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IEEE Conference on Decision and control

作者： Charalambos Menelaou Kangkang Zhang Stelios Timotheou Christos G. Panayiotou Thomas Parisini Dept. of Electrical and Computer Engineering KIOS CoE University of Cyprus Dept. of Electrical and Electronic Engineering Imperial College London London U.K. Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The increasing reliance on intelligent transportation systems (ITS) for traffic management has simultaneously heightened the potential for cybersecurity threats. Malicious cyber attacks on such systems can lead to operational inefficiencies, heightened congestion, and compromised safety. This work introduces a stealthy integrity attack tailored for ramp metered freeway systems. The attack model is conceptualized as a closed-loop dynamical system, formulated as an optimization problem based on the Cell Transmission Model. This work further proposes a distributed detection mechanism designed to identify attack residuals, which, by incorporating an adaptive threshold scheme, can detect the presence of an attack. To demonstrate the efficacy of our detection scheme, we present a scenario illustrating its application in freeway road networks.

关键词： Meters Adaptation models Roads Traffic control Safety Dynamical systems Cyberattack Optimization Intelligent transportation systems Resilience

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Discrete Shuffled Frog Leading Algorithm for Multiple-product Human-robot Collaborative Disassembly Line Balancing Problem

Discrete Shuffled Frog Leading Algorithm for Multiple-produc...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Fan, ChenYang Wang, JiaCun Guo, XiWang Qin, ShuJin Zhou, MengChu Qi, Liang Engineering of Liaoning Petrochemical University School of Information and Control Fushun China Engineering of Monmouth University Dept. Computer Science and Software West Long Branch United States Liaoning Petrochemical University Fushun China Monmouth University West Long Branch United States College of Economics and Management of Shangqiu Normal University Shangqiu China Dept. Electrical and Computer Engineering of New Jersey Institute of Technology Newark United States Tech of Shandong University of Science and Technology Dept. Computer Intelligent Sci. & Qingdao China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM's CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation. © 2022 IEEE.

关键词： Robots

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Linear Quadratic Regulator-based controller for control Mode Switching Under Fault Operation of Grid-Connected PV with Supercapacitor System

Linear Quadratic Regulator-based Controller for Control Mode...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Paychuda Kritprajun Leon M. Tolbert Nattapat Praisuwanna Jingxin Wang Yunting Liu Maximiliano Ferrari Dept. of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Dept. of Electrical Engineering The Pennsylvania State University PA USA Grid Components and Control Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable (completely charged) during fault operations. The SC improves the low-voltage ride-through (LVRT) capability of the PV system by quickly balancing power between the PV system and the grid. When the SC reaches its full charge capacity, the DC-link voltage will be controlled by the inverter’s controller, and the PV system may need to curtail its power generation. During the transition, fluctuations in the DClink voltage can occur which can compromise the stability of the PV system, especially when the control mode switching happens during severe fault operations. The proposed LQR-based controller is validated on a hardware testbed platform to demonstrate its effectiveness in reducing DC-link overvoltage during the control mode transitions. As a result, the PV system can remain compliant with LVRT requirements while maintaining stability during fault operations.

关键词： Voltage fluctuations Regulators Power quality Power control Switches Power system stability Supercapacitors control systems Voltage control Transient analysis

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Alexander M. Boronakhin Alexandra V. Bolshakova Dmitry M. Klionskiy Daniil Yu. Larionov Roman V. Shalymov dept. of Laser Measurement and Navigational Systems faculty of Information Measurement and Biotechnical Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Laser Measurement and Navigational Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Information Systems dept. of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.

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A Comparative Analysis of Deep Neural Network Models using Transfer Learning for Electrocardiogram Signal Classification 6

A Comparative Analysis of Deep Neural Network Models using T...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Rahuja, Nitin Valluru, Sudarshan K. Center for Control of Dynamical Systems and Computation Delhi Technological University Dept. of Electrical Engineering Delhi110042 India

ISBN: (纸本)9781665435598

With the development of computational resources in the past decade, the adoption of deep learning methodologies has taken a steep rise. Particularly, the use of various Convolutional Neural Network (CNN) based architectures for various computer vision and image classification problems in healthcare research can be witnessed. In this scope, performance evaluation and comparison of three different CNN architectures for multi-class classification of Electrocardiogram (ECG) signals is presented in this paper. Three distinct classes of ECG signal i.e. Normal Sinus Rhythm (NSR), Arrhythmia (ARR), and Congestive Heart failure (CHF) are utilized in this work, which is representative of an individual's heart conditions. The classification methodology adopted makes use of Continuous Wavelet Transform (CWT) which provides a 2-dimensional time-frequency representation of available samples of ECG signals. ECG dataset was collected from different PhysioBank databases, which provides openly-accessible medical research data. Images with time-frequency representation of ECG signals were applied as input to three CNN architectures namely, AlexNet, GoogLeNet, and SqueezeNet. Using transfer learning approach and modification in certain output layers of three architectures, ECG classification was performed and the performance of three CNN architectures was studied. The results revealed classification accuracy of 97.80%, 97.78% and 97.22% by AlexNet, GoogLeNet and SqueezeNet respectively. AlexNet outperformed GoogLeNet and SqueezeNet models in terms of accuracy as well as training time. © 2021 IEEE.

关键词： Electrocardiograms

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LEARNING EFFICIENT POSITIONAL ENCODINGS WITH GRAPH NEURAL NETWORKS

arXiv

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arXiv 2025年

作者： Kanatsoulis, Charilaos I. Choi, Evelyn Jegelka, Stephanie Leskovec, Jure Ribeiro, Alejandro Dept. of Computer Science Stanford University United States School of Computation Information and Technology Technical University of Munich Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node attributes in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity. Our code is available at https://***/ehejin/Pearl-PE. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A Deep Neural Network Approach to Automatic Multi-Class Classification of Electrocardiogram Signals

A Deep Neural Network Approach to Automatic Multi-Class Clas...

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Rahuja, Nitin Valluru, Sudarshan K. Delhi Technological University Center for Control of Dynamical Systems and Computation Dept. of Electrical Engineering Delhi110042 India

ISBN: (纸本)9781728185835

Classification of electrocardiogram (ECG) signals is essential for accurate clinical diagnosis of coronary illness. Deep Neural Network (DNN) has emerged as a promising tool for feature identification in ECG signals with advancements in signal processing techniques and artificial intelligence. Particularly, Convolutional Neural Network (CNN) has enabled intricate feature identification and classification of images. In this scope, an efficient classification methodology utilizing wavelet transform and AlexNet CNN is presented in this paper to classify the ECG signals into three different categories, namely: Arrythmia (ARR), Congestive Heart failure (CHF) and Normal Sinus Rhythm (NSR). Physionet research dataset is utilized in this work. Firstly, all ECG signals are converted to a corresponding scalogram using Continuous Wavelet Transform (CWT). Then, these scalograms are applied as input to AlexNet, which is a pretrained CNN. Finally, transfer learning and fine-tuning are done, and the performance of modified AlexNet is evaluated. The study revealed a good accuracy of 97.3%. © 2021 IEEE.

关键词： Electrocardiograms

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Improved Performance of a Two-Stage Converter Topology Using Incremental Conductance-Based MPPT Design for Grid-Tied PV systems

Improved Performance of a Two-Stage Converter Topology Using...

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IEEE Southeastcon

作者： Md. Sazib Mollik S M Sajjad Hossain Rafin Abdullah Al Mamun Abdul Munaf Mohamed Irfeey Ahmed S. Soliman Osama A. Mohammed Dept. of Electrical and Electronic Engineering Northern University Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA Dept. of Electrical and Computer Engineering South Eastern University of Sri Lanka Oluvil Sri Lanka

A DC-DC converter is used between the PV modules and the inverter in a two-stage grid-integrated photovoltaic (PV) system. The dynamic interactions between the maximum power point tracking (MPPT) controller, inverter, and DC-DC converter may affect the system’s performance. This grid-connected system model integrated the MPPT mechanism to maximize the output power of the photovoltaic array, regardless of the temperature, irradiation conditions, and load characteristics. The PV system’s output voltage is regulated using a control technique with multiple inputs. The multiple input controller accommodates the high-frequency switching action in the inner loop, and the PI controller in the outer loop is designed to improve the dynamic performance. The proposed incremental conductance MPPT-based two-stage converter topology is designed and simulated with MATLAB Simulink simulation tool to validate the enhanced performance and analyzed power quality of the current injected into the grid. The performance analysis of the proposed system indicates that the closed-loop performance can be improved in several ways, including a faster response with less overshoot.

关键词： Maximum power point trackers Photovoltaic systems Temperature dependence Temperature DC-DC power converters Mathematical models Inverters

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Geometric Modelling and control of Flexible Serial Robot Links

Geometric Modelling and Control of Flexible Serial Robot Lin...

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European control Conference (ECC)

作者： Nishant Mittal Shreya Shrivastava Viyom Vivek José Ángel Acosta Ravi Banavar Electrical Engg. Dept. IIT Bombay Engg. Phy. Dept. IIT Bombay Systems & Control group IIT Bombay Dept. de Ingeniería de Sistemas y Automática Universidad de Sevilla Spain Systems & Control Engineering group IIT Bombay

The article presents the initial attempts to provide a geometric framework for modelling and control of flexible link robotic manipulators whose end-effector interacts with a compliant surface. The case study presented here restricts itself to planar flexible link manipulators. Ideas from Lie groups and Lie algebras have been employed to derive the forward and inverse kinematic maps, and their associated Jacobian. Finally we employ the geodesic equation to write down the comprehensive dynamical equations of the system. A geometrical interpretation of the Jacobian is presented and a velocity-level control law proposed earlier in literature is recast in the geometric language. Lastly we incorporate Christoffel symbols in the dynamic model of the flexible manipulator highlighting the underlying geometry. Using this framework, an existing velocity based control law is synthesized and simulated. Results demonstrate that the geometric framework yields a smoother and faster error convergence.

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LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain

arXiv

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arXiv 2024年

作者： Musumeci, Emanuele Brienza, Michele Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico Daniele Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy UNINT Univeristy Via Cristoforo Colombo 200 Rome00147 Italy

In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity arises from the need to handle a wide range of document types, often characterized by semi-structured forms. Semi-structured documents present a fixed set of data without a fixed format. As a consequence, a template-based solution cannot be used, as understanding a document requires the extraction of the data structure. The recent introduction of Large Language Models (LLMs) has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired *** main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world PA scenarios. © 2024, CC BY.

关键词： Multi agent systems

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