scholarly journals Railway Overhead Contact System Point Cloud Classification

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4961
Author(s):  
Xiao Chen ◽  
Zhuang Chen ◽  
Guoxiang Liu ◽  
Kun Chen ◽  
Lu Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
High Efficiency ◽  
Engineering Application ◽  
Point Clouds ◽  
Experimental Results ◽  
Contact System ◽  
Railway Vehicle ◽  
Return Current ◽  
Low Efficiency

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.

scholarly journals An Optimized Deep Neural Network for Overhead Contact System Recognition from LiDAR Point Clouds

2021 ◽  
Vol 13 (20) ◽  
pp. 4110
Author(s):  
Siping Liu ◽  
Xiaohan Tu ◽  
Cheng Xu ◽  
Lipei Chen ◽  
Shuai Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
High Speed ◽  
Point Clouds ◽  
Model Complexity ◽  
Railway Systems ◽  
Point Cloud Segmentation ◽  
Speed Up ◽  
Low Efficiency ◽  
Irregular Point

As vital infrastructures, high-speed railways support the development of transportation. To maintain the punctuality and safety of railway systems, researchers have employed manual and computer vision methods to monitor overhead contact systems (OCSs), but they have low efficiency. Investigators have also used light detection and ranging (LiDAR) to generate point clouds by emitting laser beams. The point cloud is segmented for automatic OCS recognition, which improves recognition efficiency. However, existing LiDAR point cloud segmentation methods have high computational/model complexity and latency. In addition, they cannot adapt to embedded devices with different architectures. To overcome these issues, this article presents a lightweight neural network EffNet consisting of three modules: ExtractA, AttenA, and AttenB. ExtractA extracts the features from the disordered and irregular point clouds of an OCS. AttenA keeps information flowing in EffNet while extracting useful features. AttenB uses channel and spatialwise statistics to enhance important features and suppress unimportant ones efficiently. To further speed up EffNet and match it with diverse architectures, we optimized it with a generation framework of tensor programs and deployed it on embedded systems with different architectures. Extensive experiments demonstrated that EffNet has at least a 0.57% higher mean accuracy, but with 25.00% and 9.30% lower computational and model complexity for OCS recognition than others, respectively. The optimized EffNet can be adapted to different architectures. Its latency decreased by 51.97%, 56.47%, 63.63%, 82.58%, 85.85%, and 91.97% on the NVIDIA Nano CPU, TX2 CPU, UP Board CPU, Nano GPU, TX2 GPU, and RTX 2,080 Ti GPU, respectively.

scholarly journals High Efficiency Generalized Parallel Counters for Look-Up Table Based FPGAs

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Burhan Khurshid ◽  
Roohie Naaz Mir
Keyword(s):  
Power Dissipation ◽  
High Speed ◽  
High Efficiency ◽  
Critical Path ◽  
Fir Filters ◽  
Path Delay ◽  
Look Up Table ◽  
Improved Performance ◽  
Ip Cores ◽  
Low Efficiency

Generalized parallel counters (GPCs) are used in constructing high speed compressor trees. Prior work has focused on utilizing the fast carry chain and mapping the logic onto Look-Up Tables (LUTs). This mapping is not optimal in the sense that the LUT fabric is not fully utilized. This results in low efficiency GPCs. In this work, we present a heuristic that efficiently maps the GPC logic onto the LUT fabric. We have used our heuristic on various GPCs and have achieved an improvement in efficiency ranging from 33% to 100% in most of the cases. Experimental results using Xilinx 5th-, 6th-, and 7th-generation FPGAs and Stratix IV and V devices from Altera show a considerable reduction in resources utilization and dynamic power dissipation, for almost the same critical path delay. We have also implemented GPC-based FIR filters on 7th-generation Xilinx FPGAs using our proposed heuristic and compared their performance against conventional implementations. Implementations based on our heuristic show improved performance. Comparisons are also made against filters based on integrated DSP blocks and inherent IP cores from Xilinx. The results show that the proposed heuristic provides performance that is comparable to the structures based on these specialized resources.

scholarly journals Integrating UAV and TLS Approaches for Environmental Management: A Case Study of a Waste Stockpile Area

2020 ◽  
Vol 12 (10) ◽  
pp. 1615 ◽  
Author(s):  
Seung Woo Son ◽  
Dong Woo Kim ◽  
Woong Gi Sung ◽  
Jae Jin Yu
Keyword(s):  
Environmental Management ◽  
Point Cloud ◽  
Laser Scanning ◽  
High Efficiency ◽  
Point Clouds ◽  
Fusion Model ◽  
Spatial Features ◽  
Computation Efficiency ◽  
Volume Computation ◽  
Mean Square Errors

A methodology for optimal volume computation for the environmental management of waste stockpiles was derived by integrating the terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) technologies. Among the UAV-based point clouds generated under various flight scenarios, the most accurate point cloud was selected for analysis. The root mean square errors (RMSEs) of the TLS- and UAV-based methods were 0.202 and 0.032 m, respectively, and the volume computation yielded 41,226 and 41,526 m3, respectively. Both techniques showed high accuracy but also exhibited drawbacks in terms of their spatial features and efficiency. The TLS and UAV methods required 800 and 340 min, respectively, demonstrating the high efficiency of the UAV method. The RMSE and volume obtained using the TLS/UAV fusion model were calculated as 0.030 m and 41,232 m3, respectively. The UAV approach generally yielded high point cloud accuracy and volume computation efficiency.

On Study of High Speed Permanent Magnet Synchronous Motor without Iron Core

2013 ◽  
Vol 706-708 ◽  
pp. 882-887
Author(s):  
Ji Zhu Liu ◽  
Yang Jun Wang ◽  
Tao Chen ◽  
Ming Qiang Pan ◽  
Li Guo Chen ◽  
...  
Keyword(s):  
Permanent Magnet ◽  
High Speed ◽  
High Efficiency ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Iron Core ◽  
Torque Density ◽  
Optimized Model ◽  
Low Efficiency ◽  
Design And Manufacture

Iron loss will be rapidly increased when the permanent magnet iron core synchronous motor runs at a high speed, which makes the motor produce so much heat that causes low efficiency of the motor and even burns out the motor. The iron-core-free permanent magnet synchronous motor remedies this defect and has a high efficiency at high speed. This article makes a comparative analysis on the iron-core-free permanent magnet synchronous motor torque density with different slot engagement classifications. The paper puts forward an optimized model of permanent magnet synchronous motor without the iron core. The technology of the permanent magnet synchronous motor without iron core is studied based on this model which provides a method to design and manufacture the iron-core-free permanent magnet synchronous motor.

Rapid Design of DC Motor Speed Control System Based on MATLAB

2015 ◽  
Vol 743 ◽  
pp. 168-171 ◽  
Author(s):  
Xiao Lei Wang ◽  
Tai Yuan Yin ◽  
Jin Tao Chen ◽  
Jian Xun Liang ◽  
Yang Li
Keyword(s):  
Control System ◽  
High Speed ◽  
High Efficiency ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Loop Control ◽  
Rapid Design ◽  
Speed Control System ◽  
Low Efficiency

DC motor speed control system is a typical closed-loop control system ofelectromechanical control subject. This paper presents a fast and efficient developing method ofcontrol system based on MATLAB, overcoming the shortcomings of the low efficiency and longdesign cycle in the traditional control system, and completing the rapid design of DC motor speedcontrol system, with its whole process based on MATLAB through the combination and applicationof the multiple toolboxes of the MATLAB. It applies the System Identification toolbox ofMATLAB to model the DC motor, the Simulink toolbox to simulate the control system, SimulinkDesign Optimization toolbox to optimize the PID parameters automatically, and the RTWtechnology to generate the codes for the DSP target board. Compared with the traditional designmethod, this method is characterized by high-efficiency, high-speed, and easy adjustment, havingcertain significance to the design of other control systems.

scholarly journals Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7558
Author(s):  
Linyan Cui ◽  
Guolong Zhang ◽  
Jinshen Wang
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Engineering Application ◽  
Point Clouds ◽  
Real Point ◽  
3D Point Cloud ◽  
Virtual Camera ◽  
3D Point Clouds ◽  
Cloud Models ◽  
Point Cloud Model

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

scholarly journals An Automatic Spherical Targets Detection Method with Multiple Geometrical Constraints

2020 ◽  
Author(s):  
Fugui Xie ◽  
Tonggang Zhang ◽  
Dan Zhong ◽  
Yuhui Kan
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Radial Direction ◽  
Detection Method ◽  
Point Clouds ◽  
Filter Method ◽  
Target Point ◽  
Potential Target ◽  
Geometrical Constraints ◽  
Random Sample Consensus

Spherical targets are used extensively in the registration and coordinate transformation of the railway point cloud. Thus, it is necessary to accurately detect the spherical targets from the railway point cloud. This paper proposes an automatic spherical targets detection method with multiple geometrical constraints. In this method, possible spherical points are extracted by the improved three points filter method. And possible spherical points are refined according to neighborhood height difference and curvature. Then, the refined possible spherical points are spatially clustered by the Euclidean clustering method and the potential target point clouds can be extracted by constructing the spherical neighborhood according to the cluster centroid. Finally, the ratio constrained random sample consensus (RC-RANSAC) method is proposed in this paper, based on the RANSAC method, to detect the spherical targets in the potential target point clouds. The point cloud scanned from the high-speed railway is taken as experimental data. The spherical targets in the point cloud are detected by this method. The experimental results show that the proposed method can detect the spherical target with and without the background in radial direction.

scholarly journals Multiple Plane Fitting Algorithm to Evaluate the Accuracy of 3D Point Cloud Using Structured Light Measurement

2020 ◽  
Vol 30 (7) ◽  
pp. 12-17
Author(s):  
Thi Kim Cuc Nguyen ◽  
Van Vinh Nguyen ◽  
Xuan Binh Cao
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Structured Light ◽  
Measurement Data ◽  
Point Clouds ◽  
Maximum Relative Error ◽  
Cloud Data ◽  
3D Shape Measurement ◽  
Fitting Algorithm ◽  
Multiple Plane

3D shape measurement by structured light is a high-speed method and capable of profiling complex surfaces. In particular, the processing of measuring data also greatly affects the accuracy of obtained point clouds. In this paper, an algorithm to detect multiple planes on point cloud data was developed based on RANSAC algorithm to evaluate the accuracy of point cloud measured by structural light. To evaluate the accuracy of the point cloud obtained, two-step height parts are used. The planes are detected and the distance between them needs to be measured with high accuracy. Therefore, the distance measurement data between the planes found in the point cloud is compared with the data measured by CMM measurement. The experimental results have shown that the proposed algorithm can identify multiple planes at the same time with a maximum standard deviation of 0.068 (mm) and the maximum relative error is 1.46%.

Automatic Point Clouds Registration Method Based on Mesh Segmentation

2013 ◽  
Vol 423-426 ◽  
pp. 2587-2590
Author(s):  
Li Hua Fan ◽  
Bo Liu ◽  
Bao Ling Xie ◽  
Qi Chen
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
High Speed ◽  
Point Clouds ◽  
Mesh Segmentation ◽  
Registration Method ◽  
Icp Algorithm ◽  
Computer Aided ◽  
Aided Design ◽  
Region Feature

This paper proposes an automatic point clouds registration method based on High-Speed Mesh Segmentation. The proposed method works fast for doing an initial registration and extracting point clouds region feature. First, the features of the point region are used for matching point cloud regions. Second, matched regions sets are classified for calculating transform matrix of initial registration. Based on the initial registration result the Iterative Closest Point (ICP) algorithm which had been used for accuracy registration to composite point cloud pairs will be applied. The proposed registration approach is able to do automatic registration without any assumptions about their initial positions, and avoid the problems of traditional ICP in bad initial estimate. The proposed method plus with ICP algorithm provides an efficient 3D model for computer-aided engineering and computer-aided design.

scholarly journals A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2224 ◽  
Author(s):  
Lipei Chen ◽  
Cheng Xu ◽  
Shuai Lin ◽  
Siqi Li ◽  
Xiaohan Tu
Keyword(s):  
Deep Learning ◽  
Data Processing ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Contact System ◽  
System Component ◽  
Online Data ◽  
Scan Line ◽  
Batch Data

The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.

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