scholarly journals Non-Contact Ultrasonic Guided Wave Inspection of Rails: Next Generation Approach

2016 ◽  
Author(s):  
Stefano Mariani ◽  
Thompson V. Nguyen ◽  
Xuan Zhu ◽  
Simone Sternini ◽  
Francesco Lanza di Scalea ◽  
...  
Keyword(s):  
High Speed ◽  
Rail Steel ◽  
Signal To Noise Ratio ◽  
False Positive Rate ◽  
Impedance Matching ◽  
Roc Curves ◽  
Guided Wave ◽  
Lessons Learned ◽  
Ultrasonic Guided Wave ◽  
Positive Rate

The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, paired with a real-time statistical analysis algorithm, has been realized. This system requires a specialized filtering approach based on electrical impedance matching due to the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. The system’s operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. The prototype based on this technology was tested in October 2014 at the Transportation Technology Center (TTC) in Pueblo, Colorado, and again in November 2015 after incorporating changes based on lessons learned.

Non-Contact Ultrasonic Guided Wave Inspection of Rails

2013 ◽  
Author(s):  
Thompson V. Nguyen ◽  
Stefano Mariani ◽  
Robert R. Phillips ◽  
Piotr Kijanka ◽  
Francesco Lanza di Scalea ◽  
...  
Keyword(s):  
High Speed ◽  
Surface Characterization ◽  
Rail Steel ◽  
Signal To Noise Ratio ◽  
High Reliability ◽  
False Positive Rate ◽  
Experimental Tests ◽  
Roc Curves ◽  
Guided Wave ◽  
Ultrasonic Guided Wave

The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail integrity evaluation. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, in pair with a real-time statistical analysis algorithm, is being developed. This solution presents an improvement over the previously considered laser/air-coupled hybrid system because it replaces the costly and hard-to-maintain laser with a much cheaper, faster, and easier-to-maintain air-coupled transmitter. This system requires a specialized filtering approach due to the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. Many of the system operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Experimental tests have been carried out at the UCSD Rail Defect Farm. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. A field test will be planned later in the year to further validate these results. Extensions of the system are planned to add rail surface characterization to the internal rail defect detection.

High Speed Non-Contact Ultrasonic Guided Wave Inspections of Rails

2014 ◽  
Author(s):  
Stefano Mariani ◽  
Thompson V. Nguyen ◽  
Francesco Lanza di Scalea ◽  
Mahmood Fateh
Keyword(s):  
Defect Detection ◽  
High Speed ◽  
Surface Characterization ◽  
Rail Steel ◽  
Signal To Noise Ratio ◽  
High Reliability ◽  
False Positive Rate ◽  
Roc Curves ◽  
Guided Wave ◽  
Ultrasonic Guided Wave

This paper describes a new system for high-speed and non-contact rail defect detection being developed at the University of California at San Diego (UCSD). A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection has been tested at the UCSD Rail Defect Farm. This solution presents an improvement over the previously considered laser/air-coupled hybrid system because it replaces the costly and hard-to-maintain laser with a much cheaper, faster, and easier-to-maintain air-coupled transmitter. In addition to a real-time statistical analysis algorithm, the prototype uses a specialized filtering approach to mitigate the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. Many of the system operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Extensions of the system capability are planned to add rail surface characterization to the internal rail defect detection to optimize rail grinding operations.

scholarly journals Vision-Based Finger Tapping Detection Without Fingertip Observation

2021 ◽  
Vol 33 (3) ◽  
pp. 484-493
Author(s):  
Shotaro Narita ◽  
◽  
Shingo Kagami ◽  
Koichi Hashimoto
Keyword(s):  
High Speed ◽  
Feature Vector ◽  
False Positive Rate ◽  
Finger Tapping ◽  
Surface Patch ◽  
Operating Characteristics ◽  
Machine Learning Approach ◽  
History Information ◽  
Characteristics Analysis ◽  
Positive Rate

A machine learning approach is investigated in this study to detect a finger tapping on a handheld surface, where the movement of the surface is observed visually; however, the tapping finger is not directly visible. A feature vector extracted from consecutive frames captured by a high-speed camera that observes a surface patch is input to a convolutional neural network to provide a prediction label indicating whether the surface is tapped within the sequence of consecutive frames (“tap”), the surface is still (“still”), or the surface is moved by hand (“move”). Receiver operating characteristics analysis on a binary discrimination of “tap” from the other two labels shows that true positive rates exceeding 97% are achieved when the false positive rate is fixed at 3%, although the generalization performance against different tapped objects or different ways of tapping is not satisfactory. An informal test where a heuristic post-processing filter is introduced suggests that the use of temporal history information should be considered for further improvements.

scholarly journals Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 809
Author(s):  
Miguel Carrasco ◽  
Patricio Toledo ◽  
Nicole D. Tischler
Keyword(s):  
Signal To Noise Ratio ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Keyhole Limpet Hemocyanin ◽  
True Positive Rate ◽  
Cryo Electron Microscopy ◽  
Positive Rate ◽  
Top View ◽  
Non Negative Matrix Factorization

Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

Application of Deep Learning to Detect Ground Deformation in InSAR Data

2020 ◽  
Author(s):  
Pui Anantrasirichai ◽  
Juliet Biggs ◽  
Fabien Albino ◽  
David Bull
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Data Augmentation ◽  
Ground Deformation ◽  
Signal To Noise Ratio ◽  
Matrix Completion ◽  
False Positive Rate ◽  
Synthetic Approach ◽  
Positive Rate ◽  
Machine Learning Applications

<p>Satellite interferometric synthetic aperture radar (InSAR) can be used for measuring surface deformation for a variety of applications. Recent satellite missions, such as Sentinel-1, produce a large amount of data, meaning that visual inspection is impractical. Here we use deep learning, which has proved successful at object detection, to overcome this problem. Initially we present the use of convolutional neural networks (CNNs) for detecting rapid deformation events, which we test on a global dataset of over 30,000 wrapped interferograms at 900 volcanoes. We compare two potential training datasets: data augmentation applied to archive examples and synthetic models. Both are able to detect true positive results, but the data augmentation approach has a false positive rate of 0.205% and the synthetic approach has a false positive rate of 0.036%.  Then, I will present an enhanced technique for measuring slow, sustained deformation over a range of scales from volcanic unrest to urban sources of deformation such as coalfields. By rewrapping cumulative time series, the detection performance is improved when the deformation rate is slow, as more fringes are generated without altering the signal to noise ratio. We adapt the method to use persistent scatterer InSAR data, which is sparse in nature,  by using spatial interpolation methods such as modified matrix completion Finally, future perspectives for machine learning applications on InSAR data will be discussed.</p>

scholarly journals An Empirical Study on Transmission Beamforming for Ultrasonic Guided-Wave Based Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1445 ◽  
Author(s):  
Sergio Cantero-Chinchilla ◽  
Gerardo Aranguren ◽  
Muhammad Khalid Malik ◽  
Josu Etxaniz ◽  
Federico Martín de la Escalera
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Guided Waves ◽  
Signal To Noise Ratio ◽  
Guided Wave ◽  
High Signal ◽  
Efficient Operation ◽  
Structural Health ◽  
Thin Walled Structures ◽  
Ultrasonic Guided Wave

The development of reliable structural health monitoring techniques is enabling a healthy transition from preventive to condition-based maintenance, hence leading to safer and more efficient operation of different industries. Ultrasonic guided-wave based beamforming is one of the most promising techniques, which supports the monitoring of large thin-walled structures. However, beamforming has been typically applied to the post-processing stage (also known as virtual or receiver beamforming) because transmission or physical beamforming requires complex hardware configurations. This paper introduces an electronic structural health monitoring system that carries out transmission beamforming experiments by simultaneously emitting and receiving ultrasonic guided-waves using several transducers. An empirical characterization of the transmission beamforming technique for monitoring an aluminum plate is provided in this work. The high signal-to-noise ratio and accurate angular precision of the physical signal obtained in the experiments suggest that transmission beamforming can increase the reliability and robustnessof this monitoring technique for large structures and in real-world noisy environments.

scholarly journals Improved Defect Detection of Guided Wave Testing Using Split-Spectrum Processing

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4759
Author(s):  
Seyed Kamran Pedram ◽  
Tat-Hean Gan ◽  
Mahdieh Ghafourian
Keyword(s):  
Defect Detection ◽  
Background Noise ◽  
Structural Integrity ◽  
Traditional Approach ◽  
Signal To Noise Ratio ◽  
Guided Wave ◽  
Signal Quality ◽  
Ultrasonic Guided Wave ◽  
Split Spectrum ◽  
Dispersive Propagation

Ultrasonic guided wave (UGW) testing is widely applied in numerous industry areas for the examination of pipelines where structural integrity is of concern. Guided wave testing is capable of inspecting long lengths of pipes from a single tool location using some arrays of transducers positioned around the pipe. Due to dispersive propagation and the multimodal behavior of UGW, the received signal is usually degraded and noisy, that reduce the inspection range and sensitivity to small defects. Therefore, signal interpretation and identifying small defects is a challenging task in such systems, particularly for buried/coated pipes, in that the attenuation rates are considerably higher compared with a bare pipe. In this work, a novel solution is proposed to address this issue by employing an advanced signal processing approach called “split-spectrum processing” (SSP) to minimize the level of background noise and enhance the signal quality. The SSP technique has already shown promising results in a limited trial for a bar pipe and, in this work, the proposed technique has been experimentally compared with the traditional approach for coated pipes. The results illustrate that the proposed technique significantly increases the signal-to-noise ratio and enhances the sensitivity to small defects that are hidden below the background noise.

scholarly journals Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method

2018 ◽  
Vol 8 (10) ◽  
pp. 1815 ◽  
Author(s):  
Seyed Kamran Pedram ◽  
Peter Mudge ◽  
Tat-Hean Gan
Keyword(s):  
Structural Integrity ◽  
Signal To Noise Ratio ◽  
Processing Technique ◽  
Guided Wave ◽  
Signal Processing Technique ◽  
Pipeline Inspection ◽  
Ultrasonic Guided Wave ◽  
Small Defect ◽  
Split Spectrum ◽  
Wave Modes

Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split-spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection.

scholarly journals Voxel-level Classification of Prostate Cancer Using a Four-Compartment Restriction Spectrum Imaging Model

2020 ◽  
Author(s):  
Christine H Feng ◽  
Christopher Charles Conlin ◽  
Kanha Batra ◽  
Ana Rodriguez-Soto ◽  
Roshan Karunamuni ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
False Positive ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Imaging Data ◽  
Pelvic Mri ◽  
Positive Rate ◽  
Model Coefficient ◽  
Spectrum Imaging

Purpose: Diffusion MRI is integral to detection of prostate cancer (PCa), but conventional ADC cannot capture the complexity of prostate tissues. A four-compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4-C1, yielded greatest tumor conspicuity. In this study, RSI4-C1 was evaluated as a quantitative voxel-level classifier of PCa. Methods: This was a retrospective analysis of 46 men who underwent an expanded-acquisition pelvic MRI for suspected PCa. Twenty-three men had no detectable cancer on biopsy or clinical follow-up; the other 23 had biopsy-proven PCa corresponding to a lesion on MRI (PI-RADS category 3-5). High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4-C1 and conventional ADC. Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. Specifically, we compared RSI4-C1 and conventional ADC on mean (and 95% CI) for two metrics: area under the curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90). Classifier images were also compared. Results: RSI4-C1 outperformed conventional ADC, with greater AUC [0.977 (0.951-0.991) vs. 0.921 (0.873-0.949)] and lower FPR90 [0.033 (0.009-0.083) vs. 0.201 (0.131-0.300)]. Conclusion: RSI4-C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection.

scholarly journals Study on the ultrasonic guided wave and online visual monitoring for ultrasonic precise bonding

2021 ◽  
Author(s):  
Yibo Sun ◽  
Mengruo Cao ◽  
Li Zou ◽  
Xinhua Yang
Keyword(s):  
High Speed ◽  
Wavelet Packet ◽  
Melting Zone ◽  
Guided Wave ◽  
Frequency Characteristics ◽  
Bonding Process ◽  
Visual Monitoring ◽  
Time Frequency ◽  
Ultrasonic Bonding ◽  
Ultrasonic Guided Wave

Abstract Ultrasonic precise bonding is an emerging technology in the application of polymer micro-assembly. In the ultrasonic bonding process, the propagation of ultrasound varies with the change of the interfacial polymer physical state. So the ultrasonic guided wave is an effective parameter to in-situ monitor the fusion degree. The ultrasonic guided wave in the ultrasonic bonding process is studied by vibration analysis and online visual monitoring in this paper. The time-frequency characteristics in the ultrasonic guided wave in the bonding process are mainly analyzed by Fast Fourier Transform spectrum analysis, Wavelet Packet Decomposition, and envelope spectrum methods. The polymer interfacial fusion is monitored by the high-speed HD camera in the ultrasonic bonding process. The time-frequency characteristics in the ultrasonic guided wave and the fusion behavior of the thermal melt interface are analyzed and correlated. Results indicate that the change of the interfacial thermal melt state is related to the time-frequency characteristics of the ultrasonic guided wave. The fusion of the melting zone, the rotation of the micro-device, the generation or disappearance of local air bubbles all lead to the changing of the harmonic frequency and intensity in the ultrasonic bonding process.

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