taxi is a hot research problem. In this paper, the spatial clustering method is applied to excavate the taxi pick-up hotspots which are analyzed and visualized further. An improved spatial clustering algorithm R-Fdbscan is proposed, which has an additional parameter R for controlling the range of clusters, to fine-grained cluster the GPS data of taxis. The experimental results show that the R-Fdbscan algorithm has advantages in time performance and clustering results. In addition, two concepts, "center of mass" and "heat", are defined respectively as the location and the demand degree to reduce the representation about the taxi pick-up hotspots. R-Fdbscan algorithm is used on the GPS data of 12,000 taxis in 4 days from Beijing. The reduction results visualized on ArcGIS platform show that the pick-up hotspots are highly coincident with the residents' daily regularity.

关键词： Spatial Clustering Pick-Up Hotspots Dynamic Scheduling of Taxis dbscan algorithm ArcGIS

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Risk Assessment Method Combining Trajectory Prediction and Lateral Obstacle Monitoring 9

Risk Assessment Method Combining Trajectory Prediction and L...

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9th International Conference on Traffic and Logistic Engineering (ICTLE)

作者： Yang Wendi Xin Yahang Zhang Xiaoyu Lou Jiaming Wang Tianchen Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sch Automot Studies Shanghai Peoples R China

ISBN: (纸本)9781665427524

This paper carried out the design of the relevant part of the lidar of an enhanced two-way safety assistance system for commercial vehicles, which aims to provide real-time pedestrian position data for ordinary trucks or large trucks when turning right, and calculate the cluster centers formed by pedestrians and vehicles. The positional relationship of the dangerous area formed when turning is used to judge the dangerous situation of the pedestrian's position, to remind the driver to reduce the effect of traffic accidents.

关键词： Lidar visualization clustering dbscan algorithm

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The Method of Detecting AIS Isolated Information Based on Clustering and Distance 2

The Method of Detecting AIS Isolated Information Based on Cl...

引用

2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Li, Yan Zhang, Yingjun Zhu, Feixiang Dalian Maritime Univ Coll Nav Dalian Peoples R China

ISBN: (纸本)9781467390262

For the growing number of Automatic Identification System (AIS) data, people have begun to apply data mining and machine learning techniques to extract valuable information from AIS data. About the AIS information, the analysis of isolated information is of great significance. They represent some position points apparently deviation from the traffic flow, which may represent the abnormal reception of AIS data or the abnormal behavior of the ships. In order to detect AIS isolated information, this paper uses the dbscan algorithm to cluster AIS data which can identify arbitrarily shapes firstly, the main traffic flow of data acquisition area is extracted;then provide a method to judge whether other clusters belonging to the isolated cluster, next propose an isolated point judgment method, apply the method for the noise points identified by the process of clustering, and get eventually isolated points. Finally, through the analysis of these isolated information by overlap chart and database query, the information has certain value to the analysis of the maritime traffic safety by Vessel Traffic Service (VTS) and the analysis of the abnormal behavior of ships.

关键词： data mining dbscan algorithm isolated information AIS data

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Anomaly Detection of Target Dynamics Based on Clustering 15

Anomaly Detection of Target Dynamics Based on Clustering

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15th International Symposium on Pervasive Systems, algorithms and Networks (I-SPAN)

作者： Jian, Zhang Qing, Ye Ti, Zhou Wuhan Digital Engn Inst Wuhan Hubei Peoples R China

ISBN: (纸本)9781538685341

Battlefield data analysis plays an important role in warfare. In this paper, the anomaly detection problem of group target dynamics on the battlefield is taken as the research object,and an anomaly detection process based on dbscan clustering algorithm is designed. The simulation proves that the anomaly detection process has preferably applicability.

关键词： anomaly detection dbscan algorithm

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Clustering algorithm Based on Characteristics of Density Distribution

Clustering Algorithm Based on Characteristics of Density Dis...

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2nd IEEE International Conference on Advanced Computer Control

作者： Zheng Hua Wang Zhenxing Zhang Liancheng Wang Qian Natl Digital Switching Syst Engn & Technol R&D Ct Zhengzhou Peoples R China

ISBN: (纸本)9781424458479

Density-based clustering algorithms, which are important algorithms for the task of class identification in spatial database, have many advantages such as no dependence on the number of clusters, ability to discover clusters with arbitrary shapes and handle noise. However, clustering quality of most density-based clustering algorithms degrades when the clusters are of different densities. To address this issue, this paper brings forward a clustering algorithm based on characteristics of density distribution-CCDD algorithm. Firstly, it divides data space into a number of grids. Secondly, it re-divides data space into many smaller partitions, according to each grid's one-dimensional or multi-dimensional characteristics of density distribution. Finally, it uses an improved dbscan algorithm, which chooses different parameters according to each partition's local density, to cluster respectively. The experimental results show that CCDD algorithm, which is superior in quality and efficiency to dbscan algorithm, can find clusters with arbitrary shapes and different densities in spatial databases with noise.

关键词： data mining clustering density-based clustering dbscan algorithm grid data space partition

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Inferring Road Maps from Sparsely-Sampled GPS Traces

Inferring Road Maps from Sparsely-Sampled GPS Traces

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27th Canadian Conference on Artificial Intelligence (AI)

作者： Qiu, Jia Wang, Ruisheng Wang, Xin Univ Calgary Dept Geomat Engn Calgary AB Canada

ISBN: (纸本)9783319064833;9783319064826

In this paper, we proposed a new segmentation-and-grouping framework for road map inference from sparsely-sampled GPS traces. First, we extended dbscan with the orientation constraint to partition the whole point set of traces to clusters representing road segments. Second, we proposed an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly straight line segments. Third, the line segments are grouped according to the 'Good Continuity' principle of Gestalt Law to form a 'Stroke' for recovering the road map. Experiment results show that our algorithm is robust to noise and sampling rate. In comparison with previous work, our method has advantages to infer the road maps from sparsely-sampled GPS traces.

关键词： Map inference GPS traces dbscan algorithm k-means clustering perceptual grouping

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