1
|
Ni B, Song F, Zhao L, Fu Z, Huang Y. Wavelet denoising of fiber optic monitoring signals in permafrost regions. Sci Rep 2024; 14:9085. [PMID: 38643319 PMCID: PMC11032378 DOI: 10.1038/s41598-024-59941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/16/2024] [Indexed: 04/22/2024] Open
Abstract
To address the noise issue in fiber optic monitoring signals in frozen soil areas, this study employs wavelet denoising techniques to process the fiber optic signals. Since existing parameter choices for wavelets are typically based on conventional environments, selecting suitable parameters for frozen soil regions becomes crucial. In this work, an index library is constructed based on commonly used wavelet basis functions in civil engineering. An optimal wavelet basis function is objectively selected through specific criteria. Considering the characteristic of small root mean square error in fiber optic signals in frozen soil areas, a multi-index fusion approach is applied to determine the optimal decomposition level. Field observations validate that denoised signals, with parameters set appropriately, can more accurately identify locations where settlement occurs.
Collapse
Affiliation(s)
- Bowen Ni
- School of Highway Engineering, Institute of Geotechnical Engineering, Chang'an University, Xi'an, People's Republic of China.
- CCCC First Highway Consultants Co., Ltd, Xi'an, People's Republic of China.
| | - Fei Song
- School of Highway Engineering, Institute of Geotechnical Engineering, Chang'an University, Xi'an, People's Republic of China
| | - Liguo Zhao
- CCCC First Highway Consultants Co., Ltd, Xi'an, People's Republic of China
| | - Zhipeng Fu
- CCCC First Highway Consultants Co., Ltd, Xi'an, People's Republic of China
| | - Yongyi Huang
- CCCC First Highway Consultants Co., Ltd, Xi'an, People's Republic of China
| |
Collapse
|
2
|
Forouzanfar M, Safaeipour H, Casavola A. Oscillatory Failure Case detection in flight control systems via wavelets decomposition. ISA TRANSACTIONS 2022; 128:47-53. [PMID: 34887068 DOI: 10.1016/j.isatra.2021.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 06/13/2023]
Abstract
A fault detection design is proposed for addressing the Oscillatory Failure Case (OFC) detection problem, introduced in the joint Airbus-Stellenbosch university aerospace industrial-benchmark competition called at the IFAC 2020 World Congress1. The detection scheme is comprised of an output estimator, a wavelet decomposition and an energy-based denoising method, and the residual evaluation unit. The detection problem of wide frequency range OFCs is also addressed. According to the achieved simulation results, the proposed fault detection method is able to satisfy the competition prescriptions in the frequency range [1 10] Hz for those OFC's having an amplitude greater than 2.3 mm for OFCs at rod position sensor, or 1.4 mA for OFCs at servo input current, regardless of disturbances level, uncertainties and load factor control input. In other cases, faults are detected slightly after the prescribed detection limit, with some interesting exceptions.
Collapse
Affiliation(s)
- M Forouzanfar
- Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
| | - H Safaeipour
- Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
| | - A Casavola
- Department of Informatics, Modelling, Electronics and Systems Engineering (DIMES), University of Calabria Via P. Bucci, 42/C - 87036 Rende (CS), Italy.
| |
Collapse
|
3
|
Wang Y, Li J, Pei Y, Ma Z, Jia Y, Wei YC. An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
4
|
The comprehensive analysis of the determination of wavelet function-level pair for the decomposition and reconstruction of artificial S1 heart signals by using multi-resolution analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
5
|
Ayodele KP, Ogunlade O, Olugbon OJ, Akinwale OB, Kehinde LO. A medical percussion instrument using a wavelet-based method for archivable output and automatic classification. Comput Biol Med 2020; 127:104100. [PMID: 33171290 DOI: 10.1016/j.compbiomed.2020.104100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 10/23/2022]
Abstract
There is no standard instrument for carrying out medical percussion even though the procedure has been in continuous use since 1761. This study developed one such instrument. It generates medical percussion sounds in a reproducible manner and accurately classifies them into one of three classes. Percussion signals were generated using a push-pull solenoid plessor applying mechanical impulses through a polyvinyl chloride plessimeter. Signals were acquired using a National Instruments USB 6251 data acquisition card at a rate of 8.192 kHz through an air-coupled omnidirectional electret microphone located 60 mm from the impact site. Signal acquisition, processing, and classification were controlled by an NVIDIA Jetson TX2 computational device. A complex Morlet wavelet was selected as the base wavelet for the wavelet decomposition using the maximum wavelet energy method. It was also used to generate a scalogram suitable for manual or automatic classification. Automatic classification was achieved using a MobileNetv2 convolutional neural network with 17 inverted residual layers on the basis of 224 × 224 x 1 images generated by downsampling each scalogram. Testing was carried out using five human subjects with impulses applied at three thoracic sites each to elicit dull, resonant, and tympanic signals respectively. Classifier training utilized the Adam algorithm with a learning rate of 0.001, and first and second moments of 0.9 and 0.999 respectively for 100 epochs, with early stopping. Mean subject-specific validation and test accuracies of 95.9±1.6% and 93.8±2.3% respectively were obtained, along with cross-subject validation and test accuracies of 94.9% and 94.0% respectively. These results compare very favorably with previously-reported systems for automatic generation and classification of percussion sounds.
Collapse
Affiliation(s)
- K P Ayodele
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Osun, 220005, Nigeria.
| | - O Ogunlade
- Department of Physiological Sciences, Obafemi Awolowo University, Ile-Ife, Osun, 220005, Nigeria
| | - O J Olugbon
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Osun, 220005, Nigeria
| | - O B Akinwale
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Osun, 220005, Nigeria
| | - L O Kehinde
- Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Ogun, 110124, Nigeria
| |
Collapse
|
6
|
Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals. COMPUTERS 2020. [DOI: 10.3390/computers9040085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.
Collapse
|
7
|
Seydoux L, Balestriero R, Poli P, Hoop MD, Campillo M, Baraniuk R. Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nat Commun 2020; 11:3972. [PMID: 32769972 PMCID: PMC7414231 DOI: 10.1038/s41467-020-17841-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 07/13/2020] [Indexed: 11/09/2022] Open
Abstract
The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas.
Collapse
Affiliation(s)
- Léonard Seydoux
- ISTerre, équipe Ondes et Structures, Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine, 38610, Gières, France.
| | - Randall Balestriero
- Electrical and Computational Engineering, Rice University, 6100 Main MS-134, Houston, TX, 77005, USA
| | - Piero Poli
- ISTerre, équipe Ondes et Structures, Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine, 38610, Gières, France
| | - Maarten de Hoop
- Computational and Applied Mathematics, Rice University, 6100 Main MS-134, Houston, TX, 77005, USA
| | - Michel Campillo
- ISTerre, équipe Ondes et Structures, Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine, 38610, Gières, France
| | - Richard Baraniuk
- Electrical and Computational Engineering, Rice University, 6100 Main MS-134, Houston, TX, 77005, USA
| |
Collapse
|
8
|
Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors. SENSORS 2020; 20:s20133721. [PMID: 32635170 PMCID: PMC7374499 DOI: 10.3390/s20133721] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/01/2020] [Accepted: 07/01/2020] [Indexed: 11/17/2022]
Abstract
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.
Collapse
|
9
|
Affiliation(s)
- Stephan Schlüter
- Faculty of Mathematics, Natural and Economic Science, Ulm University, Ulm, Germany
| | | |
Collapse
|
10
|
Garg G. A signal invariant wavelet function selection algorithm. Med Biol Eng Comput 2015; 54:629-42. [PMID: 26253283 DOI: 10.1007/s11517-015-1354-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 07/07/2015] [Indexed: 11/28/2022]
Abstract
This paper addresses the problem of mother wavelet selection for wavelet signal processing in feature extraction and pattern recognition. The problem is formulated as an optimization criterion, where a wavelet library is defined using a set of parameters to find the best mother wavelet function. For estimating the fitness function, adopted to evaluate the performance of the wavelet function, analysis of variance is used. Genetic algorithm is exploited to optimize the determination of the best mother wavelet function. For experimental evaluation, solutions for best mother wavelet selection are evaluated on various biomedical signal classification problems, where the solutions of the proposed algorithm are assessed and compared with manual hit-and-trial methods. The results show that the solutions of automated mother wavelet selection algorithm are consistent with the manual selection of wavelet functions. The algorithm is found to be invariant to the type of signals used for classification.
Collapse
Affiliation(s)
- Girisha Garg
- Babu Banarasi Das Institute of Technology, Ghaziabad, Uttar Pradesh, India.
| |
Collapse
|
11
|
Yang X, Zhang H, Zhou H. A Hybrid Methodology for Salinity Time Series Forecasting Based on Wavelet Transform and NARX Neural Networks. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014. [DOI: 10.1007/s13369-014-1243-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
12
|
Anoh OO, Abd-Alhameed RA, Jones SM, Noras JM, Dama YA, Altimimi AM, Ali NT, Alkhambashi MS. Comparison of orthogonal and biorthogonal wavelets for multicarrier systems. 2013 8TH IEEE DESIGN AND TEST SYMPOSIUM 2013. [DOI: 10.1109/idt.2013.6727137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
13
|
Abstract
Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.
Collapse
|
14
|
Acciani G, Brunetti G, Fornarelli G, Giaquinto A. Angular and axial evaluation of superficial defects on non-accessible pipes by wavelet transform and neural network-based classification. ULTRASONICS 2010; 50:13-25. [PMID: 19665161 DOI: 10.1016/j.ultras.2009.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2008] [Revised: 03/25/2009] [Accepted: 07/03/2009] [Indexed: 05/28/2023]
Abstract
In this paper an effective procedure that allows evaluating the dimensions of corrosive flaws on non-accessible pipes is presented. The method is based on the propagation of ultrasound waves, analyzing the informative content of echoes reflected by defects. The approach exploits the properties of the wavelet transform to represent signals by a reduced form. The coefficients of this representation are selected properly by making use of a filter method followed by a genetic algorithm and, then, they feed a neural network classifier which evaluates the dimensions of defects on the pipe under test. Numerical results show low error rates in the evaluation of both angular and axial extension of each flaw. The main advantage offered by the method consists of analyzing long lines of non-accessible pipes, realizing an automatic evaluation of the dimensions of superficial flaws in pipelines.
Collapse
Affiliation(s)
- G Acciani
- Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari Via Orabona, 4 70125 Bari, Italy.
| | | | | | | |
Collapse
|
15
|
Xanthopoulos P, Liu CC, Zhang J, Miller ER, Nair SP, Uthman BM, Kelly K, Pardalos PM. A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:2184-7. [PMID: 19965148 DOI: 10.1109/iembs.2009.5334941] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.
Collapse
Affiliation(s)
- Petros Xanthopoulos
- Industrial and Systems Engineering Department at University of Florida, Gainesville, FL 32611, USA.
| | | | | | | | | | | | | | | |
Collapse
|
16
|
Sánchez-Beato A, Pajares G. Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2008; 17:1817-1826. [PMID: 18784030 DOI: 10.1109/tip.2008.2002833] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Super-resolution (SR) techniques produce a high-resolution image from a set of low-resolution undersampled images. In this paper, we propose a new method for super-resolution that uses sampling theory concepts to derive a noniterative SR algorithm. We first raise the issue of the validity of the data model usually assumed in SR, pointing out that it imposes a band-limited reconstructed image plus a certain type of noise. We propose a sampling theory framework with a prefiltering step that allows us to work with more general data models and also a specific new method for SR that uses Delaunay triangulation and B-splines to build the super-resolved image. The proposed method is noniterative and well posed. We prove its effectiveness against traditional iterative and noniterative SR methods on synthetic and real data. Additionally, we also prove that we can first solve the interpolation problem and then make the deblurring not only when the motion is translational but also when there are rotations and shifts and the imaging system Point Spread Function (PSF) is rotationally symmetric.
Collapse
Affiliation(s)
- Alfonso Sánchez-Beato
- Department of Informática y Automática, Universidad Nacional de Educación a Distancia, Madrid, Spain.
| | | |
Collapse
|
17
|
Lee J, Steele CM, Chau T. Time and time-frequency characterization of dual-axis swallowing accelerometry signals. Physiol Meas 2008; 29:1105-20. [PMID: 18756027 DOI: 10.1088/0967-3334/29/9/008] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Single-axis swallowing accelerometry has shown potential as a non-invasive clinical swallowing assessment tool. Previous swallowing accelerometry research has focused exclusively on the anterior-posterior vibration detected on the surface of the neck. However, hyolaryngeal motion during pharyngeal swallowing occurs in both the anterior-posterior and superior-inferior directions, suggesting that dual-axis accelerometry may be worthy of investigation. With this motivation, the present paper provides a characterization of dual-axis swallowing accelerometry signals from healthy adults in the time and time-frequency domains. Time-domain analysis revealed that signals in the two axes exhibited different probability density functions, and minimal cross-correlation and mutual information. Time-frequency analysis highlighted inter-axis dissimilarities in the scalograms, pseudo-spectra and temporal evolution of low- and high-frequency content. Therefore, it was concluded that the two axes contain different information about swallowing and that the superior-inferior axis should be further investigated in future swallowing accelerometry studies.
Collapse
Affiliation(s)
- J Lee
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.
| | | | | |
Collapse
|