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Optimal Tuning of FOPID-Like Fuzzy controller for High-Performance Fractional-Order systems

computers, Materials & Continua 2022年第1期70卷 171-180页

作者： Ahmed M.Nassef Hegazy Rezk College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz UniversityAl-Kharj11911Saudi Arabia Department of Computers and Automatic Control Engineering Faculty of EngineeringTanta UniversityTanta31733Egypt Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61517Egypt

This paper addresses improvements in fractional order(FO)system *** the classical proportional-integral-derivative(PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems,the FOPID fuzzy controller has been proven to provide better *** high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic *** paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer,social spider optimization,in order to improve the response of fractional dynamical *** group of systems had usually produced multimodal error surfaces/functions that occasionally had many variant local *** integral time of absolute error(ITAE)used in this study was the error *** results showed that the strategy adopted produced superior performance regarding the lowest ITAE *** reached a value of 88.22 while the best value obtained in previous work was 98.87.A further comparison between the current work and previous studies concerning transient-analysis factors of the model’s response showed that the strategy proposed was the only one that was able to produce fast rise time,low-percentage overshoot,and very small steady-state ***,the other strategies were good for one factor,but not for the others.

关键词： Fuzzy-logic control fractional-order systems parameter estimation optimization

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Deep Reinforcement Learning Method for control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Liu, Xingyu Apriaskar, Esa Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang50229 Indonesia

ISBN: (纸本)9798350368031

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Learning control laws in simulated environments for motion gestures of humanoid robots

Learning control laws in simulated environments for motion g...

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International Conference on control systems and Computer Science (CSCS)

作者： Andrei Mateescu Ioana-Livia Stefan Dragos Constantin Popescu Ioan Stefan Sacala Silviu Raileanu Automatic Control and Systems Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei No. 313 Bucharest Romania Automation and Industrial Informatics Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei No. 313 Bucharest Romania

This article presents a way to implement a collection of fitness moves on an iCub (Cognitive Universal Body) robot, whose objective is to act as a fitness instructor and interact with humans. Due to the advantages of a simulation medium, the robot is simulated in the Gazebo simulator, on The Construct online platform. The control is done using Robot Operating System (ROS) with PID controllers for the robot joints, with the proportional, integral and derivative parameters tuned using a Genetic Algorithm. The scripts for the collection of moves and the algorithm used to tune the PID controllers are written in Python.

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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LIFELINE-Emergency Patient Data in a Distributed Manner

LIFELINE-Emergency Patient Data in a Distributed Manner

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Rusu, Dinu-Stefan Pavaloiu, Gabriel-Nicolae University POLITEHNICA of Bucharest Faculty of Engineering in Foreign Languages Bucharest Romania University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania

ISBN: (纸本)9781665460149

In the case of medical emergencies where the victim cannot respond to questions, the first responders risk harming the patient if they are not aware of the underlaying conditions and medication. Vulnerable people often carry accessories that contain critical information, either in plain text or in the form of smartphone-scannable codes that redirect to webpages. The problem with relying on internet servers alone is that they can be unresponsive in the time of need. Lifeline aims to solve this problem by using blockchain to store vital backup data. © 2022 IEEE.

关键词： Blockchain

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A Review of Manufacturing Technologies of Soft Magnetic Composites for Electric Machines 5

A Review of Manufacturing Technologies of Soft Magnetic Comp...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Manko, Roman Dmytro, Rieznik Vukotic, Mario Rupnik, Urban Miljavec, Damijan Corovic, Selma University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine

ISBN: (纸本)9798350359787

In this article the review of the existing manufacturing technologies of the soft magnetic materials have been performed. The existing technological protocols for production of the electrically insulated powders and their further treatment towards manufacturing of the soft magnetic materials are provided. The influences of different technological processes on the mechanical, electrical and magnetic properties of the resulting soft magnetic materials have been reviewed and described. Finally, the usage and implementation of the soft magnetic composites in the field of electric motor design have been discussed. © 2023 IEEE.

关键词： Electric motors

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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