Mohamed N. Nounou

Texas A&M University at Qatar, Ad Dawḩah, Baladīyat ad Dawḩah, Qatar

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Publications (125)80.19 Total impact

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    ABSTRACT: The process monitoring systems are often utilized in environmental process operations. Many practical applications used for scheduling, planning or operator training are often complex for direct usage in process monitoring. In this paper, it is proposed to use the generalized likelihood ratio (GLR) based principal components analysis (PCA) for process monitoring and fault detection of environmental processes. The objective is to combine the GLR test with PCA model in order to improve the fault detection performance. GLR-based PCA is a multivariate statistical technique used in multivariate statistical process monitoring and fault detection. PCA reduces the dimensionality of the original data by projecting it onto a space with significantly fewer dimensions. It obtains the principal events of variability in a process. If some of these events change, it can be due to a fault in the process. The data are collected from the crop model in order to calculate the PCA model and the thresholds; Hotelling statistic, T squared , Q statistic and GLR test statistic are used in order to detect the faults. It is demonstrated that the performance of faults detection can be improved by combining GLR test and PCA.
    Full-text · Article · Jul 2016
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    ABSTRACT: The process monitoring systems are often utilized in environmental process operations. Many practical applications used for scheduling, planning or operator training are often complex for direct usage in process monitoring. In this paper, it is proposed to use the generalized likelihood ratio (GLR) based principal components analysis (PCA) for process monitoring and fault detection of environmental processes. The objective is to combine the GLR test with PCA model in order to improve the fault detection performance. GLR-based PCA is a multivariate statistical technique used in multivariate statistical process monitoring and fault detection. PCA reduces the dimensionality of the original data by projecting it onto a space with significantly fewer dimensions. It obtains the principal events of variability in a process. If some of these events change, it can be due to a fault in the process. The data are collected from the crop model in order to calculate the PCA model and the thresholds; Hotelling statistic, T squared , Q statistic and GLR test statistic are used in order to detect the faults. It is demonstrated that the performance of faults detection can be improved by combining GLR test and PCA.
    Full-text · Article · Jul 2016
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    Majdi Mansouri · Mohamed Nounou · Hazem Nounou · Nazmul Karim

    Full-text · Article · Jan 2016 · Journal of Loss Prevention in the Process Industries
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    ABSTRACT: In this paper, we propose an Iterated Robust kernel Fuzzy Principal Component Analysis (IRkFPCA), which is the method that attempts to combine the advantages of the state of art methods and use a more accurate multi-objective function for jointly reducing the modeling errors, optimizing the robustness to outliers and improving the time complexity since it does not require the storage and inversion of the covariance matrix to obtain a memory-efficient approximation of kernel PCA. This proposed technique computes iteratively the principal components, which are used for modeling and fault detection. The detection stage is related to the evaluation of residuals, also known as detection indices, which are signals that reveal the fault presence. Those indices are obtained from the analysis of the difference between the process measurements and their estimations using the IRkFPCA technique. The performance of the proposed method is illustrated and compared to Iterated kernel Principal Component Analysis (IkPCA) and Iterated Principal Component Analysis (IPCA) methods through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results of the comparative studies reveal that the developed IRkFPCA method provides a better performance in terms of modeling and fault detection accuracies than the Iterated Robust Fuzzy Principal Component Analysis (IRFPCA) and Iterated kernel Principal Component Analysis (IkPCA) methods; while both methods provide improved accuracy over the Iterated Principal Component Analysis (IPCA) method.
    No preview · Article · Dec 2015 · Journal of Computational Science
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    ABSTRACT: In this paper, we propose an Iterated Robust kernel Fuzzy Principal Component Analysis (IRkFPCA), which is the method that attempts to combine the advantages of the state of art methods and use a more accurate multi-objective function for jointly reducing the modeling errors, optimizing the robustness to outliers and improving the time complexity since it does not require the storage and inversion of the covariance matrix to obtain a memory-efficient approximation of kernel PCA. This proposed technique computes iteratively the principal components, which are used for modeling and fault detection. The detection stage is related to the evaluation of residuals, also known as detection indices, which are signals that reveal the fault presence. Those indices are obtained from the analysis of the difference between the process measurements and their estimations using the IRkFPCA technique. The performance of the proposed method is illustrated and compared to Iterated kernel Principal Component Analysis (IkPCA) and Iterated Principal Component Analysis (IPCA) methods through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results of the comparative studies reveal that the developed IRkFPCA method provides a better performance in terms of modeling and fault detection accuracies than the Iterated Robust Fuzzy Principal Component Analysis (IRFPCA) and Iterated kernel Principal Component Analysis (IkPCA) methods; while both methods provide improved accuracy over the Iterated Principal Component Analysis (IPCA) method.
    No preview · Article · Dec 2015
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    ABSTRACT: This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori, which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented.
    No preview · Article · Dec 2015 · Journal of Process Control
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    ABSTRACT: This paper presents a new measurement-based control design method for cancer chemotherapy. Cancer chemotherapy aims basically at simultaneously eradicating or significantly reducing the number of cancer cells and maintaining tolerable levels of drug concentration and toxicity. To achieve such aim, drugs are often injected into the patient’s body according to a drug schedule specifying the drug dose and delivery time. Several strategies for planning cancer chemotherapy have been developed in the literature, where evolutionary algorithms have been applied to find optimal drug schedules of cancer treatment under constraints on some key treatment parameters such as drug concentration and toxic side effects. In such methods, the amount of drug doses, delivered in the body at each time during the treatment, does not depend on the current drug concentration, toxicity level, and/or number of cancer cells. Successful design of chemotherapy drug scheduling requires the availability of an accurate mathematical model that perfectly predicts the number of cancerous cells and describes effects of treatment. Several models with either complex or simple structures are available in the literature. Complex-structure models are proposed to deeply understand interactions between cancer and normal cells that affect the performance of the cancer chemotherapy. Nevertheless, such complex models are based on a high-order set of differential equations which can be difficult to solve. Simple-structure models, which are often obtained on the basis of some simplifying assumptions, can be viewed only as an approximation of the cancer system. Hence, designing chemotherapy drug schedules on the basis of simplified models may result in unsuccessful cancer treatment. Unlike conventional control strategies for cancer chemotherapy, our attempt in this paper is to address the problem of designing a control system for cancer treatment using a set of frequency-domain data. Hence, a two-degree-of-freedom PID (proportional-integral-derivative) control scheme is proposed to control cancer growth. These PID controllers are designed to simultaneously provide the optimal amount of drug doses to be delivered into the patient’s body according to the current drug concentration and toxicity level, and maintain the drug concentration and toxicity levels within their pre-specified ranges. The proposed cancer control technique is validated through a first simulation example. Another example to control biological systems is also presented to show the feasibility of the proposed method. Simulation results obtained have demonstrated the capability of the proposed control scheme to address cancer chemotherapy problems.
    No preview · Article · Nov 2015

  • No preview · Article · Oct 2015 · Global Advanced Research Journal of Agricultural Science
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    Majdi Mansouri · Onur Avci · Hazem Nounou · Mohamed Nounou
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    ABSTRACT: Structural health monitoring of civil engineering infrastructure involves uncertainties for damage detection, damage identification, damage classification, sensor optimization, safety, durability, reliability, serviceability, performance based engineering, life-cycle performance, risk management, decision making and so on. For incorporating such uncertainties, several filtering techniques accounting for stochasticity can be implemented utilizing collected data from the structures. In this paper, an iterated square root unscented Kalman filter method is proposed for the estimation of the nonlinear state variables of nonlinear structural systems. Various conventional and state-of-the-art state estimation methods are compared for the estimation performance, namely the unscented Kalman filter (UKF), the square-root unscented Kalman filter (SRUKF), the iterated unscented Kalman filter (IUKF) and the iterated square root unscented Kalman filter (ISRUKF), in two comparative studies. In the first study, UKF, IUKF, SRUKF and ISRUKF methods are utilized at a simple non-linear second order LTI system with the aim to predict a two state variables, and to estimate two model parameters. In the second study, UKF, IUKF, SRUKF and ISRUKF techniques are utilized to a complex three degree of freedom spring-mass-dashpot system to predict the displacements and the velocities state variables. They are also used to estimate the model’s hysteretic parameters. Furthermore, the effect of practical challenges (e.g., measurement noise, number of states and parameters to be estimated) on the performances of UKF, IUKF, SRUKF and ISRUKF were investigated. The results of both comparative studies reveal that the ISRUKF method provides a better estimation accuracy than the IUKF method; while both methods provide improved accuracy over the UKF and SRUKF methods. The benefit of the ISRUKF method lies in its ability to provide accuracy related advantages over other estimation methods since it re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state. The results of the comparative studies show also that, for all the techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. The ISRUKF, however, still provides advantages over other methods in terms of the estimation accuracy and convergence.
    Full-text · Article · Sep 2015 · Journal of Civil Structural Health Monitoring
  • Fouzi Harrou · Mohamed N. Nounou · Hazem N. Nounou · Muddu Madakyaru
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    ABSTRACT: Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling’s T2 and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.
    No preview · Article · May 2015 · Journal of Loss Prevention in the Process Industries
  • Majdi Mansouri · Hazem Nounou · Mohamed Nounou

    No preview · Conference Paper · Apr 2015
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    Majdi Mansouri · Avci Onur · Hazem Nounou · Mohamed Nounou
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    ABSTRACT: In this study, the use of improved Unscented Kalman Filter algorithm based on iterated measurement updates is proposed in an attempt to estimate the nonlinear and non-Gaussian state variables (the concentration and temperature) of the Continuously Stirred Tank Reactor (CSTR) process. Various conventional and state-of-the-art state estimation techniques are compared based on their estimation performance on this objective. These techniques are the Unscented Kalman Filter (UKF), the Square-Root Unscented Kalman Filter (SRUKF), the Iterated Unscented Kalman Filter (IUKF) and the developed Iterated Square Root Unscented Kalman Filter (ISRUKF). The results of the study indicate that the ISRUKF technique has better convergence properties than the IUKF technique; and both of them can provide improved accuracy over the UKF and SRUKF techniques. Moreover, ISRUKF technique is able to provide accuracy related advantages over other estimation techniques. Since this approach re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state.
    Full-text · Conference Paper · Mar 2015
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    Majdi Mansouri · Avci Onur · hazem Nounou · Mohamed Nounou
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    ABSTRACT: For computational modeling of biological systems, one of the major challenges is the identification of the model parameters. It is very beneficial to use easily obtained measurements and estimate variables and/or parameters in such systems. For instance, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks. These models can be used to design intervention strategies such as understanding the biological system behavior and curing major illnesses. The study shown in this paper focuses on the parameter identification of biological phenomena modeled by S-systems using Particle Filter (PF). While the nonlinear observed system is assumed to progress according to a probabilistic state space model, the results show that the PF has good convergence properties. It is concluded that the good convergence is due to PF’s ability to deal with highly nonlinear process models.
    Full-text · Conference Paper · Mar 2015
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    Majdi Mansouri · Onur Avci · Hazem Nounou · Mohamed Nounou
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    ABSTRACT: Researchers have been studying the uncertainties unique to civil infrastructure such as redundancy; nonlinearity; interaction with surrounding; heterogeneity; boundaries and support conditions; structural continuity, stability, integrity; life cycle performance expectations and so on. For incorporating such uncertainties, filtering techniques accounting for stochasticity can be implemented employing collected data from the structures. In this paper, an Iterated Square Root Unscented Kalman Filter (ISRUKF) method is proposed for the estimation of the nonlinear state variables of nonlinear structural systems, idealized herein in simplified spring-mass-dashpot. Various conventional and state-of-the-art state estimation methods are compared for the estimation performance, namely the Unscented Kalman Filter (UKF), the Square-Root Unscented Kalman Filter (SRUKF), the Iterated Unscented Kalman Filter (IUKF) and the Iterated Square Root Unscented Kalman Filter (ISRUKF) methods. The comparison reveals that the ISRUKF method provides a better estimation accuracy than the IUKF method; while both methods provide improved accuracy over the UKF and SRUKF methods. The benefit of the ISRUKF method lies in its ability to provide accuracy related advantages over other estimation methods since it re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state.
    Full-text · Conference Paper · Feb 2015
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    B. Wajid · E. Serpedin · M. Nounou · H. Nounou
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    ABSTRACT: This paper presents MARAGAP, a modular approach to reference assisted genome assembly pipeline. MARAGAP uses the principle of Minimum Description Length to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and SNPs in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer the insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs.
    Full-text · Article · Jan 2015 · International Journal of Computational Biology and Drug Design
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    ABSTRACT: This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate. © 2015 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd.
    No preview · Article · Jan 2015 · Asian Journal of Control
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    ABSTRACT: In this paper, we present a new approach based on discrete Fourier transform (DFT) analysis for controller tuning of the closed-loop system with unknown plant. The DFT analysis is used to process the closed-loop measurements collected online to derive the frequency response of an initial closed-loop system that does not provide a good performance. Based on the closed-loop frequency response data, we propose two methods for tuning PID controller parameters according to some desired closed-loop performance specifications. The proposed approach can be applied online because the closed-loop system does not need to be stopped for data collection. The tuning problem of rotor speed controllers of electric drives, is chosen as an example to experimentally validate our proposed method. Specifically, we are interested here in the control of an induction motor. The availability of high-performance computational and storage facilities greatly simplifies the collection of measured data used for controller tuning. The experimental results presented in this paper demonstrate the efficacy and usefulness of the proposed control design method in many industrial applications.
    No preview · Article · Dec 2014
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    Majdi M. Mansouri · Hazem N. Nounou · Mohamed N. Nounou
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    ABSTRACT: In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.
    Preview · Article · Dec 2014
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    ABSTRACT: This paper presents a new technique to design fixed-structure controllers for linear unknown systems using a set of measurements. In model-based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed-loop performance is a challenging task. Hence, a possible alternative to model-based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed-loop frequency-domain data. The principle of such an approach is based on computing the parameters of a fixed-order controller for which the closed-loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre-selected controller structure, which allows for the selection of low-order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.
    No preview · Article · Nov 2014 · Asian Journal of Control
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    ABSTRACT: This paper presents a new model-free technique to design fixed-structure controllers for linear unknown systems. In the current control design approaches, measured data are used to first identify a model of the plant, then a controller is designed based on the identified model. Due to errors associated with the identification process, degradation in the controller performance is expected. Hence, we use the measured data to directly design the controller without the need for model identification. Our objective here is to design measurement-based controllers for stable and unstable systems, even when the closed-loop architecture is unknown. This proposed method can be very useful for many industrial applications. The proposed control methodology is a reference model design approach which aims at finding suitable parameter values of a fixed-order controller so that the closed-loop frequency response matches a desired frequency response. This reference model design problem is formulated as a nonlinear programming problem using the concept of bounded error, which can then be solved to find suitable values of the controller parameters. In addition to the well-known advantages of data-based control methods, the main features of our proposed approach are: (1) the error is guaranteed to be bounded, (2) it enables us to avoid issues related to the use of minimization methods, (3) it can be applied to stable and unstable plants and does not require any knowledge about the closed-loop architecture, and (4) the controller structure can be selected a priori, which means that low-order controllers can be designed. The proposed technique is experimentally validated through a real position control problem of a DC servomotor, where the results demonstrate the efficacy of the proposed method.
    No preview · Article · Aug 2014 · Automatica

Publication Stats

455 Citations
80.19 Total Impact Points

Institutions

  • 2007-2015
    • Texas A&M University at Qatar
      Ad Dawḩah, Baladīyat ad Dawḩah, Qatar
  • 2007-2011
    • Texas A&M University
      • • Department of Electrical and Computer Engineering
      • • Department of Chemical Engineering
      College Station, Texas, United States
  • 2004-2006
    • United Arab Emirates University
      • Department of Chemical and Petroleum Engineering
      Al Ain, Abu Dhabi, United Arab Emirates
  • 1997-2002
    • The Ohio State University
      • Department of Statistics
      Columbus, OH, United States