Abstract:To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating conditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation environment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simulation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and Differential Evolution.
The measures taken during the pandemic have had lasting effects on people’s lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people’s behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).
Kavuran, G., Gurgenç, T. & Özkaynak, F. On the modeling of the multi-segment capacitance: a fractional-order model and Ag-doped SnO2 electrode fabrication. J Mater Sci57, 2775–2793 (2022). https://doi.org/10.1007/s10853-021-06670-y
This study proposes a methodology of electrochemical capacitor modeling via fractional-order impedance equation for porous electrodes fabricated with pure and Ag-doped SnO2 nanoparticles. It was carried out to prove the assumption that fractional-order integrodifferential expressions better model the various real systems. Firstly, the pure and different amounts of silver (Ag)-doped tin oxide (SnO2) nanoparticles were produced using the hydrothermal method. Tin (II) chloride dihydrate (SnCl2·2H2O) was used as an Sn source and (AgNO3) as an Ag source. Hydrothermal synthesis was completed at 200 °C for 24 h. The synthesized particles were calcined at 600 °C for 2 h. All of the structural and morphological properties were investigated by FT-IR, XRD, FE-SEM, and EDX. It has been observed that the hydrothermal method successfully produced nano-SnO2 particles without and with Ag dopant. As a result of the applied procedure, the structural properties of SnO2 nanoparticles, such as physical shape, were changed from spherical-like to nano-sheet with the Ag doping. Next, the nanopowders were coated on AZ31 magnesium sheets. Electrochemical impedance spectroscopy measurements were examined to determine the capacitance of EC materials with Ag-doped SnO2 nanoparticles. Finally, using the multi-objective cost function, the experimentally measured real and imaginary impedance parts are fitted to the proposed fractional-order model by the particle swarm optimization algorithm. It has been proven that fractional-order modeling enables finding the electrical parameters and properties of EC with higher accuracy. Furthermore, the Ag-doped SnO2 electrode can significantly improve electrical performance because of the increase in conductivity. The total capacitance gets increased by 10.788% for 7% Ag-doped SnO2 against pure SnO2.
Abstract: The challenge of classifying multivariate time series generated by discrete and continuous dynamical systems according to their chaotic or non-chaotic behavior has been studied extensively in the literature. The examination of noise or the variation of variables that affect a dynamic system’s chaoticity will not be beneficial in analyzing structures employing random number generators (RNG) that are already assured to be chaotic. However, detecting the structural changes and their time intervals in deterministic systems with proven chaoticity can contribute to the literature in encryption applications. Machine Learning algorithms provide flexible possibilities to analyze and predict manipulations that may occur in the dynamics of chaotic and complex systems. This study proposes a deep Long-Short-Term-Memory (LSTM) network with a classification process to predict dynamical changes in a fractional-order chaotic (FOC) system. First, the appropriate system parameters are calculated to satisfy the chaotic behavior in the fractional-order Chen system. The predictive-corrective Adams-Bashforth-Moulton algorithm is used to simulate the FOC Chen system in the time domain. The Lyapunov exponents of the system were obtained according to the Wolf method. Next, three different scenarios have been designed to test and demonstrate the effectiveness of the proposed method. Synthetic FOC signals obtained after sub-sampling and statistical feature extraction processes fed the input of the deep bidirectional LSTM (BiLSTM) network to perform the training and testing process. The classification performance for “q” and “c” classes reaches 100% with the proposed model. The overall average testing accuracy, sensitivity, specificity, precision, F1 score and MCC are 98%, 98%, 99.3%, 98.1%, 98%, and 97.3%, respectively. Our results demonstrate the utility of using a deep BiLSTM network for detecting dynamical variations in nonlinear FOC systems. Keywords: Fractional-order dynamical systems; Chaos; Deep learning; BiLSTM; Time series; Classification
Abstract: Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers’ potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model’s performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method. Keywords: Hydrothermal method; Nanoscience; Ag-doped SnO2; Feature selection; Binary Particle Swarm Optimization; Deep learning
Malatya Turgut Özal Üniversitesi (MTÜ) Tıp Fakültesi ve Mühendislik Fakültesi öğretim üyelerince geliştirilen ve MTU-COVNet olarak isimlendirilen yapay zekâ yazılımı, Covid-19 tanısında oldukça yüksek başarı elde etti. Geliştirilen yapay zekâ programı ile Covid-19 hastalarının teşhisinde yaklaşık yüzde 98 oranında başarıya ulaşıldı.
“DİKKAT ÇEKEN ARAŞTIRMACI” ÖDÜLÜNE LAYIK GÖRÜLDÜ
Uygulama, uluslararası tıp dergisi Clinical Imaging’de de yayımlanırken Doç. Dr. Erdal İn, Dr. Öğretim Üyesi Ayşegül Altıntop Geçkil, Dr. Öğretim Üyesi Nurcan Kırıcı Berber, Elektrik Elektronik Mühendisliği Bölüm Başkanı Dr. Öğretim Üyesi Gürkan Kavuran ile MTÜ Eğitim ve Araştırma Hastanesinden Uzman Dr. Mahmut Şahin bu çalışmalarıyla ‘Solunum2021 Dijital Kongresi’nde “Dikkat Çeken Araştırmacı” ödülüne layık görüldü.