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A Method for Separating Multisource Partial Discharges in a Substation Based on Selected Bispectra of UHF Signals. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113751] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A method for separating multisource partial discharges (PDs) in a substation is proposed based on selected bispectra of ultrahigh frequency (UHF) electromagnetic waves. Bispectra are sensitive to Gaussian noises and processes of symmetrical distribution. The phase information contained in bispectra can be useful and important for further signal processing. Bifrequencies where Fisher-like class separability measures between signals’ bispectra achieve their maximums are selected as characteristic parameters of the signals. Then, the selected bispectra are utilized for training the radial basis neural network to separate PD UHF signals in a substation. The method is used to analyze simulated UHF signals mixed with Gaussian white noise and frequency-fixed interference, and to separate PD UHF signals that are collected in a 500 kV substation. In order to prove the validity of the proposed separation method, the localization results are compared with the results calculated by time delay sequence, and the proposed separating algorithm is verified in the interference circumstances of a substation. However, the exact location of PD sources cannot be calculated according to the time delay sequence when the PD sources in a substation are close to each other or there are fewer than four antennas for receiving signals.
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Tambouratzis T, Antonopoulos-Domis M, Marseguerra M, Padovani E. On-Line Estimation of Transit Time Using Artificial Neural Networks. NUCL SCI ENG 2017. [DOI: 10.13182/nse98-a1994] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- T. Tambouratzis
- Institute of Nuclear Technology-Radiation Protection, NCSR “Demokritos” Aghia Paraskevi, Athens 153 10, Greece
| | - M. Antonopoulos-Domis
- Institute of Nuclear Technology-Radiation Protection, NCSR “Demokritos” Aghia Paraskevi, Athens 153 10, Greece
| | - M. Marseguerra
- Politecnico di Milano, Department of Nuclear Engineering Via Ponzio 3403, 20133 Milano, Italy
| | - E. Padovani
- Politecnico di Milano, Department of Nuclear Engineering Via Ponzio 3403, 20133 Milano, Italy
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A High-Resolution Demodulation Algorithm for FBG-FP Static-Strain Sensors Based on the Hilbert Transform and Cross Third-Order Cumulant. SENSORS 2015; 15:9928-41. [PMID: 25923938 PMCID: PMC4482008 DOI: 10.3390/s150509928] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Revised: 04/10/2015] [Accepted: 04/20/2015] [Indexed: 11/17/2022]
Abstract
Static strain can be detected by measuring a cross-correlation of reflection spectra from two fiber Bragg gratings (FBGs). However, the static-strain measurement resolution is limited by the dominant Gaussian noise source when using this traditional method. This paper presents a novel static-strain demodulation algorithm for FBG-based Fabry-Perot interferometers (FBG-FPs). The Hilbert transform is proposed for changing the Gaussian distribution of the two FBG-FPs’ reflection spectra, and a cross third-order cumulant is used to use the results of the Hilbert transform and get a group of noise-vanished signals which can be used to accurately calculate the wavelength difference of the two FBG-FPs. The benefit by these processes is that Gaussian noise in the spectra can be suppressed completely in theory and a higher resolution can be reached. In order to verify the precision and flexibility of this algorithm, a detailed theory model and a simulation analysis are given, and an experiment is implemented. As a result, a static-strain resolution of 0.9 nε under laboratory environment condition is achieved, showing a higher resolution than the traditional cross-correlation method.
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The enhanced locating performance of an integrated cross-correlation and genetic algorithm for radio monitoring systems. SENSORS 2014; 14:7541-62. [PMID: 24763254 PMCID: PMC4029646 DOI: 10.3390/s140407541] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 04/20/2014] [Accepted: 04/21/2014] [Indexed: 11/26/2022]
Abstract
The rapid development of wireless broadband communication technology has affected the location accuracy of worldwide radio monitoring stations that employ time-difference-of-arrival (TDOA) location technology. In this study, TDOA-based location technology was implemented in Taiwan for the first time according to International Telecommunications Union Radiocommunication (ITU-R) recommendations regarding monitoring and location applications. To improve location accuracy, various scenarios, such as a three-dimensional environment (considering an unequal locating antenna configuration), were investigated. Subsequently, the proposed integrated cross-correlation and genetic algorithm was evaluated in the metropolitan area of Tainan. The results indicated that the location accuracy at a circular error probability of 50% was less than 60 m when a multipath effect was present in the area. Moreover, compared with hyperbolic algorithms that have been applied in conventional TDOA-based location systems, the proposed algorithm yielded 17-fold and 19-fold improvements in the mean difference when the location position of the interference station was favorable and unfavorable, respectively. Hence, the various forms of radio interference, such as low transmission power, burst and weak signals, and metropolitan interference, was proved to be easily identified, located, and removed.
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Li X, Liu H. Sound Source Localization for HRI Using FOC-Based Time Difference Feature and Spatial Grid Matching. IEEE TRANSACTIONS ON CYBERNETICS 2013; 43:1199-1212. [PMID: 26502430 DOI: 10.1109/tsmcb.2012.2226443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In human-robot interaction (HRI), speech sound source localization (SSL) is a convenient and efficient way to obtain the relative position between a speaker and a robot. However, implementing a SSL system based on TDOA method encounters many problems, such as noise of real environments, the solution of nonlinear equations, switch between far field and near field. In this paper, fourth-order cumulant spectrum is derived, based on which a time delay estimation (TDE) algorithm that is available for speech signal and immune to spatially correlated Gaussian noise is proposed. Furthermore, time difference feature of sound source and its spatial distribution are analyzed, and a spatial grid matching (SGM) algorithm is proposed for localization step, which handles some problems that geometric positioning method faces effectively. Valid feature detection algorithm and a decision tree method are also suggested to improve localization performance and reduce computational complexity. Experiments are carried out in real environments on a mobile robot platform, in which thousands of sets of speech data with noise collected by four microphones are tested in 3D space. The effectiveness of our TDE method and SGM algorithm is verified.
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Ismaili Alaoui EM, Ibn-Elhaj E, Bouyakhf EH. Noise-insensitive image optimal flow estimation using higher-order statistics. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2009; 26:1212-1220. [PMID: 19412240 DOI: 10.1364/josaa.26.001212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
A new algorithm is presented that estimates the displacement vector field from two successive image frames. In the case where the sequence is severely corrupted by additive (Gaussian or not, colored) noise of unknown covariance, then second-order statistics methods do not work well. However, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image optimal flow estimation. Our scheme is based on subpixel optimal flow estimation using the bispectrum in the parametric domain. The displacement vector of a moving object is estimated by solving linear equations involving third-order holograms and the matrix containing the Dirac delta function. To prove the feasibility of the proposed method, we compared it with a phase correlation technique and the nonparametric bispectrum method described in Res. Lett. Signal Process., ID 417915 (2008). Our results show that our method is considerably more immune to the presence of noise.
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Affiliation(s)
- El Mehdi Ismaili Alaoui
- Faculty of Sciences, University Mohamed V Rabat Agdal Morocco, 4 Avenue Ibn Battouta, B.P. 1014 RP, Rabat, Morocco.
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Mandard E, Kouamé D, Battault R, Remenieras JP, Patat F. Methodology for developing a high-precision ultrasound flow meter and fluid velocity profile reconstruction. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2008; 55:161-172. [PMID: 18334322 DOI: 10.1109/tuffc.2008.625] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
This article reports the methodology used to develop a high-precision ultrasound transit time flow meter dedicated to liquid hydrocarbons. This kind of flow meter is designed for custody transfer applications requiring accuracy better than 0.15% of reading. We focus here on certain specific points to achieve this accuracy. The transit time method needs to estimate accurately the time delay between signals received by a pair of transducers. In this study, we review different ways of estimating this time delay. We also propose a specific configuration of the flow meter paths. In particular, this configuration compensates for the swirl phenomenon, which has a significant impact on the accuracy of the flow meter. We also propose a theoretical parametric profile to reconstruct the fluid velocity profile in order to perform in situ diagnosis of the flow. The parameters of the model are estimated from the measurements of the flow meter. Simulations and experimental results showed that this method provides characterization of the flow in disturbed and undisturbed flow conditions.
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Affiliation(s)
- Emmanuelle Mandard
- Univ. of François Rabelais, Laboratoire UltraSons Signaux et Instrumentation, Centre National de la Recherche Scientifique FRE 2448, Tours, France
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Wang Z, He Z, Chen JDZ. Robust time delay estimation of bioelectric signals using least absolute deviation neural network. IEEE Trans Biomed Eng 2005; 52:454-62. [PMID: 15759575 DOI: 10.1109/tbme.2004.843287] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L1-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L1-norm model superior to Lp-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).
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Affiliation(s)
- Zhishun Wang
- Department of Child Psychiatry and Brain Imaging, Columbia University and NYSPI, New York, NY 10032, USA.
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Cerutti S, Carrault G, Cluitmans PJ, Kinie A, Lipping T, Nikolaidis N, Pitas I, Signorini MG. Non-linear algorithms for processing biological signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 1996; 51:51-73. [PMID: 8894391 DOI: 10.1016/0169-2607(96)01762-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.
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Affiliation(s)
- S Cerutti
- Biomedical Engineering Department, Polytechnic University, Milano, Italy.
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Anderson JM, Giannakis GB. Image motion estimation algorithms using cumulants. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 1995; 4:346-357. [PMID: 18289984 DOI: 10.1109/83.366482] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed.
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Affiliation(s)
- J M Anderson
- Dept. of Electr. Eng., Florida Univ., Gainesville, FL
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Passamante A, Farrell ME. Characterizing attractors using local intrinsic dimension via higher-order statistics. PHYSICAL REVIEW. A, ATOMIC, MOLECULAR, AND OPTICAL PHYSICS 1991; 43:5268-5274. [PMID: 9904838 DOI: 10.1103/physreva.43.5268] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
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Petropulu A, Nikias C. The complex cepstrum and bicepstrum: analytic performance evaluation in the presence of Gaussian noise. ACTA ACUST UNITED AC 1990. [DOI: 10.1109/29.57553] [Citation(s) in RCA: 22] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Chiang HH, Nikias C. A new method for adaptive time delay estimation for non-Gaussian signals. ACTA ACUST UNITED AC 1990. [DOI: 10.1109/29.103056] [Citation(s) in RCA: 40] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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