Search results for: robust estimator

Authors: Sofiane Bououden, Ilyes Boulkaibet

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In this paper, a robust model predictive controller (RMPC) for uncertain nonlinear system under actuator saturation is designed to control a DC-DC buck converter in PV pumping application, where this system is subject to actuator saturation and parameter uncertainties. The considered nonlinear system contains a linear constant part perturbed by an additive state-dependent nonlinear term. Based on the saturating actuator property, an appropriate linear feedback control law is constructed and used to minimize an infinite horizon cost function within the framework of linear matrix inequalities. The proposed approach has successfully provided a solution to the optimization problem that can stabilize the nonlinear plants. Furthermore, sufficient conditions for the existence of the proposed controller guarantee the robust stability of the system in the presence of polytypic uncertainties. In addition, the simulation results have demonstrated the efficiency of the proposed control scheme.

Keywords: PV pumping system, DC-DC buck converter, robust model predictive controller, nonlinear system, actuator saturation, linear matrix inequality

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Authors: David A. Padilla, Rodolfo Villamizar

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In recent years, closed loop control strategies have been developed in order to establish a healthy glucose profile in type 1 diabetic mellitus (T1DM) patients. However, the controller itself is unable to define a suitable reference trajectory for glucose. In this paper, a control strategy Is proposed where the shape of the reference trajectory is generated bases in the amount of carbohydrates present during the digestive process, due to the effect of carbohydrate intake. Since there no exists a sensor to measure the amount of carbohydrates consumed, an estimator is proposed. Thus this paper presents the entire process of designing a carbohydrate estimator, which allows estimate disturbance for a predictive controller (MPC) in a T1MD patient, the estimation will be used to establish a profile of reference and improve the response of the controller by providing the estimated information of ingested carbohydrates. The dynamics of the diabetic model used are due to the equations described by the UVA/Padova model of the T1DMS simulator, the system was developed and simulated in Simulink, taking into account the noise and limitations of the glucose control system actuators.

Keywords: estimation, glucose control, predictive controller, MPC, UVA/Padova

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Authors: Ould Bouamama, M. Bressel, D. Hissel, M. Hilairet

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Fuel cell (FC) is one of the best alternatives of fossil energy. Recently, the research community of fuel cell has shown a considerable interest for diagnosis in view to ensure safety, security, and availability when faults occur in the process. The problematic for model based FC diagnosis consists in that the model is complex because of coupling of several kind of energies and the numerical values of parameters are not always known or are uncertain. The present paper deals with use of one tool: the Linear Fractional Transformation bond graph tool not only for uncertain modelling but also for monitorability (ability to detect and isolate faults) analysis and formal generation of robust fault indicators with respect to parameter uncertainties.The developed theory applied to a nonlinear FC system has proved its efficiency.

Keywords: bond graph, fuel cell, fault detection and isolation (FDI), robust diagnosis, structural analysis

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Authors: Adriano Z. Zambom, Preethi Ravikumar

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One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: additive model, nonparametric regression, variable selection, Akaike Information Criteria

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Manlika Ratchagit

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This paper is concerned with robust mean square stability of uncertain stochastic switched discrete time-delay systems. The system to be considered is subject to interval time-varying delays, which allows the delay to be a fast time-varying function and the lower bound is not restricted to zero. Based on the discrete Lyapunov functional, a switching rule for the robust mean square stability for the uncertain stochastic discrete time-delay system is designed via linear matrix inequalities. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: robust mean square stability, discrete-time stochastic systems, hybrid systems, interval time-varying delays, Lyapunov functional, linear matrix inequalities

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Authors: Manlika Rajchakit

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This paper is concerned with robust mean square stability of uncertain stochastic switched discrete time-delay systems. The system to be considered is subject to interval time-varying delays, which allows the delay to be a fast time-varying function and the lower bound is not restricted to zero. Based on the discrete Lyapunov functional, a switching rule for the robust mean square stability for the uncertain stochastic discrete time-delay system is designed via linear matrix inequalities. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: robust mean square stability, discrete-time stochastic systems, hybrid systems, interval time-varying delays, lyapunov functional, linear matrix inequalities

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Elena Castillo-López, Raúl Pereda, Julio Manuel de Luis, Rubén Pérez, Felipe Piña

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Sediments are an important source of accumulation of toxic contaminants within the aquatic environment. Bioassays are a powerful tool for the study of sediments in relation to their toxicity, but they can be expensive. This article presents a methodology to estimate the main physical property of intertidal sediments in coastal zones: heavy metals concentration. This study, which was developed in the Bay of Santander (Spain), applies classical and robust statistic to CASI-2 hyperspectral images to estimate heavy metals presence and ecotoxicity (TOC). Simultaneous fieldwork (radiometric and chemical sampling) allowed an appropriate atmospheric correction to CASI-2 images.

Keywords: remote sensing, intertidal sediment, airborne sensors, heavy metals, eTOCoxicity, robust statistic, estimation

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Authors: Sandeep Reddy, Sonia Allan, Simon Coghlan, Paul Cooper

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Artificial Intelligence (AI) and its application in medicine has generated ample interest amongst policymakers and clinicians. Successes with AI in medical imaging interpretation and clinical decision support are paving the way for its incorporation into routine healthcare delivery. While there has been a focus on the development of ethical principles to guide its application in healthcare, challenges of this application go beyond what ethics principles can address thus requiring robust governance frameworks. Also, while ethical challenges of medical artificial intelligence are being discussed, the ethics of deploying AI in lower-income countries receive less attention than in other developed economies. This creates an imperative not only for sound ethical guidelines but also for robust governance frameworks to regulate AI in medicine around the world. In this article, we discuss what components need to be considered in developing these governance frameworks and who should lead this worldwide effort.

Keywords: artificial intelligence, global health, governance, ethics

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Authors: Tran Gia Khanh, Dao Phuong Nam, Do Trong Tan, Nguyen Van Huong, Mai Xuan Sinh

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This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.

Keywords: input state stability (ISS), tube-based robust MPC, continuous-time nonlinear systems, wheeled inverted pendulum

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Authors: R. Rama Kishore, Sunesh

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Nowadays, robust and secure watermarking algorithm and its optimization have been need of the hour. A watermarking algorithm is presented to achieve the copy right protection of the owner based on visual cryptography, histogram shape property and entropy. In this, both host image and watermark are preprocessed. Host image is preprocessed by using Butterworth filter, and watermark is with visual cryptography. Applying visual cryptography on water mark generates two shares. One share is used for embedding the watermark, and the other one is used for solving any dispute with the aid of trusted authority. Usage of histogram shape makes the process more robust against geometric and signal processing attacks. The combination of visual cryptography, Butterworth filter, histogram, and entropy can make the algorithm more robust, imperceptible, and copy right protection of the owner.

Keywords: digital watermarking, visual cryptography, histogram, butter worth filter

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Authors: Sarah Odofin, Zhiwei Gao

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Operations and maintenance of wind turbine have received much attention by researcher due to rapid expansion of wind farms. This paper explores a novel fault diagnosis that is designed and optimized to be very sensitive to faults and robust to disturbances. The faults considered are the sensor faults of which the augmented observer is considered to enlarge faults and to be robust to disturbance. A qualitative model based analysis is proposed for early fault diagnosis to minimize downtime mostly caused by components breakdown and exploit productivity. Simulation results are computed validating the models provided which demonstrates system performance using practical application of fault type examples. The results demonstrate the effectiveness of the developed techniques investigated in a Matlab/Simulink environment.

Keywords: wind turbine, condition monitoring, genetic algorithm, fault diagnosis, augmented observer, disturbance robustness, fault estimation, sensor monitoring

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Authors: Hao Wang, Shuguo Pan

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The Global Navigation Satellite System (GNSS) signal received by the dynamic vehicle in the harsh environment will be frequently interfered with and blocked, which generates gross error affecting the positioning accuracy of the GNSS/Inertial Navigation System (INS) integrated navigation. Therefore, this paper put forward an improved robust Cubature Kalman filter (CKF) algorithm for single-frequency GNSS/INS tightly coupled system ambiguity resolution. Firstly, the dynamic model and measurement model of a single-frequency GNSS/INS tightly coupled system was established, and the method for GNSS integer ambiguity resolution with INS aided is studied. Then, we analyzed the influence of pseudo-range observation with gross error on GNSS/INS integrated positioning accuracy. To reduce the influence of outliers, this paper improved the CKF algorithm and realized an intelligent selection of robust strategies by judging the ill-conditioned matrix. Finally, a field navigation test was performed to demonstrate the effectiveness of the proposed algorithm based on the double-differenced solution mode. The experiment has proved the improved robust algorithm can greatly weaken the influence of separate, continuous, and hybrid observation anomalies for enhancing the reliability and accuracy of GNSS/INS tightly coupled navigation solutions.

Keywords: GNSS/INS integrated navigation, ambiguity resolution, Cubature Kalman filter, Robust algorithm

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Authors: Belkacem Soundes, Guezouli Larbi

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This paper presents a robust and accurate method for text detection and localization over lecture videos. Frame regions are classified into text or background based on visual feature analysis. However, lecture video shows significant degradation mainly related to acquisition conditions, camera motion and environmental changes resulting in low quality videos. Hence, affecting feature extraction and description efficiency. Moreover, traditional text detection methods cannot be directly applied to lecture videos. Therefore, robust feature extraction methods dedicated to this specific video genre are required for robust and accurate text detection and extraction. Method consists of a three-step process: Slide region detection and segmentation; Feature extraction and non-text filtering. For robust and effective features extraction moment functions are used. Two distinct types of moments are used: orthogonal and non-orthogonal. For orthogonal Zernike Moments, both Pseudo Zernike moments are used, whereas for non-orthogonal ones Hu moments are used. Expressivity and description efficiency are given and discussed. Proposed approach shows that in general, orthogonal moments show high accuracy in comparison to the non-orthogonal one. Pseudo Zernike moments are more effective than Zernike with better computation time.

Keywords: text detection, text localization, lecture videos, pseudo zernike moments

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Passive sonar is a method for detecting acoustic signals in the ocean. It detects the acoustic signals emanating from external sources. With passive sonar, we can determine the bearing of the target only, no information about the range of the target. Target Motion Analysis (TMA) is a process to estimate the position and speed of a target using passive sonar information. Since bearing is the only available information, the TMA technique called Bearing-only TMA. Many TMA techniques have been developed. However, until now, there is not a very effective method that could be used to always track an unknown target and extract its moving trace. In this work, a design of effective Bearing-only TMA Algorithm is done. The measured bearing angles are very noisy. Moreover, for multi-beam sonar, the measurements is quantized due to the sonar beam width. To deal with this, modified gain extended Kalman filter algorithm is used. The algorithm is fine-tuned, and many modules are added to improve the performance. A special validation gate module is used to insure stability of the algorithm. Many indicators of the performance and confidence level measurement are designed and tested. A new method to detect if the target is maneuvering is proposed. Moreover, a reactive optimal observer maneuver based on bearing measurements is proposed, which insure converging to the right solution all of the times. To test the performance of the proposed TMA algorithm a simulation is done with a MATLAB program. The simulator program tries to model a discrete scenario for an observer and a target. The simulator takes into consideration all the practical aspects of the problem such as a smooth transition in the speed, a circular turn of the ship, noisy measurements, and a quantized bearing measurement come for multi-beam sonar. The tests are done for a lot of given test scenarios. For all the tests, full tracking is achieved within 10 minutes with very little error. The range estimation error was less than 5%, speed error less than 5% and heading error less than 2 degree. For the online performance estimator, it is mostly aligned with the real performance. The range estimation confidence level gives a value equal to 90% when the range error less than 10%. The experiments show that the proposed TMA algorithm is very robust and has low estimation error. However, the converging time of the algorithm is needed to be improved.

Keywords: target motion analysis, Kalman filter, passive sonar, bearing-only tracking

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Authors: O. Abiodun Adeyinka, B. Adeyemo Adesesan

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The forensic use of handwriting depends on the analysis, comparison, and evaluation decisions made by forensic document examiners. When using biometric technology in forensic applications, it is necessary to compute Likelihood Ratio (LR) for quantifying strength of evidence under two competing hypotheses, namely the prosecution and the defense hypotheses wherein a set of assumptions and methods for a given data set will be made. It is therefore important to know how repeatable and reproducible our estimated LR is. This paper evaluated the accuracy and reproducibility of examiners' decisions. Confidence interval for the estimated LR were presented so as not get an incorrect estimate that will be used to deliver wrong judgment in the court of Law. The estimate of LR is fundamentally a Bayesian concept and we used two LR estimators, namely Logistic Regression (LoR) and Kernel Density Estimator (KDE) for this paper. The repeatability evaluation was carried out by retesting the initial experiment after an interval of six months to observe whether examiners would repeat their decisions for the estimated LR. The experimental results, which are based on handwriting dataset, show that LR has different confidence intervals which therefore implies that LR cannot be estimated with the same certainty everywhere. Though the LoR performed better than the KDE when tested using the same dataset, the two LR estimators investigated showed a consistent region in which LR value can be estimated confidently. These two findings advance our understanding of LR when used in computing the strength of evidence in handwriting using forensics.

Keywords: confidence interval, handwriting, kernel density estimator, KDE, logistic regression LoR, repeatability, reproducibility

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Authors: Rachida Ouysse

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This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T.

Keywords: high dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, regularization, pseudo-out-of-sample forecasting

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Authors: Raghunath Arnab

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The two-phase sampling which is also known as double sampling was introduced in 1938. In two-phase sampling, samples are selected in phases. In the first phase, a relatively large sample of size is selected by some suitable sampling design and only information on the auxiliary variable is collected. During the second phase, a sample of size is selected either from, the sample selected in the first phase or from the entire population by using a suitable sampling design and information regarding the study and auxiliary variable is collected. Evidently, two phase sampling is useful if the auxiliary information is relatively easy and cheaper to collect than the study variable as well as if the strength of the relationship between the variables and is high. If the sample is selected in more than two phases, the resulting sampling design is called a multi-phase sampling. In this article we will consider how one can use data collected at the first phase sampling at the stages of estimation of the parameter, stratification, selection of sample and their combinations in the second phase in a unified setup applicable to any sampling design and wider classes of estimators. The problem of the estimation of variance will also be considered. The variance of estimator is essential for estimating precision of the survey estimates, calculation of confidence intervals, determination of the optimal sample sizes and for testing of hypotheses amongst others. Although, the variance is a non-negative quantity but its estimators may not be non-negative. If the estimator of variance is negative, then it cannot be used for estimation of confidence intervals, testing of hypothesis or measure of sampling error. The non-negativity properties of the variance estimators will also be studied in details.

Keywords: auxiliary information, two-phase sampling, varying probability sampling, unbiased estimators

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Authors: Dong Sang Yoo

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We consider nonlinear uncertain systems such that a priori information of the uncertainties is not available. For such systems, we assume that the upper bound of the uncertainties is represented as a Fredholm integral equation of the first kind and we propose an adaptation law that is capable of estimating the upper bound and design a continuous robust control which renders nonlinear uncertain systems ultimately bounded.

Keywords: adaptive control, estimation, Fredholm integral, uncertain system

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

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Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

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This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

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Authors: Fatma Romeh, Shiferaw Gurmu

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This paper examines the impact of female education on fertility, using the change in length of primary schooling in Egypt in 1988-89 as the source of exogenous variation in schooling. In particular, beginning in 1988, children had to attend primary school for only five years rather than six years. This change was applicable to all individuals born on or after October 1977. Using a nonparametric regression discontinuity approach, we compare education and fertility of women born just before and after October 1977. The results show that female education significantly reduces the number of children born per woman and delays the time until first birth. Applying a robust regression discontinuity approach, however, the impact of education on the number of children is no longer significant. The impact on the timing of first birth remained significant under the robust approach. Each year of female education postponed childbearing by three months, on average.

Keywords: Egypt, female education, fertility, robust regression discontinuity

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Authors: Graziano Chesi

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The topological entropy plays a key role in linear dynamical systems, allowing one to establish the existence of stabilizing feedback controllers for linear systems in the presence of communications constraints. This paper addresses the determination of a robust value of the topological entropy in nonlinear dynamical systems, specifically the largest value of the topological entropy over all linearized models in a region of interest of the state space. It is shown that a sufficient condition for establishing upper bounds of the sought robust value of the topological entropy can be given in terms of a semidefinite program (SDP), which belongs to the class of convex optimization problems.

Keywords: non-linear system, communication constraint, topological entropy

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Authors: Abdolsalam Ghaderi

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In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: location-routing problem, robust optimization, stochastic programming, variable neighborhood search

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Authors: Roya Ahmadi Ahangar, Hamid Madadyari

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The seeker optimization algorithm (SOA) is increasingly gaining popularity among the researchers society due to its effectiveness in solving some real-world optimization problems. This paper provides the load-frequency control method based on the SOA for removing oscillations in the power system. A three-zone power system includes a thermal zone, a hydraulic zone and a wind zone equipped with robust proportional-integral-differential (PID) controllers. The result of simulation indicates that load-frequency changes in the wind zone for the multi-zone system are damped in a short period of time. Meanwhile, in the oscillation period, the oscillations amplitude is not significant. The result of simulation emphasizes that the PID controller designed using the seeker optimization algorithm has a robust function and a better performance for oscillations damping compared to the traditional PID controller. The proposed controller’s performance has been compared to the performance of PID controller regulated with Particle Swarm Optimization (PSO) and. Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithms in order to show the superior capability of the proposed SOA in regulating the PID controller. The simulation results emphasize the better performance of the optimized PID controller based on SOA compared to the PID controller optimized with PSO, GA and ABC algorithms.

Keywords: load-frequency control, multi zone, robust PID controller, wind generation

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Authors: Henni Mansour Abdelwaheb

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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

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Authors: Hanan Rizk

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A heat exchanger is a device used to mix liquids having different temperatures. In this case, the temperature control becomes a critical objective. This research work presents the temperature control of the double-pipe heat exchanger (multi-input multi-output (MIMO) system), which is modeled as first-order coupled hyperbolic partial differential equations (PDEs), using conventional and advanced control techniques and develops appropriate robust control strategy to meet stability requirements and performance objectives. We designed a PID controller and H-infinity controller for a heat exchanger (HE) system. Frequency characteristics of sensitivity functions and open-loop and closed-loop time responses are simulated using MATLAB software, and the stability of the system is analyzed using Kalman's test. The simulation results have demonstrated that the H-infinity controller is more efficient than PID in terms of robustness and performance.

Keywords: heat exchanger, multi-input multi-output system, MATLAB simulation, partial differential equations, PID controller, robust control

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