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

作者： Chorna, Olha Kurliak, Petro Busher, Victor Chornyi, Oleksii Tytiuk, Valerii Zachepa, Iurii Institute of Education and Science in Electrical Engineering and Information Technologies Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Electrical Power Engineering Electrical Engineering And Electromechanics Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350395532

This paper presents client-server mobile system for diagnosis of asynchronous motors. The system is created as a mobile application for smartphones and tablets. The system provides processing of the measured values of the currents and voltages of the stator phases of the asynchronous motor. According to the processing results, the results of diagnostics and forecasting of the current state of asynchronous motors are calculated. The software runs on the Android OS. The main purpose is to predict the current state of AD, which has a parametric electromagnetic asymmetry of the stator phases. © 2023 IEEE.

关键词： Induction motors

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Steady-state analysis of networked epidemic models

arXiv

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arXiv 2023年

作者： Khong, Sei Zhen Su, Lanlan Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state values in a wide range of compartmental epidemic models, including both group and networked processes. In the case of a group (resp. networked) model, we show that the convergence limit of the susceptible proportion of the population (resp. the susceptible proportion in at least one of the subgroups) is upper bounded by the reciprocal of the basic reproduction number (BRN) of the model. The BRN, when it is greater than unity, thus demonstrates the level of penetration into a subpopulation by the disease. Both non-strict and strict bounds on the convergence limits are derived and shown to correspond to substantially distinct scenarios in the epidemic processes, one in the presence of the endemic state and another without. Formulae for calculating the limits are provided in the latter case. We apply the developed framework to examining various group and networked epidemic models commonly seen in the literature to verify the validity of our conclusions. Copyright © 2023, The Authors. All rights reserved.

关键词： Cell proliferation

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Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations

arXiv

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arXiv 2023年

作者： Jones, Morgan The Department of Automatic Control and Systems Engineering The University of Sheffield United Kingdom

This paper introduces a novel approach for learning polynomial representations of physical objects. Given a point cloud data set associated with a physical object, we solve a one-class classification problem to bound the data points by a polynomial sublevel set while harnessing Sum-of-Squares (SOS) programming to enforce prior shape knowledge constraints. By representing objects as polynomial sublevel sets we further show it is possible to construct a secondary SOS program to certify whether objects are packed correctly, that is object boundaries do not overlap and are inside some container set. While not employing reinforcement learning (RL) in this work, our proposed secondary SOS program does provide a potential surrogate reward function for RL algorithms, autonomously rewarding agents that propose object rotations and translations that correctly pack objects within a given container set. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

引用

European control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Homotopic Optimization for Autonomous Vehicle Maneuvering

Homotopic Optimization for Autonomous Vehicle Maneuvering

引用

IEEE Symposium on Intelligent Vehicle

作者： Jian Zhou Arvind Balachandran Björn Olofsson Lars Nielsen Erik Frisk Department of Electrical Engineering Division of Vehicular Systems Linköping University Linköping Sweden Department of Automatic Control Lund University Lund Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Optimization of vehicle maneuvers using dynamic models in constrained spaces is challenging. Homotopic optimization, which has shown success for vehicle maneuvers with kinematic models, is studied in the case where the vehicle model is governed by dynamic equations considering road-tire interactions. This method involves a sequence of optimization problems that start with a large free space. By iteration, this space is progressively made smaller until the target problem is reached. The method uses a homotopy index to iterate the sequence of optimizations, and the method is verified by solving challenging maneuvering problems with different road surfaces and entry velocities using a double-track vehicle dynamics model. The main takeaway is that homotopic optimization is also efficient for dynamic vehicle models at the limit of road-tire friction, and it demonstrates capabilities in solving demanding maneuvering problems compared with alternative methods like stepwise initialization and driver model-based initialization.

关键词： Space vehicles Roads Friction Kinematics Mathematical models Indexes Vehicle dynamics

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A Review of Parameterised MPC Algorithms

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IFAC-PapersOnLine 2023年第2期56卷 7692-7697页

作者： John A. Rossiter Oscar J. Gonzalez-Villareal Mohammad A.M. Sarbini Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Electrical and Electronic Engineering Programme Area Universiti Teknologi Brunei

This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised Predictive control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

关键词： Predictive control Model predictive and optimization-based control Monte Carlo methods

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Influence of Resonant Frequency on Power of the High-Voltage Transformer-Less Resonant Charger of Capacitive Energy Storage Device 7

Influence of Resonant Frequency on Power of the High-Voltage...

引用

7th IEEE International Conference on Smart Technologies in Power engineering and Electronics, STEE 2024

作者： Vinnychenko, Dmytro Nazarova, Natalia Vinnichenko, Irina Institute of Electrodynamics of the National Academy of Sciences of Ukraine Department of Technological Systems of Power Supply Kyiv Ukraine Institute of Pulse Processes and Technologies Department of Impulse Electrical Engineering Systems Mykolaiv Ukraine Admiral Makarov National University of Shipbuilding Department of Computerized Control Systems Mykolaiv Ukraine

ISBN: (纸本)9798331540999

The dependences of the charging time of the capacitive energy storage device to the specified voltage and power of the inverter high-voltage transformer-less resonant charger of the capacitive energy storage on the resonant frequency were obtained. The obtained dependences made it possible to substantiate the frequency range for which the greatest power of the high-voltage resonant charger of the capacitive energy storage device at the constant charging current will be provided. Simulation of the charging process of capacitive energy storage with high-voltage transformer-less resonant charger was performed. © 2024 IEEE.

关键词： Capacitor storage

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Operation Analysis and Performance Evaluation of a Modular Step-Down DC-DC Converter 7

Operation Analysis and Performance Evaluation of a Modular S...

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7th IEEE International Conference on Smart Technologies in Power engineering and Electronics, STEE 2024

作者： Stepenko, Serhii Stala, Robert Waradzyn, Zbigniew Yatchenko, Yevhenii Prystupa, Anatoliy Chernihiv Polytechnic National University Electrical Engineering Information and Measurement Technologies Chernihiv Ukraine Agh University of Krakow Department of Power Electronics and Energy Control Systems Kraków Poland

ISBN: (纸本)9798331540999

The paper presents a comprehensive analysis of the design parameters, operational characteristics and performance evaluation of a modular step-down DC-DC converter. The converter is engineered for applications demanding high-output current, featuring an input voltage of 48 V and an output voltage of 1 V. When operating in the single-stage voltage regulator module (VRM), its modular architecture is optimized to minimize resistive losses. Noteworthy is the integration of an input capacitor divider to alleviate voltage stress of components. Additionally, special attention is given to the impact of parasitic parameters on the converter's performance. Through a meticulous examination of system topology, validated by observed current and voltage waveforms, and analytical comparison to classic interleave converters, this study elucidates the operational intricacies of the converter. The findings, anchored in the context of a MOSFET-based four-module converter rated for a maximum power output of 350 W at 1 V, provide valuable insights for the design and optimization of similar power conversion systems. © 2024 IEEE.

关键词： DC-DC converters

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Exact Complexity Certification of Start Heuristics in Branch-and-Bound Methods for Mixed-Integer Linear Programming

Exact Complexity Certification of Start Heuristics in Branch...

引用

IEEE Conference on Decision and control

作者： Shamisa Shoja Daniel Axehill Department of Electrical Engineering Division of Automatic Control Linkoping University Sweden

Model predictive control (MPC) with linear performance measure for hybrid systems requires the solution of a mixed-integer linear program (MILP) at each time instance. A well-known method to solve MILP problems is branch-and-bound (B&B). To enhance the performance of B&B, start heuristic methods are often used, where they have shown to be useful supplementary tools to find good feasible solutions early in the B&B search tree, hence, reducing the overall effort in B&B to find optimal solutions. In this work, we extend the recently-presented complexity certification framework for B&B-based MILP solvers to also certify computational complexity of the start heuristics that are integrated into B&B. Therefore, the exact worst-case computational complexity of the three considered start heuristics and, consequently, the B&B method when applying each one can be determined offline, which is of significant importance for real-time applications of hybrid MPC. The proposed algorithms are validated by comparing against the corresponding online heuristic-based MILP solvers in numerical experiments.

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