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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zaiets, Nataliia Lutska, Nataliia Vlasenko, Lidiia National University of Life and Environmental Sciences of Ukraine Det. of Automation and Robotic Systems Kyiv Ukraine National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9798350346831

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department. © 2022 IEEE.

关键词： Evaporation

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Executive Conference Chair Welcome

2023 IEEE Gaming, Entertainment, and Media Conference, GEM 2...

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2023 IEEE Gaming, Entertainment, and Media Conference, GEM 2023 2023年

作者： Hogue, Andrew Cannavo, Alberto Game Development & Interactive Media Ontario Tech University Oshawa Canada Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy

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A Novel Real-Time Redundant System For Aiding Drivers To Increase Their Attention Level

A Novel Real-Time Redundant System For Aiding Drivers To Inc...

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Smart World Congress (SWC), IEEE

作者： Jacopo Sini Luigi Pugliese Pietro D’Agostino Massimo Violante Riccardo Groppo Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy Sleep Advice Technologies S.r.l Turin Italy

Road accidents are a major concern worldwide. Research of technological solutions to improve road safety is an open topic, considering this issue from various perspectives. As widely reported in the literature, driver distraction is one of the most common causes of collisions. Even if vehicles are becoming safer and safer over the years, pedestrians and cyclists are still exposed to severe accidents. Major concerns about driver distraction is mobile phone use and drowsy driving. This paper proposes a virtual buddy designed to help drivers, thanks to warning sound messages, improve their attention level. The main idea is to recognize, thanks to different sources (video camera, physiological data, and interior environmental conditions) interpreted by a data-fusion algorithm, whenever there is a distracted or drowsy behavior, recalling the driver to the road. The effectiveness of the data-fusion algorithm in detecting dangerous conditions has been verified thanks to a driving simulator experimentally, obtaining no false negatives from distraction from drowsiness, a sensitivity of about 85% for the distraction caused by mobile phone usage or other activities different from focusing on driving.

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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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Real-time Sleep Prediction Algorithm using Commercial Off the Shelf Wearable Devices

Real-time Sleep Prediction Algorithm using Commercial Off th...

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Smart World Congress (SWC), IEEE

作者： Luigi Pugliese Massimo Violante Riccardo Groppo Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy Sleep Advice Technologies S.r.l. Turin Italy

As reported by the American National Highway Traffic Safety Administration (NHTSA), sleep while driving is one of the most influential factors in fatal vehicle crashes, along with excessive vehicle speed and alcohol consumption. Physiologically speaking, driving for more than two hours in a nocturnal environment produces a driving impairment like a blood alcohol concentration of 0.05%. In this work, we present an innovative and patented sleep prediction method based on the analysis of the Autonomic Nervous System (ANS) (and its subsystems) that monitors the actions happening during the transition from awake to sleep onset. The prediction method processes the Heart Rate (HR) and the Heart Rate Variability (HRV) as collected by a wearable device on the subject wrist. Using a sliding window approach that operates on 20 seconds of samples (acquired at 1 Hz), the trend of the variance of HR and HRV is used to classify the subject condition according to a reduced Karolinska Sleepiness Scale (rKSS) that comprises five stages: Calibration, Awake, Low Drowsiness level, Medium Drowsiness level, High Drowsiness level. The prediction method has been validated experimentally using a set of recordings acquired in a realistic environment (AVL dynamic car simulator, in Graz (AT)). During the experiments, 15 subjects performed several rounds of the Maintenance Wakefulness Test (MWT). Each subject was equipped with a wearable device and apolysomnography medical equipment to gather both the data processed by the proposed approach, and the data set that constituted the ground truth under the supervision of a sleep expert medical doctor. A further experimental section has been conducted, involving the Italian truck company Chrono Express. 15 different drivers have utilized the built-up system for more than 13000 km. The proposed method is sensor agnostic, as it has been proven through preliminary activities with contactless Radio Frequency (RF) sensors. The output produced by the propos

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Real-Time Local Map Generation and Collision-Free Trajectory Planning for Autonomous Vehicles in Dynamic Environments

Real-Time Local Map Generation and Collision-Free Trajectory...

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Aristeidis Geladaris Lampis Papakostas Athanasios Mastrogeorgiou Michael Sfakiotakis Panagiotis Polygerinos TWI-Hellas Athens Greece Mechanical Engineering Dept. Control Systems and Robotics Lab (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Crete Greece Control Systems Lab (CSL) School of Mechanical Engineering National Technical University of Athens Greece Electrical and Computer Engineering Dept. Control Systems and Robotics Lab (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Crete Greece

In this paper we propose a method for autonomous navigation in GPS-denied environments that can be deployed in all types of robotic vehicles requiring only a single RGB-D camera. Our algorithm is an integration of localization, mapping and trajectory planning software modules that can handle dynamic environments. dept. measurements and visual odometry are used to (a) create a robocentric Euclidean Signed Distance Fields (ESDF) map in real-time, (b) estimate the position of obstacles surrounding the robot, and (c) calculate the distance and gradient from them. Subsequently, we use an optimization algorithm to plan a collision-free trajectory. We show that a local map can be created faster than with the fixed-size array method used in current optimization methods. Our approach also allows for effective detection of dynamic obstacles by constantly updating the map. As a result, the robot deviates from its initial path only when necessary. We validate our results in simulation by deploying our algorithm in a skid steering rover and a hexacopter in an environment with static and dynamic obstacles.

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Dynamic Topology Optimization for Efficient and Decentralised Federated Learning

Dynamic Topology Optimization for Efficient and Decentralise...

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IEEE International Conference on Big Data

作者： Danilo Menegatti Alessandro Giuseppi Cecilia Poli Antonio Pietrabissa Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy National Centre for Innovative Technologies in Public Health Department Istituto Superiore di Sanità Rome Italy

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario.

关键词： Federated learning Heuristic algorithms Big Data Autonomous agents Topology Optimization Convergence

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm

Enhancing 6G-Satellite Network Integration with Artificial I...

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Learning and Technology Conference (L&T)

作者： Ibrahim Salah Aziza I. Hussein Mostafa Wageh Lotfy Mohamed Mourad Mabrook Control and Computer Department College of Engineering Almaaqal University Basrah Iraq College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future.

关键词： 6G mobile communication Wireless communication Smart cities Resource management Reliability Internet of Things Stakeholders Artificial intelligence Vehicle dynamics Satellite communications

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Bridging the Gap between Trust–Region Methods (TRMs) and Linesearch Based Methods (LBMs) for Nonlinear Programming: Quadratic Sub–Problems

SSRN

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SSRN 2022年

作者： Fasano, Giovanni Piermarini, Christian Roma, Massimo Dept. of Management Ca’ Foscari University of Venice Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Italy

We consider the solution of a recurrent sub–problem within both constrained and unconstrained Nonlinear Programming: namely the minimization of a quadratic function subject to linear constraints. This problem appears in a number of LBM frameworks, and to some extent it reveals a close analogy with the solution of trust–region sub–problems. In particular, we refer to a structured quadratic problem where five linear inequality constraints are included. We show that our proposal retains an appreciable versatility, despite its particular structure, so that a number of different real instances may be reformulated following the pattern in our proposal. Moreover, we detail how to compute an exact global solution of our quadratic sub–problem, exploiting first order KKT conditions. © 2022, The Authors. All rights reserved.

关键词： Nonlinear programming

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