The Experts below are selected from a list of 8775 Experts worldwide ranked by ideXlab platform
Mohamed Nounou - One of the best experts on this subject based on the ideXlab platform.
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Multiscale gaussian process regression based generalized likelihood ratio test for fault detection in water distribution networks
Engineering Applications of Artificial Intelligence, 2019Co-Authors: Radhia Fazai, Hazem Nounou, Majdi Mansouri, Kamal Abodayeh, Vicenc Puig, M Noori I Raouf, Mohamed NounouAbstract:Abstract This paper proposes a new leak/contaminant detection approach that aims to enhance the monitoring of water distribution network (WDN). The developed method relies on using machine learning (e.g Gaussian process regression (GPR)) as a modeling framework and generalized likelihood ratio (GLRT) for detection purposes. To improve the performances of the developed GPR model even further, Multiscale Representation of data will be used to develop Multiscale extension of these method. Multiscale Representation is a powerful data analysis technique that presents efficient separation of deterministic characteristics from random noise. Therefore, the Multiscale GPR method, that combines the advantages of the GPR method with those of Multiscale Representation, will be developed to enhance the WDN modeling performance. We develop a new technique for detecting leak/contaminant in WDN using GLRT. For further enhance, the performance of GLRT, an exponentially weighted moving average (EWMA)-GLRT (EWMA-GLRT) chart is developed. The simulation results show that the MSGPR-based EWMA-GLRT method outperforms MSGPR-based GLRT and that both of them provide clear advantages over the neural networks (NN)- and support vector regression (SVR)- and GPR-based GLRT techniques.
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kernel generalized likelihood ratio test for fault detection of biological systems
IEEE Transactions on Nanobioscience, 2018Co-Authors: Majdi Mansouri, Hazem Nounou, Mohamed Nounou, Mohamedfaouzi Harkat, Raoudha Baklouti, Ahmed Ben HamidaAbstract:In this paper, we develop an improved fault detection (FD) technique in order to enhance the monitoring abilities of nonlinear biological processes. Generalized likelihood ratio test (GLRT)-based kernel principal component analysis (KPCA) (called also kernel GLRT) is an effective data-driven technique for monitoring nonlinear processes. However, it is well known that the data collected from complex and multivariate processes are Multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance the process monitoring abilities, we propose to combine the advantages of kernel GLRT and Multiscale Representation using wavelets by developing a Multiscale kernel GLRT (MS-KGLRT) detection chart. The proposed fault detection approach is addressed so that the KPCA is used to compute the model in the feature space and the MS-KGLRT chart is applied to detect the faults. The detection performance of the new chart is studied using two examples, one using synthetic data and the other using biological process representing a Cad System in E. Coli (CSEC) model for detecting small and moderate shifts (offset or bias and drift). The MS-KGLRT chart is used to enhance fault detection of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine, and cadaverine.
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an effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test
Energy, 2018Co-Authors: Majdi Mansouri, Hazem Nounou, Mansour Hajji, Mohamed Trabelsi, Mohamedfaouzi Harkat, Ayman Alkhazraji, Andreas Livera, Mohamed NounouAbstract:Abstract This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a Multiscale Representation. The Multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed Multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.
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monitoring of wastewater treatment plants using improved univariate statistical technique
Process Safety and Environmental Protection, 2018Co-Authors: Imen Baklouti, Hazem Nounou, Ahmed Ben Hamida, Majdi Mansouri, Mohamed NounouAbstract:Abstract Proper operation of the wastewater treatment plants (WWTPs) is crucial in order to maintain the sought effectiveness and desirable water quality. Therefore, the objective of this paper is to develop univariate statistical technique that aims at enhancing the monitoring of wastewater treatment plants using an improved particle filtering (IPF)-based Multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The advantages of the developed technique are fivefold: (i) estimate a nonlinear state variables of WWTPs using IPF technique. The IPF method yields an optimum choice of the sampling distribution, which also accounts for the observed data; (ii) use the dynamical Multiscale Representation to extract accurate deterministic features and decorrelate autocorrelated measurements. (iii) Develop an optimized EWMA (OEWMA) based on the best selection of smoothing parameter (λ) and control width L; (iv) combine the advantages of state estimation technique with MS-OEWMA chart to improve the fault detection in WWTP systems; and (v) investigate the effect of fault types (offset or bias, variance and drift) and fault sizes on the fault detection performances. The developed technique is validated using simulated COST wastewater treatment BSM1 model. The BSM1, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor faults disturbances in a wastewater treatment plant. The detection results are evaluated using three fault detection criteria: missed detection rate (MDR), false alarm rate (FAR) and average run length (ARL1).
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enhanced performance of shewhart charts using Multiscale Representation
Advances in Computing and Communications, 2016Co-Authors: Ziyan M Sheriff, Mohamed NounouAbstract:Monitoring charts play an essential role in statistical process control. Shewhart charts are commonly used due to their computational simplicity, and have seen many extensions that attempt to improve their performance. Most univariate charts operate under the assumption that data follow a normal distribution, are independent and contain only a moderate level of noise. Unfortunately, most practical data violate one or more of these assumptions. Wavelet-based Multiscale Representation of data possess characteristics that can help address these assumptions violations, and may be exploited to improve the performance of the conventional Shewhart chart. In this paper, a Multiscale Shewhart chart is developed to deal with violation of these assumptions. The advantages brought forward by the developed Multiscale Shewhart chart fault detection algorithm are illustrated through simulated examples. The results clearly demonstrate that the developed method is able to provide lower missed detection and comparable false alarm rates under violation of the above mentioned assumptions.
Philippe Salembier - One of the best experts on this subject based on the ideXlab platform.
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hierarchical analysis of remote sensing data morphological attribute profiles and binary partition trees
International Symposium on Memory Management, 2011Co-Authors: Lorenzo Bruzzone, Philippe Salembier, Mauro Dalla Mura, Jocelyn Chanussot, Silvia ValeroAbstract:The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrow's challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of attributes providing a local Multiscale Representation of the information and hence leading to a fine description of the local structures of the image. Using different attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.
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binary partition tree as an efficient Representation for image processing segmentation and information retrieval
IEEE Transactions on Image Processing, 2000Co-Authors: Philippe Salembier, Luis GarridoAbstract:This paper discusses the interest of binary partition trees as a region-oriented image Representation. Binary partition trees concentrate in a compact and structured Representation a set of meaningful regions that can be extracted from an image. They offer a Multiscale Representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this Representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree Representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.
E. Figueredo - One of the best experts on this subject based on the ideXlab platform.
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timeline analysis and wavelet Multiscale analysis of the akari all sky survey at 90 μm
Monthly Notices of the Royal Astronomical Society, 2008Co-Authors: Lingyu Wang, Issei Yamamura, Hiroshi Shibai, Seb Oliver, Matthew Thomson, Nurur Rahman, D. L. Clements, M Rowanrobinson, R Savage, E. FigueredoAbstract:We present a careful analysis of the point-source detection limit of the AKARI All-Sky Survey in the WIDE-S 90-μm band near the North Ecliptic Pole (NEP). Timeline analysis is used to detect IRAS (Infrared Astronomy Satellite) sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90-μm flux of a source. Combined with a robust noise measurement, the point-source flux detection limit at signal-to-noise ratio (S/N) > 5 for a single detector row is 1.1 ± 0.1 Jy which corresponds to a point-source detection limit of the survey of ∼0.4 Jy. Wavelet transform offers a Multiscale Representation of the Time Series Data (TSD). We calculate the continuous wavelet transform of the TSD and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our Multiscale analysis, IRAS sources selected above 4σ can be identified as the only real sources at the Point Source Scales. We also investigate the correlation between the non-IRAS sources detected in timeline analysis and cirrus emission using wavelet transform and contour plots of wavelet power spectrum. It is shown that the non-IRAS sources are most likely to be caused by excessive noise over a large range of spatial scales rather than real extended structures such as cirrus clouds.
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timeline analysis and wavelet Multiscale analysis of the akari all sky survey at 90 micron
arXiv: Astrophysics, 2008Co-Authors: Lingyu Wang, Issei Yamamura, Hiroshi Shibai, Seb Oliver, Matthew Thomson, Nurur Rahman, M Rowanrobinson, R Savage, Dave Clements, E. FigueredoAbstract:We present a careful analysis of the point source detection limit of the AKARI All-Sky Survey in the WIDE-S 90 $\mu$m band near the North Ecliptic Pole (NEP). Timeline Analysis is used to detect IRAS sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90 $\mu$m flux of a source. Combined with a robust noise measurement, the point source flux detection limit at S/N $>5$ for a single detector row is $1.1\pm0.1$ Jy which corresponds to a point source detection limit of the survey of $\sim$0.4 Jy. Wavelet transform offers a Multiscale Representation of the Time Series Data (TSD). We calculate the continuous wavelet transform of the TSD and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our Multiscale analysis, IRAS sources selected above $4\sigma$ can be identified as the only real sources at the Point Source Scales. We also investigate the correlation between the non-IRAS sources detected in Timeline Analysis and cirrus emission using wavelet transform and contour plots of wavelet power spectrum. It is shown that the non-IRAS sources are most likely to be caused by excessive noise over a large range of spatial scales rather than real extended structures such as cirrus clouds.
Zuowei Shen - One of the best experts on this subject based on the ideXlab platform.
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Multiscale Representation of surfaces by tight wavelet frames with applications to denoising
Applied and Computational Harmonic Analysis, 2016Co-Authors: Bin Dong, Qingtang Jiang, Chaoqiang Liu, Zuowei ShenAbstract:Abstract In this paper, we introduce a new Multiscale Representation of surfaces using tight wavelet frames. Both triangular and quadrilateral (quad) surfaces are considered. The Multiscale Representation for triangulated surfaces is generalized from the non-tensor-product tight wavelet frame Representation of functions (of two variables) that were introduced in [1] , while the tensor-product tight frames of continuous linear B-spline from [63] are used for quad surfaces Representation. As one of many possible applications of such Representation, we consider surface denoising as an example at the end of the paper. We propose an analysis based surface denoising model for triangular and quad surfaces. Fast numerical algorithms are also proposed, which is different from the algorithms used in image restoration [50] , [52] due to the nonlinear nature of the proposed tight wavelet frame transforms on surfaces.
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a new Multiscale Representation for shapes and its application to blood vessel recovery
SIAM Journal on Scientific Computing, 2010Co-Authors: Bin Dong, Zuowei Shen, Aichi Chien, Stanley OsherAbstract:In this paper, we will first introduce a novel Multiscale Representation (MSR) for shapes via level set motions and PDEs. Based on the MSR, we will then design a surface inpainting algorithm to recover three-dimensional geometry of blood vessels. Because of the nature of irregular morphology in vessels and organs, both phantom and real inpainting scenarios were tested using our new algorithm. Successful vessel recoveries are demonstrated with numerical estimation of the degree of arteriosclerosis and vessel occlusion.
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a new Multiscale Representation for shapes and its application to blood vessel recovery
arXiv: Analysis of PDEs, 2009Co-Authors: Bin Dong, Zuowei Shen, Aichi Chien, Stanley OsherAbstract:In this paper, we will first introduce a novel Multiscale Representation (MSR) for shapes. Based on the MSR, we will then design a surface inpainting algorithm to recover 3D geometry of blood vessels. Because of the nature of irregular morphology in vessels and organs, both phantom and real inpainting scenarios were tested using our new algorithm. Successful vessel recoveries are demonstrated with numerical estimation of the degree of arteriosclerosis and vessel occlusion.
Miguel Velezreyes - One of the best experts on this subject based on the ideXlab platform.
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integrating spatial information in unsupervised unmixing of hyperspectral imagery using Multiscale Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Maria C Torresmadronero, Miguel VelezreyesAbstract:This paper presents an unsupervised unmixing approach that takes advantage of Multiscale Representation based on nonlinear diffusion to integrate the spatial information in the spectral endmembers extraction from a hyperspectral image. The main advantages of unsupervised unmixing based on Multiscale Representation (UUMR) are the avoidance of matrix rank estimation to determine the number of endmembers and the use of spatial information without employing spatial kernels. Multiscale Representation builds a family of smoothed images where locally spectrally uniform regions can be identified. The Multiscale Representation is extracted solving a nonlinear diffusion partial differential equation (PDE). Locally, homogeneous regions are identified by taking advantage of an algebraic multigrid method used to solve the PDE. Representative spectra for each region are extracted and then clustered to build spectral endmember classes. These classes represent the different spectral components of the image as well as their spectral variability. The number of spectral endmember classes is estimated using the Davies and Bouldin validity index. A quantitative assessment of unmixing approach based on Multiscale Representation is presented using an AVIRIS image captured over Fort. A.P. Hill, Virginia. A comparison of UUMR results with others unmixing techniques is included.
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Multiscale Representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid methods
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Julio M Duartecarvajalino, Guillermo Sapiro, Miguel Velezreyes, P E CastilloAbstract:A fast algorithm for Multiscale Representation and segmentation of hyperspectral imagery is introduced in this paper. The Multiscale/scale-space Representation is obtained by solving a nonlinear diffusion partial differential equation (PDE) for vector-valued images. We use algebraic multigrid techniques to obtain a fast and scalable solution of the PDE and to segment the hyperspectral image following the intrinsic multigrid structure. We test our algorithm on four standard hyperspectral images that represent different environments commonly found in remote sensing applications: agricultural, urban, mining, and marine. The experimental results show that the segmented images lead to better classification than using the original data directly, in spite of the use of simple similarity metrics and piecewise constant approximations obtained from the segmentation maps.
