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The COVID-19 outbreak continues to spread all over the world. In many countries, measures are being taken and vaccination studies continue due to the spread of different virus mutations. In order to facilitate the fight against the virus, the data collected from the beginning of the epidemic until today have been used to construct a mathematical model of the epidemic spread. In this study, it was aimed to introduce an extended version of a recent epidemic model that has been proposed, in which parameters such as vaccination rate, quarantine conditions and time-varying virus reproduction number are included. For in case of the reproduction number and some of the states are time-varying and difficult to measure, they are estimated by a recently introduced efficient nonlinear observer. The real-data validation of the estimations shows the accuracy of the mathematical model and indicates that the spread of the virus can be controlled by observer-based control. Therefore, a nonlinear model-predictive control is used to stabilize the dynamics of the outbreak while unmeasured dynamics and parameters are estimated by the designed observer. Numerical results were obtained for the daily vaccinated population of Turkey is 2.1 million individuals (w = 0.025) and 420 thousand individuals (w = 0.005), respectively. For w = 0.025, it is seen that the COVID-19 pandemic will completely terminate in nearly 600 days for the BNT162b2 vaccine and nearly 700 days for the CoronaVac vaccine. For w = 0.005, it can be said that herd immunity will be reached on average 1000 days after (March 2020) the first active case is reported in Turkey. In the light of the numerical results, depending on the different quarantine conditions and vaccination rates, the future course of the epidemic in Turkey can be determined. Finally, observer-based stabilization results can be used in other countries where similar measures and vaccination strategies are applied. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

This paper proposes a novel population-based metaheuristic optimization algorithm, called Adolescent Identity Search Algorithm (AISA), which is inspired by the process of identity development/search of adolescents. AISA simulates the identity formation behavior of adolescents in the peer group. This behavior is modeled mathematically to solve optimization problems. The proposed algorithm is evaluated on thirty-nine well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC 2019 benchmark functions to test exploration, exploitation, local optima avoidance, and convergence properties. The results are verified by an extensive comparative study with thirteen state-of-art metaheuristic algorithms. Furthermore, AISA has been used to solve IIR system identification and inverse kinematics problem of a seven Degrees Of Freedom (7-DOF) robot manipulator considered as the real-life engineering applications. The overall optimization results demonstrate that AISA possesses a strong and robust capability to produce superior performance over other competitor metaheuristic algorithms in solving various complex numerical optimization problems. (C) 2020 Elsevier B.V. All rights reserved. C1 [Bogar, Esref] Pamukkale Univ, Denizli Vocat Sch Tech Sci, Dept Elect & Automat, TR-20070 Denizli, Turkey. [Beyhan, Selami] Izmir Democracy Univ, Fac Engn Elect & Elect Engn, Gursel Aksel Blvd 14, TR-35140 Izmir, Turkey.

A non-contact mode atomic force microscope with chaotic dynamics may exposed to unknown faults, disturbances or uncertain parameters that are not always be compensated using classical control methods. Therefore, a fault tolerant controller must be designed for accurate tracking of the tip-position of the end-effector. In this paper, first, an unscented Kalman filter is designed for joint estimation of the states and parameters for an atomic force microscopy under process noise. The velocity of the end-effector, sample height and unknown fault are simultaneously estimated by measuring the tip position of randomly excited microscopy. Second, unscented Kalman filtering based model predictive controller is proposed for the accurate tracking of the tip-position. To prevent the disadvantage of the model-based controller design, an uncertainty or unknown fault function of the system is estimated by unscented Kalman filter such that the unmodeled dynamics of the system are compensated while the control signal is produced. Note that the controller voltage being applied to the microscopy is produced based on the estimated states and parameters of the atomic force microscopy. The numerical applications present that satisfactory tracking performance for tip position is obtained by the proposed fault tolerant controller such that extended Kalman filtering-based tracking results are also compared and discussed. C1 [Cetin, Meric] Pamukkale Univ, Dept Comp Engn, Kinikli Campus, TR-20020 Denizli, Turkey. [Beyhan, Selami] Izmir Democracy Univ, Dept Elect & Elect Engn, Izmir, Turkey.

This paper proposes an observer-controller configuration for the well-known Hovarka Glucose-Insulin dynamics. The aim is to control the blood glucose level without measuring the states in the Hovarka model. In order that nonlinear model predictive control is designed to control the blood glucose level by considering the random inertial glucose inputs under external insulin inputs. For the estimation, a Cubature Kalman filter is designed to estimate the states. Numerical computations are conducted under random glucose inputs from the meals where the applied observer-controller configuration provides blood glucose control without measuring the states. As a result, by using a single subcutaneous glucose measurement, the Hovarka glucose-insuling dynamics can be controlled for a possible portable device in future. C1 [Cetin, Meric] Pamukkale Univ, Dept Comp Engn, Denizli, Turkey. [Beyhan, Selami] Izmir Democracy Univ, Dept Elect & Elect Engn, Izmir, Turkey.

Recently, an approximate mathematical model of the cortex dynamics was introduced for treatment of the epilepsy, Parkinson's, etc., where it is found applicable in real-time applications. However, designed control methods for treatments are based on the assumption of the exact mathematical model of the cortex and full state measurements are also available. In fact, the mathematical model is not exact, full state measurements may not always possible, and the measurements are always noisy. Therefore, in this brief, an adaptive unscented Kalman filter (UKF) based optimal controller is proposed to control of uncertain cortex dynamics with a single membrane potential measurement. The unmeasurable states and uncertainty function of the dynamics are estimated using adaptive UKF with noisy output measurement that are used in the production of control signal. For the security of treatments, a constrained input signal is also proposed to apply. In numerical applications, the epileptic state of the cortex dynamics is handled and accurately stabilized to a normal state with a constrained input signal. The estimation and control results are presented for future applications of uncertain and unmeasurable cortex dynamics. C1 [Cetin, Meric] Pamukkale Univ, Dept Comp Engn, TR-20020 Denizli, Turkey. [Beyhan, Selami] Pamukkale Univ, Dept Elect & Elect Engn, TR-20020 Denizli, Turkey.

In this paper, an adaptive model predictive controller (MPC) with a function approximator is proposed to the control of the uncertain nonlinear systems. The proposed adaptive Sigmoid and Chebyshev neural networks-based MPCs (ANN-MPC and ACN-MPC) compensate the system uncertainty and control the system accurately. Using Lyapunov theory, the closed-loop signals of the linearized dynamics and the uncertainty modeling-based model predictive controller have been proved to be bounded. Accuracy of the ANN-MPC and ACN-MPC has been compared with the Runge-Kutta discretization-based nonlinear MPC on an experimental MIMO three-tank liquid-level system where a functional uncertainty is created on its dynamics. Real-time experimental results demonstrate the effectiveness of the proposed controllers. In addition, due to the faster function approximation capability of Chebyshev polynomial networks, ACN-MPC provided better control performance results. C1 [Cetin, Meric] Pamukkale Univ, Dept Comp Engn, TR-20070 Denizli, Turkey. [Bahtiyar, Bedri] Pamukkale Univ, Denizli Vocat Sch, Dept Elect & Energy, TR-20070 Denizli, Turkey. [Beyhan, Selami] Pamukkale Univ, Dept Elect & Elect Engn, TR-20070 Denizli, Turkey.

In this article, nonlinear regressor models namely polynomial regressor, artificial neural-network (ANN) and least-squares support vector machine (LS-SVM) were designed and applied to model the drying kinetics and change of the antioxidant properties of mahaleb puree during infrared drying process. Temperature and time were used as the model inputs and moisture ratio, antioxidant capacity and total anthocyanin content were the outputs of nonlinear regressors. The regressor models were compared in terms of the root mean-squared-error (RMSE) and minimum-descriptive-length (MDL) criteria. According to statistical selection criteria, LS-SVM was the best model to describe the infrared drying kinetics of mahaleb puree with the lowest RMSE and MDL values. ANN with Levenberg-Marquardth optimization gave the best results to predict antioxidant capacity and total anthocyanin content during infrared drying process of mahaleb puree. Practical applications(a) Design and analysis of intelligent models for modeling of drying processes. (b) Design of automatic drying equipments by embedding intelligent models. (c) Prediction of moisture ratio, antioxidant capacity and total anthocyanin of drying mahaleb puree at any time and temperature.

This paper proposes a novel state space least squares support vector machine (SS LS-SVM) for polynomial nonlinear state space (PNLSS) model based recursive system identification. SS LS-SVM, which also possesses an adaptive kernel function, provides an optimum formulation of the monomials (zeta) of the states and input in the PNLSS model. Hence, the PNLSS model encompasses the proposed SS LS-SVM. Recursive nonlinear state space identification is developed in the output error prediction context. The input-output observations are processed sequentially, hence leading to recursive update of the parameters using conventional Gauss-Newton optimization. System states do not need to be measured. However, to to yield a conformal representation of the actual system, number of states need to be known via some physical insight. This characterizes the identification procedure as a grey box one. The PNLSS model is employed in the generalized predictive control (GPC) of a nonlinear continuously stirred tank reactor (CSTR) system. The case which includes additive white noise on the output measurements and a time-varying parameter in the nonlinear system is considered. Numerical applications give the results of a high closed loop identification performance addition to the smooth control input and closely tracking the reference in the GPC scheme.

When it's essential to conduct intact rock strength tests on saturated samples to be representative of rock mass, clay bearing rocks such as marl which might present water sensitive behavior should be carefully inspected. In the following water table study, rock samples of Tunçbilek region that tend to fracture were alternatively conditioned in a climate cabin. According to the underground mine weather information taken from Tunçbilek region, triaxial compression tests were performed on oven dried marl samples and conditioned in 60 % Relative Humidity (RH), 80 % RH and 90 % RH at 17.6 °C. Calculated values of Mohr-Coulomb and Hoek-Brown failure criteria parameters depending on triaxial test results showed inverse change which does not let evaluate rock material strength. To achieve this goal, numerical modeling analyses were used. This allowed evaluating of intact rock triaxial strength by the effect of intact rock properties on the behavior of rock mass. Finally; it was found that moisture decreased the strength of marl rock material, and conditioning by moisture diffusion is an appropriate procedure for evaluating water sensitive rocks such as marl. © 2016, Academy of Sciences of the Czech Republic. All rights reserved.

In this study, the structure of fuzzy functions is improved by function expansion. Unlike fuzzy conventional if-then rules, classical fuzzy function structure includes fuzzy bases and linear inputs. Membership functions of fuzzy bases are set using fuzzy C-means (FCM) algorithm, and the linear parameters are computed using the least-square estimation (LSE). This study has two main contributions. First, conventional "fuzzy functions" structure is powered by the expansion of orthogonal "trigonometric functions" where the approximation capabilities of the fuzzy functions are increased. Second, the widths of the normalized membership functions determined for the fuzzy function model are optimized using the Nelder-Mead simplex algorithm that provides further enhancement on the identification performance. The advantages of the proposed model are shown via offline identification of a benchmark nonlinear system and online identification of two real-time nonlinear systems.

In this paper, a novel adaptive Takagi-Sugeno (TS) fuzzy observer-based controller is proposed. The closed-loop stability and the boundedness of all the signals are proven by Lyapunov stability analysis. The proposed controller is applied to a flexible-transmission experimental setup. The performance for constant payload in the presence of noisy measurements is compared to a controller based on a classical extended Luenberger observer. Simulation and real-time results show that the proposed observer-based feedback controller provides accurate position tracking under constant and varying payloads. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Soft sensing technology has still important industrial applications especially for chemical reactors, robotic applications, etc. Therefore, this paper introduces and applies novel Runge-Kutta (RK) discretization-based nonlinear observers for real-time sensing applications of unmeasurable quantities. The contribution of the paper is twofold. First, for reliability and accuracy, the stability of the RK based observers are proven. Second, they have been compared with well-known extended-Luenberger observer, extended-Kalman filter and sliding-mode observer on an experimental system. Experiments have shown that the RK model-based observers have a considerable place among conventional ones with respect to design issues and estimation performance for future soft sensing applications.

In this paper, a novel adaptive extended fuzzy function state observer based controller is proposed to control a class of unknown or uncertain nonlinear systems. The controller uses Nussbaum-gain technique from literature to prevent controller singularity with unknown control direction and the controller degree of freedom is increased. A state observer which employs the adaptive extended fuzzy function system to approximate a nonlinear system dynamics and estimates the unmeasurable state. The stability of closed-loop control system are shown using Lyapunov stability criterion and Nussbaum function property. The proposed and conventional fuzzy system based controllers are designed to control an inverted pendulum in simulation and a flexible-joint manipulator in real-time experiment. The integral of absoulte error (IAE) of tracking, integral of squared error (ISE) of tracking and integral of required absolute control signal (IA U) performances are compared in applications. The aim of the paper is not only to improve the tracking performances, but also to implement the adaptive extended fuzzy function based controller to a real-time system and conduct the tracking with unknown control direction.

In this paper, the tip displacement of a conducting polymer actuator (CPA), which has an unknown and time-varying dynamics, is controlled using three different model-free adaptive controllers. First, conventional indirect adaptive fuzzy and Chebyshev functional-link network controllers are introduced. Second, a novel hybrid network which combines the capability of conventional fuzzy system and Chebyshev functional-link network is proposed for online identification and control of the CPA. The stability of the proposed controllers and boundedness of the closed-loop signals are proven using Lyapunov stability approach. The designed adaptive controllers are implemented experimentally in real-time. The performances of the controllers in tracking a square and a sine reference are compared in terms of root-mean-squared error (RMSE), required input signal power (ICVP) and settling time (t(s)) performances.

The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems. The designed controllers must undertake the effect of these undesired uncertainties. In this paper, adaptive uncertainty modeling based model predictive controller is proposed for the control of uncertain linear systems. The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation. The stability of the proposed adaptive uncertainty modeling based model predictive control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and proposed UMPC are applied to the control of a real-time DC/DC buck power converter. The conventional MPC cannot accurately control the DC/DC converter due to the unknown parameters and unmodeled dynamics. However, the proposed UMPC controller can accurately control the system with modeling the uncertainties in controller dynamics. The proposed controller is promising to control uncertain systems in future applications.

WOS: 000214976300012, Urban identity is formed by the entire values and characteristics of a city. Urban transformation is the strategies and activities for maintaining environmental quality and life balance. The concepts of urban identity and urban transformation, which have been quite popular in Turkey recently, are the grounds of this study. The socio-cultural and economic conditions, which have changed as a result of globalization experienced all over the world, make it compulsory to re-shape cities, so urban identity concept has gradually become more important. The aim of this study is to reveal misidentification in Turkey caused by urban transformation implementations, particularly via the example of Isparta. As Isparta is a typical Anatolian city developed after the proclamation of the Republic, it was considered worthy of analysis. At, it was observed that Isparta had developed in terms of planning until 1960s. It was concluded that turning points had occurred in urban transformation between the years 1960-1980 and after 1980, these turning points spoiled urban identity, and therefore, the city has developed and globalized without any identity since then.

This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed.

ISRM Regional Symposium, EUROCK 2015 -- 7 October 2015 through 10 October 2015 -- -- 115068, The anisotropic strength properties of rocks, which are of great importance for the stability of engineering structures, can be determined by doing various laboratory works. In this study, the anisotropic strength properties of sedimentary rocks such as marl and mudstone were examined by making use of the indirect tensile strength test. Two different methods were applied in the test phase. Firstly, the disc shaped specimens prepared by using NX diameter cylindrical samples taken from blocks parallel to the bedding plane were placed in the testing apparatus at different angles from horizontal to perform tests. In the second phase, the NX diameter cylindrical samples were prepared at perpendicular (0°), parallel (90°) and 45° angles to the bedding plane and tests were performed at these orientation angles. Accordingly, in the tests performed by placing samples in the testing apparatus at different angles, the highest value was gathered at ? =0°, and the lowest at ?=90° anisotropy angle. In the tests of second phase, the highest value was gathered at ?=90°, the lowest at ?=45° orientation angle. © ÖGG.

This paper proposes a novel nonlinear gradient-based observer for synchronization and observer-based control of chaotic systems. The model is based on a Runge-Kutta model of the chaotic system where the evolution of the states or parameters is derived based on the error-square minimization. The stability and convergence conditions of observer and control methods are analyzed using a Lyapunov stability approach. In numerical simulations, the proposed observer and well-known sliding-mode observer are compared for the synchronization of a Lü chaotic system and observer-based stabilization of a Chen chaotic system. The noisy case for synchronization and parameter uncertainty case for stabilization are also considered for both observer-based methods.

This paper proposes a novel nonlinear gradient-based observer for synchronization and observer-based control of chaotic systems. The model is based on a Runge-Kutta model of the chaotic system where the evolution of the states or parameters is derived based on the error-square minimization. The stability and convergence conditions of observer and control methods are analyzed using a Lyapunov stability approach. In numerical simulations, the proposed observer and well-known sliding-mode observer are compared for the synchronization of a Lid chaotic system and observer-based stabilization of a Chen chaotic system. The noisy case for synchronization and parameter uncertainty case for stabilization are also considered for both observer-based methods. (c) 2013 ISA. Published by Elsevier Ltd. All rights reserved.

In this paper, two nonlinear state estimation methods, Takagi-Sugeno fuzzy observer and extended-Kalman filter are compared in terms of their ability to reliably estimate the velocity and an unknown, variable payload of a nonlinear servo system. Using the system dynamics and a position measurement, the velocity and unknown payload are estimated. In a simulation study, the servo system is excited with a randomly generated step input. In real-time experiments, the estimation is performed under feedback-linearizing control. The performance of the TS fuzzy payload estimator is discussed with respect to the choice of the desired convergence rate. The application results show that the Takagi-Sugeno fuzzy observer provides better performance than the extended-Kalman filter with robust and less parameter dependent structure.

In this paper, the well-known heuristic Artificial Bee Colony algorithm (ABC) and deterministic Levenberg-Marquardt (LM) optimization method are unified to get better performance of nonlinear optimization. In the proposed cascaded ABC-LM algorithm, the power of the ABC and LM algorithms are synergized to reduce computational-time and get rid of the problem "stucking at local minima" of some nonlinear functions. Then, the proved power of the cascaded optimization is also tested on the training of Artificial Neural Network (ANN) for classification of XOR data and nonlinear system identification of real-time inverted pendulum set-up. The comparisons in function optimization and system identification using ABC, LM and ABC-LM showed that ABC-LM optimized nonlinear functions and ABC-LM trained ANN has resulted smaller cost functions and mean-squared-error (MSE) values, respectively.

Boren;DEMiR Export;et al;metso Expect results;Outotec;TMMOB Maden Muhendisleri Odasi, 23rd International Mining Congress and Exhibition of Turkey, IMCET 2013 -- 16 April 2013 through 19 April 2013 -- Antalya -- 105453, Based on the probability that an effective progress speed could be achieved with an efficient work plan as well as choosing the appropriate TBM (Tunnel Boring Machine) and optimum working conditions for the formation of the excavation site, the aim of this study was to determine the effects of breakdown and delay times on the progress efficiency of the TBM used in Konya Plain Irrigation Project. TBMs data between December 2008 and October 2010 were evaluated by means of Malmquist Productivity Index and decrease overall productivity was determined. The most effective ones among the factors influencing the monthly efficiency of the TBM were delay times caused by hydraulic breakdowns and lack of water + ventilation, TBM mechanic breakdowns, TBM electrical breakdowns, external + other delays and back-up mechanic breakdowns.

Boren;DEMiR Export;et al;metso Expect results;Outotec;TMMOB Maden Muhendisleri Odasi, 23rd International Mining Congress and Exhibition of Turkey, IMCET 2013 -- 16 April 2013 through 19 April 2013 -- Antalya -- 105453, Mining is the sector which has highest risk rate in terms of occupational accidents due to its nature. Therefore, technical staffs who are working in the sector have to be aware of their responsibilities and roles. In the present study, occupational accidents that occurred in the underground and open pit mines of Turkish Coal Enterprise - the biggest mining enterprises in Turkey - were examined for the period of five years (2005 -2010) and these accidents were brought to focus in relation to their economic dimensions. In addition, technical evaluation was performed for determining the reasons of changes in accidents for the mentioned years.

Objective: The aim of this study was to assess levels of the burnout syndrome among the Eastern Mediterranean Region anaesthesiologists Methods: After obtaining the approval of the Çukurova University Faculty of Medicine ethics committee, anaesthesia and reanimation physicians in the East Mediterranean Region and residents who were working more than one year in the at Cukurova University anaesthesia and reanimation units were included in this study. Emotional exhaustion, depersonalization and personal success were evaluated with Maslach Burnout Inventory (MBI). The personal characteristics, habits and working conditions were evaluated with the socio-demographic data collection form. SPSS 18.0 package program was used for statistical analysis of the data. Results: Sixty (68.2%) physicians and twenty-seven (30.7%) residents were included this study. Burnout scores were not affected by gender, marital status, alcohol abuse, chronic illness. MBI-EE (emotional exhaustion) score (20.09±6.38), MBI-D (depersonalization) score (7.64±3.64) and MBI-NW score (11.50±2.68) were significantly higher in 25-30 age group. In addition, burnout was related to smoking, sleep disturbance, working at a public hospital, not having a hobby, the number of night shifts between 6-10, being a resident. Conclusion: Burnout levels may decrease with reduction of the number of shifts, having a hobby, no smoking, and a regular sleep pattern. Regulation of working conditions, concentrating on education, psychological support and organizational arrangements would effectively contribute to the prevention of burnout.

Based on the probability that an effective progress speed could be achieved with an efficient work plan as well as choosing the appropriate TBM (Tunnel Boring Machine) and optimum working conditions for the formation of the excavation site, the aim of this study was to determine the effects of breakdown and delay times on the progress efficiency of the TBM used in Konya Plain Irrigation Project. DEAP 2.1 (Data Envelopment Analysis Program) was employed in the efficiency measurements that adopted Data Envelopment Analysis (DEA). As a result of the analyses, inefficiency caused by delays was identified in approximately 73% of the excavation weeks but it was also determined that the average working productivity could be increased from 24,43% to 55,01% by means of rehabilitation studies.