scholarly journals Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar

2022 ◽  
Vol 14 (2) ◽  
pp. 294
Author(s):  
Shuo Li ◽  
Jieqiong Ding ◽  
Weirong Liu ◽  
Heng Li ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Continuous Wave ◽  
Great Influence ◽  
Railway Track ◽  
Support Vector ◽  
Data Set ◽  
Statistical Characterization ◽  
Wave Radar ◽  
Track Surface

The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system.

scholarly journals Monitoring and characterization of vibration and shock conditions aboard high-performance marine craft

2018 ◽  
Vol 233 (4) ◽  
pp. 1068-1081
Author(s):  
Manudul Pahansen de Alwis ◽  
Karl Garme
Keyword(s):  
Real Time ◽  
High Speed ◽  
High Performance ◽  
Severity Index ◽  
Irregular Waves ◽  
Vibration Exposure ◽  
Data Set ◽  
Real Time Analysis ◽  
Impact Acceleration ◽  
The Impact

The stochastic environmental conditions together with craft design and operational characteristics make it difficult to predict the vibration environments aboard high-performance marine craft, particularly the risk of impact acceleration events and the shock component of the exposure often being associated with structural failure and human injuries. The different timescales and the magnitudes involved complicate the real-time analysis of vibration and shock conditions aboard these craft. The article introduces a new measure, severity index, indicating the risk of severe impact acceleration, and proposes a method for real-time feedback on the severity of impact exposure together with accumulated vibration exposure. The method analyzes the immediate 60 s of vibration exposure history and computes the severity of impact exposure as for the present state based on severity index. The severity index probes the characteristic of the present acceleration stochastic process, that is, the risk of an upcoming heavy impact, and serves as an alert to the crew. The accumulated vibration exposure, important for mapping and logging the crew exposure, is determined by the ISO 2631:1997 vibration dose value. The severity due to the impact and accumulated vibration exposure is communicated to the crew every second as a color-coded indicator: green, yellow and red, representing low, medium and high, based on defined impact and dose limits. The severity index and feedback method are developed and validated by a data set of 27 three-hour simulations of a planning craft in irregular waves and verified for its feasibility in real-world applications by full-scale acceleration data recorded aboard high-speed planing craft in operation.

A multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains

2018 ◽  
Vol 24 (17) ◽  
pp. 3797-3808 ◽  
Author(s):  
Jing Ning ◽  
Qi Liu ◽  
Huajiang Ouyang ◽  
Chunjun Chen ◽  
Bing Zhang
Keyword(s):  
Monitoring System ◽  
Sensor Fusion ◽  
Small Amplitude ◽  
High Speed ◽  
Railway Track ◽  
Support Vector ◽  
Monitoring Systems ◽  
Operation Condition ◽  
High Speed Trains ◽  
Fusion Framework

Hunting monitoring is very important for high-speed trains to achieve safe operation. But all the monitoring systems are designed to detect hunting only after hunting has developed sufficiently. Under these circumstances, some damage may be caused to the railway track and train wheels. The work reported in this paper aims to solve the detection problem of small amplitude hunting before the lateral instability of high-speed trains occurs. But the information from a single sensor can only reflect the local operation state of a train. So, to improve the accuracy and robustness of the monitoring system, a multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains based on an improved Dempster–Shafer (DS) theory is proposed. The framework consists of a series of steps. Firstly, the method of combining empirical mode decomposition and sample entropy is used to extract features of each operation condition. Secondly, the posterior probability support vector machine is used to get the basic probability assignment. Finally, the DS theory improved by the authors is proposed to get a more accurate detection result. This framework developed by the authors is used on high-speed trains with success and experimental findings are provided. This multi-sensor fusion framework can also be used in other condition monitoring systems on high-speed trains, such as the gearbox monitoring system, from which nonstationary signals are acquired too.

scholarly journals Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Qisong Wang ◽  
Zhening Dong ◽  
Dan Liu ◽  
Tianao Cao ◽  
Meiyan Zhang ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Respiratory Diseases ◽  
Nearest Neighbor ◽  
Continuous Wave ◽  
Respiratory Pattern ◽  
Support Vector ◽  
Numerical Comparison ◽  
K Nearest Neighbor ◽  
Rectangular Window ◽  
Wave Radar

Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76–81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.

scholarly journals A modified YOLOv3 detection method for vision-based water surface garbage capture robot

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142093271
Author(s):  
Xiali Li ◽  
Manjun Tian ◽  
Shihan Kong ◽  
Licheng Wu ◽  
Junzhi Yu
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
High Speed ◽  
Detection Method ◽  
Training Data ◽  
Aquatic Environments ◽  
Valuable Insight ◽  
Data Set ◽  
Detection Algorithms

To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

scholarly journals Efficient Face Recognition System for Operating in Unconstrained Environments

2021 ◽  
Vol 7 (9) ◽  
pp. 161
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Jesus Olivares-Mercado ◽  
Aldo Hernandez-Suarez ◽  
Karina Toscano-Medina ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Speed ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Processing System ◽  
Recognition System ◽  
Support Vector ◽  
The Face ◽  
Facial Recognition System

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

scholarly journals An analysis of the causes of track twist at high speed of driving

2019 ◽  
Vol 22 (22) ◽  
pp. 11-15
Author(s):  
Andrzej Pacana ◽  
Dominika Siwiec
Keyword(s):  
Quality Management ◽  
High Speed ◽  
Service Industries ◽  
Railway Track ◽  
Railway Transport ◽  
Measuring Device ◽  
Ishikawa Diagram ◽  
Railway Infrastructure ◽  
Track Surface

Abstract The research of railway track surfaces is aimed at improving the quality of railway infrastructure and providing the chance of improving the safety of driving. Therefore, it is advisable to constantly monitor track surface, but the research itself is not innovative because it is based on a known procedure. The crucial element are the techniques that allow to delve into the sources of problems. The aim of the article is to use the selected instruments of quality management to analyze the causes of track twist at high (180 km/h) speed of driving. The analysis of the causes of track twist was done on the basis of results from the measurement of one kilometer track section of the Krakow Glowny - Medyka route, which was made by using the TEC measuring device in April 2018. It was inferred that it is impossible to reach 180 km/h without track twist. In order to identify the causes of track twist it was proposed to use the selected sequence of quality management instruments, i.e.: brainstorming, Ishikawa diagram and the 5Why method. The identified causes of track twist include the abrasion of rail, scratches and exploitation point. The analysis and conclusions drawn from it may be useful in the analysis of other problems in railway transport as well as production and service industries.

scholarly journals Real-Time Defects Analyses Using High-Speed Imaging during Aluminum Magnesium Alloy Laser Welding

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1877
Author(s):  
Sabin Mihai ◽  
Diana Chioibasu ◽  
Muhammad Arif Mahmood ◽  
Liviu Duta ◽  
Marc Leparoux ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Yttrium Aluminum Garnet ◽  
Continuous Wave ◽  
Threshold Value ◽  
High Speed Imaging ◽  
Vapor Plume ◽  
Imaging Camera ◽  
Microscopy Images

In this study a continuous wave Ytterbium-doped Yttrium Aluminum Garnet (Yb: YAG) disk laser has been used for welding of AlMg3 casted alloy. A high-speed imaging camera has been employed to record hot vapor plume features during the process. The purpose was to identify a mechanism of pores detection in real-time based on correlations between metallographic analyses and area/intensity of the hot vapor in various locations of the samples. The pores formation and especially the position of these pores had to be kept under control in order to weld thick samples. Based on the characterization of the hot vapor, it has been found that the increase of the vapor area that exceeded a threshold value (18.5 ± 1 mm2) was a sign of pores formation within the weld seam. For identification of the pores’ locations during welding, the monitored element was the hot vapor intensity. The hot vapor core spots having a grayscale level reaching 255 was associated with the formation of a local pore. These findings have been devised based on correlation between pores placement in welds cross-section microscopy images and the hot vapor plume features in those respective positions.

scholarly journals Design and Implementation of 24 GHz Multichannel FMCW Surveillance Radar with a Software-Reconfigurable Baseband

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Eugin Hyun ◽  
Young-Seok Jin ◽  
Jong-Hun Lee
Keyword(s):  
High Speed ◽  
Continuous Wave ◽  
Frequency Synthesizer ◽  
Parallel Architecture ◽  
Gain Control ◽  
Radar System ◽  
Data Logging ◽  
Time Data ◽  
Wave Radar ◽  
24 Ghz

We designed and developed a 24 GHz surveillance FMCW (Frequency Modulated Continuous Wave) radar with a software-reconfigurable baseband. The developed radar system consists of transceiver, two selectable transmit antennas, eight parallel receive antennas, and a back-end module for data logging and to control the transceiver. The architecture of the developed radar system can support various waveforms, gain control of receive amplifiers, and allow the selection of two transmit antennas. To do this, we implemented the transceiver using a frequency synthesizer device and a two-step VGA (Variable Gain Amplifier) along with switch-controlled transmit antennas. To support high speed implementation features along with good flexibility, we developed a back-end module based on a FPGA (Field Programmable Gate Array) with a parallel architecture for the real-time data logging of the beat signals received from a multichannel 24 GHz transceiver. To verify the feasibility of the developed radar system, signal processing algorithms were implemented on a host PC. All measurements were carried out in an anechoic chamber to extract a 3D range-Doppler-angle map and target detections. We expect that the developed software-reconfigurable radar system will be useful in various surveillance applications.

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