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    A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction
    (Springer, 2025) Tolun, Omer Can; Zor, Kasim; Tutsoy, Onder
    The current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of T & uuml;rkiye. The performance and error metrics, including determination coefficient (R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} ), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction.
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    A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data
    (Oxford Univ Press, 2024) Tutsoy, Onder; Sumbul, Hilmi Erdem
    Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.
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    A Novel Exploration-Exploitation-Based Adaptive Law for Intelligent Model-Free Control Approaches
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Tutsoy, Onder; Barkana, Duygun Erol; Balikci, Kemal
    Model-free control approaches require advanced exploration-exploitation policies to achieve practical tasks such as learning to bipedal robot walk in unstructured environments. In this article, we first construct a comprehensive exploration-exploitation policy that carries quality knowledge about the long-term predictor and the control policy, and the control signal of the model-free algorithms. Therefore, the developed model-free algorithm continues exploration by adjusting its unknown parameters until the desired learning and control are accomplished. Second, we provide an utterly model-free adaptive law enriched with the exploration-exploitation policy and derived step-by-step using the exact analogy of the model-based solution. The obtained adaptive control law considers the control signal saturation and the control signal (input) delay. Performed Lyapunov stability analysis ensures the convergence of the adaptive law that can also be used for intelligent control approaches. Third, we implement the adaptive algorithm in real time on a challenging benchmark system: a fourth-order, coupled dynamics, input saturated, and time-delayed underactuated manipulator. The results show that the proposed adaptive algorithm explores larger state-action spaces and treats the vanishing gradient problem in both learning and control. Also, we notice from the results that the learning and control properties of the adaptive algorithm are optimized as required.
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    Adaptive estimator design for unstable output error systems: A test problem and traditional system identification based analysis
    (Sage Publications Ltd, 2015) Tutsoy, Onder; Colak, Sule
    A key open question in adaptive estimator design is how to assure that the parameters of the proposed algorithms are converging to their almost correct solutions; hence, the learning algorithm is unbiased. Moreover, determining the speed of parameter convergence is important as it provides insight about the performance of the learning algorithms. The main contributions of the article are fourfold: the first one is that the article, initially, introduces an adaptive estimator to learn the discounted Q-function and approximate optimal control policy without requiring linear, discrete time, unstable output error system dynamics, but using only the noisy system measurements. The simulation results show that the adaptive estimator minimizes the stochastic cost function and temporal difference error and also learns the approximate Q-function together with the control policy. The second one is consideration of a different approach by taking a simple test problem to investigate issues associated with the Q-function's representation and parametric convergence. In particular, the terminal convergence problem is analyzed with a known optimal control policy where the aim is to accurately learn only the Q-function. It is parameterized by terms which are functions of the unknown plant's parameters and the Q-function's discount factor, and their convergence properties are analyzed and compared with the adaptive estimator. The third one is to show that even though the adaptive estimator with a large Q-function discount factor yields larger control feedback gains, so that faster state converges upright, the learning problem is badly conditioned; hence, the parameter convergence is sluggish, as the Q-function discount factor approaches the inverse of the dominant pole of the unstable system. Finally, the fourth one is comparison of the state output learned by the adaptive estimator with the ones obtained from traditional system identification algorithms. Simulation result for a higher order unstable output error system shows that the adaptive estimator closely follows the real system output whereas the system identification algorithms do not.
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    Chaotic dynamics and convergence analysis of temporal difference algorithms with bang-bang control
    (Wiley, 2016) Tutsoy, Onder; Brown, Martin
    Reinforcement learning is a powerful tool used to obtain optimal control solutions for complex and difficult sequential decision making problems where only a minimal amount of a priori knowledge exists about the system dynamics. As such, it has also been used as a model of cognitive learning in humans and applied to systems, such as humanoid robots, to study embodied cognition. In this paper, a different approach is taken where a simple test problem is used to investigate issues associated with the value function's representation and parametric convergence. In particular, the terminal convergence problem is analyzed with a known optimal control policy where the aim is to accurately learn the value function. For certain initial conditions, the value function is explicitly calculated and it is shown to have a polynomial form. It is parameterized by terms that are functions of the unknown plant's parameters and the value function's discount factor, and their convergence properties are analyzed. It is shown that the temporal difference error introduces a null space associated with the finite horizon basis function during the experiment. The learning problem is only non-singular when the experiment termination is handled correctly and a number of (equivalent) solutions are described. Finally, it is demonstrated that, in general, the test problem's dynamics are chaotic for random initial states and this causes digital offset in the value function learning. The offset is calculated, and a dead zone is defined to switch off learning in the chaotic region. Copyright (C) 2015 John Wiley & Sons, Ltd.
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    Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification
    (Bmc, 2024) Tutsoy, Onder; Koc, Gizem Gul
    BackgroundBlood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.ResultsThis paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.ConclusionsThe results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
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    Design and Comparison Base Analysis of Adaptive Estimator for Completely Unknown Linear Systems in the Presence of OE Noise and Constant Input Time Delay
    (Wiley, 2016) Tutsoy, Onder
    In this paper, an adaptive estimator (AE) is introduced to learn the approximate Q-function and control policy by only using the noisy states and control signals of the unknown linear, discrete time systems having constant input time delay. The system measurements are uncertain owing to output error (OE)-type noise acting randomly on the system measurements. Therefore, this research differs from the designed AE in the literature since previous research ignores the role of the external random disturbances on AE-based learning. In order to compare the AE-based learning results with traditional system identification (SI) approaches, a modified version of the OE model structure for unstable systems is reviewed and parameters of a second-order unstable system with constant input time delay are identified. The simulation results demonstrate that the designed AE efficiently minimizes the stochastic cost function and the temporal difference error by learning the approximate solution for the Hamilton-Jacobi-Bellman (HJB) equation. It is noted that the error in the Q-function obtained with the AE is slightly larger than the Q-function attained with the identified OE parameters. However, as the noise standard deviation increases, the error in the AE-based learning results reduces whereas the error in the OE-based learning increases. This indicates that even though the added random noise deteriorates the performance of the OE predictor, it improves the learning efficiency of the AE since it acts like exploration noise.
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    Design of a completely model free adaptive control in the presence of parametric, non-parametric uncertainties and random control signal delay
    (Elsevier Science Inc, 2018) Tutsoy, Onder; Barkana, Duygun Erol; Tugal, Harun
    In this paper, an adaptive controller is developed for discrete time linear systems that takes into account parametric uncertainty, internal-external non-parametric random uncertainties, and time varying control signal delay. Additionally, the proposed adaptive control is designed in such a way that it is utterly model free. Even though these properties are studied separately in the literature, they are not taken into account all together in adaptive control literature. The Q-function is used to estimate long-term performance of the proposed adaptive controller. Control policy is generated based on the long-term predicted value, and this policy searches an optimal stabilizing control signal for uncertain and unstable systems. The derived control law does not require an initial stabilizing control assumption as in the ones in the recent literature. Learning error, control signal convergence, minimized Q-function, and instantaneous reward are analyzed to demonstrate the stability and effectiveness of the proposed adaptive controller in a simulation environment. Finally, key insights on parameters convergence of the learning and control signals are provided. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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    Doctor Robots: Design and Implementation of a Heart Rate Estimation Algorithm
    (Springer, 2022) Gongor, Fatma; Tutsoy, Onder
    Populations are ageing and the healthcare costs are increasing accordingly. Humanoid Robots (HRs) perform basic but crucial health checkups such as the heart rate can efficiently meet the healthcare demands. This paper develops a 9-stage heart rate estimation algorithm and implements it to an HR. The 9-stages cover the recognition of the face with the Viola-Jones algorithm, determination of the facial regions with the geometric-based facial distance measurement technique, extraction of the forehead and cheek regions, tracking of these facial regions with the Hierarchical Multi Resolution algorithm, decomposition of the facial regions in the Red-Green-Blue (RGB) color channels, averaging and normalization of the RGB color data, elimination of the artifacts with the Adaptive Independent Component Analysis (ICA) technique, calculating the power spectrum of the data with the Fast Fourier Transform (FFT) technique, and finally determining the peaks inside the threshold reflecting the human heart rate boundaries. One of the key contributions of this paper is building and incorporating the Hierarchical Multi Resolution technique in the heart rate estimation algorithm to eliminate the deteriorating effects of the human and camera motions. A further contribution of this paper is generating a rule-based approach to discard the effects of the sudden movements. These two contributions have noticeably improved the accuracy of the heart rate estimation algorithm in the dynamic environments. The algorithm has been assessed extensively with 5 different experimental scenarios consisting of 33 conditions.
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    Learning to balance an NAO robot using reinforcement learning with symbolic inverse kinematic
    (Sage Publications Ltd, 2017) Tutsoy, Onder; Barkana, Duygun Erol; Colak, Sule
    An autonomous humanoid robot (HR) with learning and control algorithms is able to balance itself during sitting down, standing up, walking and running operations, as humans do. In this study, reinforcement learning (RL) with a complete symbolic inverse kinematic (IK) solution is developed to balance the full lower body of a three-dimensional (3D) NAO HR which has 12 degrees of freedom. The IK solution converts the lower body trajectories, which are learned by RL, into reference positions for the joints of the NAO robot. This reduces the dimensionality of the learning and control problems since the IK integrated with the RL eliminates the need to use whole HR states. The IK solution in 3D space takes into account not only the legs but also the full lower body; hence, it is possible to incorporate the effect of the foot and hip lengths on the IK solution. The accuracy and capability of following real joint states are evaluated in the simulation environment. MapleSim is used to model the full lower body, and the developed RL is combined with this model by utilizing Modelica and Maple software properties. The results of the simulation show that the value function is maximized, temporal difference error is reduced to zero, the lower body is stabilized at the upright, and the convergence speed of the RL is improved with use of the symbolic IK solution.
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    Model free adaptive control of the under-actuated robot manipulator with the chaotic dynamics
    (Elsevier Science Inc, 2021) Tutsoy, Onder; Barkana, Duygun Erol
    Development of practical control approaches for the under-actuated chaotic systems such as the robot manipulators are challenging due to the unpredictable character of the chaotic dynamics, and the inevitable real-time application properties like delays, saturations, and uncertainties In this paper, we propose a model free digital adaptive control approach, which considers the time delay of the control signal, actuator saturation, and non-parametric uncertainties, for an under-actuated manipulator. We also develop a chaos control to learn the unbiased and smooth digital control policy inside the chaotic regions of the continuous time under-actuated manipulator. We perform real-time experiments in a dynamic environment with the proposed digital adaptive control. Then we compare the results of the learning and control with and without chaos control. We observe that the proposed model free adaptive control approach can accurately learn both the long-term predictor and unbiased control policy even in the chaotic regions of the under-actuated robot manipulator. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
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    Modified adaptive discrete-time incremental nonlinear dynamic inversion control for quad-rotors in the presence of motor faults
    (Academic Press Ltd- Elsevier Science Ltd, 2023) Ahmadi, Karim; Asadi, Davood; Nabavi-Chashmi, Seyed-Yaser; Tutsoy, Onder
    Unmanned air vehicles are intrinsically non-linear, unstable, uncertain, and prone to a variety of faults, most commonly the motor faults. The main objective of this paper is to develop a faulttolerant control algorithm for the quadrotors with the motor faults. Accordingly, a novel adaptive modified incremental nonlinear dynamic inversion (MINDI) control is proposed to stabilize and control the quad-rotor with partial motor faults. The controller consists of a MINDI controller augmented with a discrete-time nonlinear adaptive algorithm. Since the incremental nonlinear dynamic inversion (INDI) algorithm is essentially based on the sensor measurements, it necessitates the angular rates differentiation and therefore amplifies the high-frequency noises produced by the gyroscopes. The application of derivative filters causes unavoidable internal state delays in the INDI structure. Henceforth, the performance of the controller developed for the unstable and uncertain quadrotors degrades considerably. To address this drawback, this paper proposes the MINDI controller which basically derives the angular accelerations from the angular moment estimations. Furthermore, to increase the robustness of the MINDI against motor faults, a discrete-time adaptive controller has been incorporated. The performance of the proposed controllers is verified both through the nonlinear simulations and testbed experiments. The results are compared with a recent efficient algorithm, which had been implemented on a quad-rotor model.
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    On the Remarkable Advancement of Assistive Robotics in Human-Robot Interaction-Based Health-Care Applications: An Exploratory Overview of the Literature
    (Taylor & Francis Inc, 2024) Gongor, Fatma; Tutsoy, Onder
    With the rapid advancement of technology, assistive robotic entities have arisen as indispensable instruments within diverse Human-Robot Interaction (HRI)-based health-care applications. By integrating Artificial Intelligence (AI) into these assistive robotic entities, they gain the capacity to autonomously perceive, engage in sophisticated reasoning, and execute actions within highly dynamic and complex environments. In light of these impressive achievements, this paper highlights a three-stage exploratory overview of the literature on the remarkable advancement of assistive robotics in HRI-based health-care applications. The first stage initiates an assessment of assistive robotics spanning historical epochs from ancient to modern times. Following this, the second stage comprehensively explores assistive robotics investigations in the realm of HRI-based health-care with its four sub-fields including rehabilitation, geriatric-care, pediatric-care, and nursing. Finally, the third stage entails a thorough analysis of the common challenges encountered in these pertinent investigations and provides a set of recommendations. This comprehensive paper not only provides an abundance of studies for each concept, method, and application in HRI, but it also presents their theoretical foundations, strengths, gaps, critical challenges, and recommendations. The results of the conducted exploratory overview shed light on the noteworthy prominence of assistive robotic entities within the HRI-based health-care field. The acquired findings emphasize the positive impact of such entities on human health, affirming their pivotal role in contributing to the advancement and effectiveness of health-care interventions. Furthermore, this paper provides an opportunity for scholars and researchers actively engaged in the pertinent field to obtain comprehensive additional insights, serving as a guiding resource for their academic endeavors.
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    Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases
    (IEEE Computer Soc, 2022) Tutsoy, Onder
    Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates.
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    Reinforcement learning analysis for a minimum time balance problem
    (Sage Publications Ltd, 2016) Tutsoy, Onder; Brown, Martin
    Reinforcement learning was developed to solve complex learning control problems, where only a minimal amount of a priori knowledge exists about the system dynamics. It has also been used as a model of cognitive learning in humans and applied to systems, such as pole balancing and humanoid robots, to study embodied cognition. However, closed-form analysis of the value function learning based on a higher-order unstable test problem dynamics has been rarely considered. In this paper, firstly, a second-order, unstable balance test problem is used to investigate issues associated with the value function parameter convergence and rate of convergence. In particular, the convergence of the minimum time value function is analysed, where the minimum time optimal control policy is assumed known. It is shown that the temporal difference error introduces a null space associated with the experiment termination basis function during the simulation. As this effect occurs due to termination or any kind of switching in control signal, this null space appears in temporal differences (TD) error for more general higher-order systems. Secondly, the rate of parameter convergence is analysed and it is shown that residual gradient algorithm converges faster than TD(0) for this particular test problem. Thirdly, impact of the finite horizon on both the value function and control policy learning has been analysed in case of unknown control policy and added random exploration noise.