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European control Conference (ECC)

作者： Adrian Burlacu Radu L. Rosca Andrei I. Iancu Enric Cervera Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Romania Dept. of Computer Science and Engineering Jaume I University Spain

In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.

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NEW TOOL FOR SYSTEM IDENTIFICATION AND PREDICTIVE control BASED ON NEURAL NETWORKS USING LABVIEW SOFTWARE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2022年第2期84卷 91-110页

作者： Alshareefi, Hamid Lupu, Ciprian Alboudkhal, Hayder Ismail, Laith Dept. of Automatic Control and Computers University POLITEHNICA of Bucharest Romania Ministry of Electricity Iraq Dept. of Computer Technology Engineering AL-TURATH University College Baghdad Iraq

In this paper, a new tool for system identification and model predictive control (MPC) has been developed. The mathematical approximation of the model identification was derived using the neural network theory. The emphasis of this paper is to employ a multi-layer recurrent neural network with three layers to match an autoregressive-moving average (ARMA) model in the identification stage to provide the mathematical model for the model predictive controller in the control stage. The tool was designed using LabVIEW software and the MathScript facility. The tool can be used to identify and control simulated or real systems using the stored input/output data of the system. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Recurrent neural networks

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture

Optimization of Neural Networks using Swarm Intelligence Tec...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Karthick Raja Makhbuba Shermatova Mohammed Saleh Al Ansari Shokhjakhon Abdufattokhov Vuda Sreenivasa Rao I. Infant Raj Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Civil Engineering and Architecture Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Automatic Control and Computer Engineering Dept Turin Polytechnic University Dept of Information Technologies Tashkent Intl. University of Education Tashkent Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Fou. Green Fields Vaddeswaram A.P India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.

关键词： Training Energy consumption Smart buildings Architecture Heuristic algorithms Neural networks Energy efficiency

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Study of the Influence of Rolling Modes on the Operation of Interconnected Electric Drives of a Rolling Mill 4

Study of the Influence of Rolling Modes on the Operation of ...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Tytiuk, Valerii Busher, Victor Kalinin, Alexey Chornyi, Oleksii Druzhinin, Valeriy Halushko, Dmytro Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Informative computer systems Abylkas Saginov Karaganda Technical University NPJSC Karaganda Kazakhstan Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Power Engineering Karaganda Industrial University NPJSC Temirtau Kazakhstan Dept. of Electrical Engineering Ukrainian State University of Science and Technologies Dnipro Ukraine

ISBN: (纸本)9798350395532

The paper is devoted to study of the influence of rolling modes on the performance of powerful interconnected electric drives of a hot rolling mill under the action of an electromagnetic coupling circuit between the electric drives of the roughing and finishing groups. The action of the electromagnetic coupling circuit manifests itself in the form of the influence of impact loads of synchronous electric drives on the angular velocity of direct current electric drives of the finishing group of the mill. The parameters of the electromagnetic coupling circuit are determined by the power supply scheme and equipment parameters. Values of impact loads applied to a synchronous motor depend on the rolling process parameters, in particular, the chemical composition of the rolled steel and the temperature of the ingot. The paper presents the results of calculating the rolling power for various rolling mode parameters. Using mathematical modeling in Simulink, numerical estimates of the influence of rolling modes on the magnitude of the voltage drop in the power supply unit and the drop in the angular velocity of the electric drive of the finishing group were obtained on the example of a wide-strip hot rolling mill 1700 of ArcelorMittal Temirtau JSC. © 2023 IEEE.

关键词： Hot rolling

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Home Care Assistance Solution based on a Software Multi-Agents System

Home Care Assistance Solution based on a Software Multi-Agen...

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International Conference on control Systems and computer Science (CSCS)

作者： Mohammad Rasras Iuliana Marin Şerban Radu Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Computer Science University POLITEHNICA of Bucharest Bucharest ROMANIA

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.

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A computer Vision-Based Solution for Assessing Prosthetic Leg Length Discrepancy

A Computer Vision-Based Solution for Assessing Prosthetic Le...

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E-Health and Bioengineering Conference (EHB)

作者： Otilia Zvorişteanu Dimitrie-Cristian Fodor Vasile-Ion Manta Neculai-Eugen Seghedin Ştefan-Daniel Achirei Dragoş-Florin Chitariu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania “Dr. Iacob Czihac” Military Emergency Clinical Hospital Iaşi Dept. of Digital Production Systems Faculty of Machine Manufacturing and Industrial Management “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

This paper presents a novel computer vision-based approach for assessing leg length discrepancy (LLD) in individuals with prosthetic limbs. The proposed solution uses image processing techniques to detect markers placed on the patient's knee, prosthesis, and a reference wall, allowing for precise measurement of limb alignment. Through a comparative analysis of the initial reference position, set by a specialist, and the current limb positioning, the algorithm identifies discrepancies in leg length. The system employs a non-invasive methodology, utilizing an IP camera to capture images and communicate them via Wi-Fi to a computing unit for further analysis. Experimental validation, conducted on simulated LLDs ranging from 1mm to 10mm, demonstrates the system's high sensitivity and accuracy in detecting subtle changes in limb alignment. This approach offers a scalable, automated alternative to traditional manual methods, improving both the reliability and ease of prosthetic adjustments.

关键词： Legged locomotion Wireless communication Limbs Prosthetic limbs Current measurement Manuals Cameras Reliability IP networks Wireless fidelity

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Study of the Shock Loads Effect on the Operation of Multi-Motor Electric Drives of a Rolling Mill with a Second-order Elastic Coupling 5

Study of the Shock Loads Effect on the Operation of Multi-Mo...

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

作者： Tytiuk, Valerii Kalinin, Alexey Druzhinin, Valeriy Kuznetsov, Vitalii Chornyi, Oleksii Busher, Victor Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine Abylkas Saginov Karaganda Technical University Npjsc Department of Informative Computer Systems Karaganda Kazakhstan Karaganda Industrial University Npjsc Department of Power Engineering Temirtau Kazakhstan Ukrainian State University of Science and Technologies Department of the Electrical Engineering Dnipro Ukraine National University Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi Kremenchuk Ukraine National University "odessa Maritime Academy" Dept. of Electrical Engineering and Electronics Odessa Ukraine

ISBN: (纸本)9798350359787

This paper presents a study for optimizing a hot rolling mill. The paper is devoted to the study of ways to reduce the effect of shock loads of synchronous electric drives of the roughing mill group on the relative lengthening of the metal strip in the inter-stand gap of the rolling stands of the finishing mill group. The elasticity of the metal strip in the inter-stand gap of the rolling mill has a significant impact on the movement of intercoupled multi-motor electric drives of the finishing mill group. Another crucial route of exposure that influences the motion of the electric drives within the finishing mill group involves the electromagnetic connection between the drives in the roughing and finishing mill groups. The impact of the electromagnetically connected circuits is evident through the effect of shock loads from synchronous electric drives on the angular velocity of the direct current electric drives within the finishing mill group. The characteristics of the electromagnetic communication circuit are established by the power grid arrangement and equipment specifications. To assess the quality of work of electric drives of the finishing mill group, it is proposed to use the relative lengthening of the metal strip in the inter-stand gap, which is an important indirect indicator of the quality of rolling. The mathematical model of a hot rolling mill that takes into account both the elasticity of the metal strip that connects the DC electric drives of the finishing group, and the electromagnetic coupling circuit that takes into account the impact loads of synchronous electric drives of the roughing group on the example of the mill 1700 of ArcelorMittal Temirtau JSC was proposed in this paper. Using the mathematical model developed in Simulink software using the methods of planning the experiment, regression models were obtained to establish the dependence of the parameters of overexcitation of a synchronous motor on the magnitude of the voltage sag in the no

关键词： Electric loads

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A Machine Learning Approach to Classify Network Traffic 13

A Machine Learning Approach to Classify Network Traffic

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Jadav, Nilesh Dutta, Nitul Sarma, Hiren Kumar Deva Pricop, Emil Tanwar, Supeep Marwadi University Dept. of Computer Engineering Rajkot India Sikkim Manipal Inst. of Technology Dept. of Information Technology Majitar India Petroleum-Gas University of Ploiesti Automatic Control Comp and Elect. Dept Ploiesti Romania Nirma University Dept. of Computer Science and Eng. Ahmedabad India

ISBN: (纸本)9781665425346

There is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic to determine patterns of malicious activity is a very difficult task and the results might not be satisfactory. The usage of machine learning is the most viable approach for the classification of the traffic as normal or malign activity. This paper analyzes CIC-Darknet 2020 dataset to classify the benign and darknet traffic. Before applying any classifiers to our dataset, we have balanced it using Synthetic Minority Oversampling Technique (SMOTE). We have applied PCA to reduce dimensionality, furthermore, ensemble techniques, logistic classifiers, tree-classifiers, and Naive Bayes have been compared and evaluated thoroughly with various evaluation metrics Accuracy, Precision, Recall, F1-Score, and Mathew's Correlation Coefficient (MCC). © 2021 IEEE.

关键词： Machine learning

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DESIGN OF PID controlLER FOR NONLINEAR MAGNETIC LEVITATION SYSTEM USING FUZZY-TUNING APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2022年第2期84卷 45-62页

作者： Ismail, Laith S. Lupu, Ciprian Alshareefi, Hamid Luu, Duc Lich Dept. of Automatic Control University POLITEHNICA of Bucharest Romania Computer Technology Engineering Department Al Turath University Collage Baghdad Iraq Faculty of Mechanical Dynamics Vinh University of Technology Education Nghe an Viet Nam

This paper presents a dynamic model of a magnetic levitation system (Maglev) and suggests a Fuzzy Tuning of a proportional—integral--derivative controller (PID) that uses a fuzzy approach system to determine the PID controller's settings. The controller allows a steel ball to be suspended in free fall in the chosen position in reference to the electromagnet bottom end. The electromagnet levitation system can be controlled with a traditional PID controller, however because of the highly nonlinear of this system, it is unpredictable in the case of load and air gap changes. The fuzzy rules are developed to fuzzy tune PID while considering the control response and behavior of the system to find a solution to this problem. This paper shows excellent stability, rising time, settling time overshoot, and robust response results. The results reveal that this fuzzy PID controller solution can stabilize the ball's location and has strong disturbance rejection. The real time platform is designed for educational laboratory and test all the proposed control systems. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： controllers

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Convex neural network synthesis for robustness in the 1-norm

arXiv

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arXiv 2024年

作者： Drummond, Ross Guiver, Chris Turner, Matthew C. Dept. of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom School of Computing Engineering & the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off has to be navigated. To address this issue, this paper proposes a method to generate an approximation of a neural network which is certifiably more robust. Crucially, the method is fully convex and posed as a semi-definite programme. An application to robustifying model predictive control is used to demonstrate the results. The aim of this work is to introduce a method to navigate the neural network robustness/accuracy trade-off. © 2024, CC BY.

关键词： Model predictive control

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