Pub Date : 2017-10-23DOI: 10.23919/EUSIPCO.2017.8081435
S. Koukoura, J. Carroll, Stepha Weiss, A. McDonald
This paper aims to present a methodology for health monitoring wind turbine gearboxes using vibration data. Monitoring of wind turbines is a crucial aspect of maintenance optimisation that is required for wind farms to remain sustainable and profitable. The proposed methodology performs spectral line analysis and extracts health features from harmonic vibration spectra, at various time instants prior to a gear tooth failure. For this, the tachometer signal of the shaft is used to reconstruct the signal in the angular domain. The diagnosis approach is applied to detect gear faults affecting the intermediate stage of the gearbox. The health features extracted show the gradient deterioration of the gear at progressive time instants before the catastrophic failure. A classification model is trained for fault recognition and prognosis of time before failure. The effectiveness of the proposed fault diagnostic and prognostic approach has been tested with industrial data. The above will lay the groundwork of a robust framework for the early automatic detection of emerging gearbox faults. This will lead to minimisation of wind turbine downtime and increased revenue through operational enhancement.
{"title":"Wind turbine gearbox vibration signal signature and fault development through time","authors":"S. Koukoura, J. Carroll, Stepha Weiss, A. McDonald","doi":"10.23919/EUSIPCO.2017.8081435","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081435","url":null,"abstract":"This paper aims to present a methodology for health monitoring wind turbine gearboxes using vibration data. Monitoring of wind turbines is a crucial aspect of maintenance optimisation that is required for wind farms to remain sustainable and profitable. The proposed methodology performs spectral line analysis and extracts health features from harmonic vibration spectra, at various time instants prior to a gear tooth failure. For this, the tachometer signal of the shaft is used to reconstruct the signal in the angular domain. The diagnosis approach is applied to detect gear faults affecting the intermediate stage of the gearbox. The health features extracted show the gradient deterioration of the gear at progressive time instants before the catastrophic failure. A classification model is trained for fault recognition and prognosis of time before failure. The effectiveness of the proposed fault diagnostic and prognostic approach has been tested with industrial data. The above will lay the groundwork of a robust framework for the early automatic detection of emerging gearbox faults. This will lead to minimisation of wind turbine downtime and increased revenue through operational enhancement.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"385 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121246769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-23DOI: 10.23919/EUSIPCO.2017.8081650
William Coventry, C. Clemente, J. Soraghan
Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially averaging polynomial spacetime covariance matrices. This is shown to restore the rank of the polynomial source covariance matrix. In the application of the polynomial MUSIC algorithm, the spatially smoothed spacetime covariance matrix greatly enhances the direction of arrival estimate in the presence of strongly correlated sources. Simulation results are described shows the performance improvement gained using the new approach compared to the conventional non-smoothed method.
{"title":"Enhancing polynomial MUSIC algorithm for coherent broadband sources through spatial smoothing","authors":"William Coventry, C. Clemente, J. Soraghan","doi":"10.23919/EUSIPCO.2017.8081650","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081650","url":null,"abstract":"Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially averaging polynomial spacetime covariance matrices. This is shown to restore the rank of the polynomial source covariance matrix. In the application of the polynomial MUSIC algorithm, the spatially smoothed spacetime covariance matrix greatly enhances the direction of arrival estimate in the presence of strongly correlated sources. Simulation results are described shows the performance improvement gained using the new approach compared to the conventional non-smoothed method.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134359977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-23DOI: 10.23919/EUSIPCO.2017.8081340
Johan Sward, Filip Elvander, A. Jakobsson
In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.
{"title":"Designing optimal sampling schemes","authors":"Johan Sward, Filip Elvander, A. Jakobsson","doi":"10.23919/EUSIPCO.2017.8081340","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081340","url":null,"abstract":"In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-03DOI: 10.23919/EUSIPCO.2017.8081511
Colm O'Reilly, M. Köküer, P. Jančovič, R. Drennan, N. Harte
Zoologists have long studied species distinctions, but until recently a quantitative system which could be applied to all birds which satisfies rigor and repeatability was absent from the zoology literature. A system which uses morphology, acoustic and plumage evidence to review species status of bird populations was presented by Tobias et al. The acoustic evidence in that work was extracted using manual inspection of spectrograms. The current work seeks to automate this process. Signal processing techniques are employed in this paper to automate the extraction of the acoustic features: maximum, minimum and peak frequency, and bandwidth. YIN-bird, a pitch detection algorithm optimized for birds, and sine-track method, successfully applied to bird species recognition previously, are the automatic methods employed. The performance of automatic methods is compared to the manual method currently used by zoologists. Both methods are well suited to this task, and demonstrate the strong potential to begin to automate the task of acoustic comparison of bird species.
{"title":"Automatic frequency feature extraction for bird species delimitation","authors":"Colm O'Reilly, M. Köküer, P. Jančovič, R. Drennan, N. Harte","doi":"10.23919/EUSIPCO.2017.8081511","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081511","url":null,"abstract":"Zoologists have long studied species distinctions, but until recently a quantitative system which could be applied to all birds which satisfies rigor and repeatability was absent from the zoology literature. A system which uses morphology, acoustic and plumage evidence to review species status of bird populations was presented by Tobias et al. The acoustic evidence in that work was extracted using manual inspection of spectrograms. The current work seeks to automate this process. Signal processing techniques are employed in this paper to automate the extraction of the acoustic features: maximum, minimum and peak frequency, and bandwidth. YIN-bird, a pitch detection algorithm optimized for birds, and sine-track method, successfully applied to bird species recognition previously, are the automatic methods employed. The performance of automatic methods is compared to the manual method currently used by zoologists. Both methods are well suited to this task, and demonstrate the strong potential to begin to automate the task of acoustic comparison of bird species.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128851479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081374
Hassan Mortada, V. Mazet, C. Soussen, C. Collet
This paper addresses the delayed (or anechoic) source separation problem in the case of parameterized deterministic sources. An alternating least square scheme is proposed to estimate the source parameters, the mixing coefficients and the delays. For the challenging delay parameter we adapt a sparse approximation strategy. A first algorithm considers discrete delays; then an extension, inspired by the recent sparse deconvolution literature, allows for continuous delay estimation. Numerical simulations demonstrate the effectiveness of the proposed algorithms compared to state-of-the-art methods for highly correlated Gaussian sources.
{"title":"Separation of delayed parameterized sources","authors":"Hassan Mortada, V. Mazet, C. Soussen, C. Collet","doi":"10.23919/EUSIPCO.2017.8081374","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081374","url":null,"abstract":"This paper addresses the delayed (or anechoic) source separation problem in the case of parameterized deterministic sources. An alternating least square scheme is proposed to estimate the source parameters, the mixing coefficients and the delays. For the challenging delay parameter we adapt a sparse approximation strategy. A first algorithm considers discrete delays; then an extension, inspired by the recent sparse deconvolution literature, allows for continuous delay estimation. Numerical simulations demonstrate the effectiveness of the proposed algorithms compared to state-of-the-art methods for highly correlated Gaussian sources.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127083054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081198
Raphael Bacher, F. Chatelain, O. Michel
In this paper, a target detection procedure with global error control is proposed. The novelty of this approach consists in taking into account spatial structures of the target while ensuring proper error control over pixelwise errors. A generic framework is discussed and a method based on this framework is implemented. Results on simulated data show conclusive gains in detection power for a nominal control level. The method is also applied on real data produced by the astronomical instrument MUSE.
{"title":"Global error control procedure for spatially structured targets","authors":"Raphael Bacher, F. Chatelain, O. Michel","doi":"10.23919/EUSIPCO.2017.8081198","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081198","url":null,"abstract":"In this paper, a target detection procedure with global error control is proposed. The novelty of this approach consists in taking into account spatial structures of the target while ensuring proper error control over pixelwise errors. A generic framework is discussed and a method based on this framework is implemented. Results on simulated data show conclusive gains in detection power for a nominal control level. The method is also applied on real data produced by the astronomical instrument MUSE.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125777852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081159
Mathieu Fontaine, C. Vanwynsberghe, A. Liutkus, R. Badeau
In this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed α-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations.
{"title":"Scalable source localization with multichannel α-stable distributions","authors":"Mathieu Fontaine, C. Vanwynsberghe, A. Liutkus, R. Badeau","doi":"10.23919/EUSIPCO.2017.8081159","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081159","url":null,"abstract":"In this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed α-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"64 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081439
Nacerredine Lassami, K. Abed-Meraim, A. Aïssa-El-Bey
In this paper, we focus on tracking the signal subspace under a sparsity constraint. More specifically, we propose a two-step approach to solve the considered problem whether the sparsity constraint is on the system weight matrix or on the source signals. The first step uses the OPAST algorithm for an adaptive extraction of an orthonormal basis of the principal subspace, then an estimation of the desired weight matrix is done in the second step, taking into account the sparsity constraint. The resulting algorithms: SS-OPAST and DS-OPAST have low computational complexity (suitable in the adaptive context) and they achieve both good convergence and estimation performance as illustrated by our simulation experiments for different application scenarios.
{"title":"Low cost subspace tracking algorithms for sparse systems","authors":"Nacerredine Lassami, K. Abed-Meraim, A. Aïssa-El-Bey","doi":"10.23919/EUSIPCO.2017.8081439","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081439","url":null,"abstract":"In this paper, we focus on tracking the signal subspace under a sparsity constraint. More specifically, we propose a two-step approach to solve the considered problem whether the sparsity constraint is on the system weight matrix or on the source signals. The first step uses the OPAST algorithm for an adaptive extraction of an orthonormal basis of the principal subspace, then an estimation of the desired weight matrix is done in the second step, taking into account the sparsity constraint. The resulting algorithms: SS-OPAST and DS-OPAST have low computational complexity (suitable in the adaptive context) and they achieve both good convergence and estimation performance as illustrated by our simulation experiments for different application scenarios.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133770408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081639
Marielle Malfante, M. Mura, J. Mars, J. Métaxian
The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.
{"title":"Machine learning for automatic classification of volcano-seismic signatures","authors":"Marielle Malfante, M. Mura, J. Mars, J. Métaxian","doi":"10.23919/EUSIPCO.2017.8081639","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081639","url":null,"abstract":"The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126388200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-28DOI: 10.23919/EUSIPCO.2017.8081635
É. Grivel, Susan Medina, F. Krief, Jean-Rémy Falleri, G. Ferré, Laurent Réveillère, D. Négru
In this article, we share our positive experience about the creation of a Telecom showcase in our engineering school, which is an exhibition of old technology to help students learn about previous habits and think about some of the consequences of rapid innovation. This project was done in collaboration with industrial partners such as Thales and Orange. It includes the following steps: collecting objects, organizing and rendering the objects accessible to students, disseminating the history of the telecommunications industry by using a website and quizzes and helping students see how the telecommunications industry and engineers have contributed to social and cultural evolution. This exhibit is particularly useful for the Minute Telecom, inspired from the Minute Physics, where the students are invited to create a video on theoretical concepts such as Shannon's theorem, mobile communication systems or the impact of innovation on user habits.
{"title":"Telecom showcase: An exhibition of ole technology useful for students and teachers","authors":"É. Grivel, Susan Medina, F. Krief, Jean-Rémy Falleri, G. Ferré, Laurent Réveillère, D. Négru","doi":"10.23919/EUSIPCO.2017.8081635","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081635","url":null,"abstract":"In this article, we share our positive experience about the creation of a Telecom showcase in our engineering school, which is an exhibition of old technology to help students learn about previous habits and think about some of the consequences of rapid innovation. This project was done in collaboration with industrial partners such as Thales and Orange. It includes the following steps: collecting objects, organizing and rendering the objects accessible to students, disseminating the history of the telecommunications industry by using a website and quizzes and helping students see how the telecommunications industry and engineers have contributed to social and cultural evolution. This exhibit is particularly useful for the Minute Telecom, inspired from the Minute Physics, where the students are invited to create a video on theoretical concepts such as Shannon's theorem, mobile communication systems or the impact of innovation on user habits.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114829887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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