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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Kameli, Sayed Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Guzinski, Jaroslaw Texas AandM University at Qatar Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdansk University of Technology Electrical and Control Engineering Faculty ul. Gabriela Narutowicza 11/12 Gdansk80-233 Poland

ISBN: (纸本)9798350300048

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity. © 2023 IEEE.

关键词： Power transformers

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Community Membership Hiding via Gradient-based Optimization

arXiv

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arXiv 2025年

作者： Silvestri, Matteo Gabrielli, Edoardo Silvestri, Fabrizio Tolomei, Gabriele Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇- CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency. © 2025, CC BY.

关键词： Optimization algorithms

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Bicheng Guo Conghao Zhou Haoyu Liu Shibo He Jiming Chen Xuemin Sherman Shen College of Control Science and Engineering Zhejiang University China Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

关键词： Continuing education Training Deep learning Adaptation models Multi-access edge computing Computational modeling Data models Global communication Convergence

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Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion Infilling

Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Leiyang Xu Qiang Wang Chenguang Yang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Motion infilling is a fundamental and challenging research field in human motion modeling and analysis, which aims to generate natural and visually coherent transitions to fill in missing motion frames based on the start and end motion sequences. However, most current methods ignore the spatial structure formed by joints, which may lose some spatial information. This work proposes a novel spatiotemporal graph U-Net that supports flexible inputs for skeleton-based motion infilling. We apply spatiotemporal graph convolutional layers, skeleton pooling layers, and skeleton unpooling layers to extract spatial and temporal features in the motion sequence. At the same time, we use the U-Net structure to integrate the information in the start and end motion sequences. In addition, the generative adversarial mechanism is introduced to ensure the generated skeleton poses are smooth and natural. We conduct experiments on two motion datasets, including one large-scale public dataset and one self-built dataset. The model inputs are joint quaternions or joint coordinates. Experimental results show that our method can improve the performance of skeleton-based motion infilling and achieve state-of-the-art results when using joint coordinates as model input.

关键词： Analytical models Quaternions Feature extraction Skeleton Data models Spatiotemporal phenomena

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Stress Matrix-Based Formation control of Multi-Agent Systems with Discrete-Time Communication 12

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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2024 12th International Conference on Systems and control, ICSC 2024

作者： Onuoha, Okechi Ubochi, Brendan Chijioke Kurawa, Suleiman Tang, Zezhi Wu, Changshun Dong, Yi University of Hertfordshire United Kingdom Federal University of Technology The Department of Electrical and Electronic Engineering Akure Nigeria University of Manchester The Department of Electrical Engineering United Kingdom University of Sheffield The Department of Automatic Control and Systems Engineering United Kingdom University Grenoble Alpes Grenoble38000 France The School of Electronics & Computer Science University of Southampton United Kingdom

ISBN: (纸本)9798331531812

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws. © 2024 IEEE.

关键词： Continuous time systems

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations 43

Optimal denial-of-service attack scheduling for remote state...

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43rd Chinese control Conference, CCC 2024

作者： Yang, Xinxin Ni, Yuqing Chen, Xiaomeng Chen, Shan Liu, Fei Jiangnan University The Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9789887581581

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems (CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR (Signal-to-Interference-plus-Noise Ratio) model. It should be noted that the same attack power can lead to different packet loss rates in different network environments. From the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process (SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process (MDP). The simulation results support our theoretical findings. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Markov processes

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Keeping Eyes on the Road: Understanding Driver Attention and Its Role in Safe Driving 9

Keeping Eyes on the Road: Understanding Driver Attention and...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Ponzi, Valerio Russo, Samuele Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy

Monitoring the driver’s attention is an important task to maintain the driver’s safety. The estimation of the driver’s gaze direction can help us to evaluate if the drivers are not focusing their attention on the street. For an evaluation of this type, comparing the inside view and outside scenery of the vehicle is essential, therefore we decided to create a specific dataset for this task. In this work, we realize a machine-learning-oriented approach to driver’s attention evaluation using a coupled visual perception system. By analyzing the road and the driver’s gaze simultaneously it is possible to understand if the driver is looking at the traffic signs detected. We evaluate if a determined Region Of Interest (ROI) contains a road sign or not through YOLOv8. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Assessing the Reliability of Different Split Computing Neural Network Applications

Assessing the Reliability of Different Split Computing Neura...

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Latin American Test Workshop, LATW

作者： Giuseppe Esposito Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Marco Levorato Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Computer Science Department University of California Irvine US

ISBN: (数字)9798350365559

ISBN: (纸本)9798350365566

1 Neural Networks (NNs) are increasingly adopted in many domains. Given their considerable computational expenses required only during inference, it has been proposed that their architecture can be split into two sections (Head and Tail) in a paradigm referred to as Split Computing (SC). The Head section executes the first part of a NN at a mobile device, while a Tail model calculates the missing part of the NN resorting to a server. SC allows a designer to find the optimal configuration, trading off computing load, performance, latency, and power. However, the adoption of SC approaches in safety-critical domains involves strong reliability concerns. This paper introduces an environment allowing to estimate the effects of permanent faults affecting the hardware supporting the execution of the head model, which is the most prone to faults. Results are reported for different NNs and different configurations, demonstrating the effectiveness of the method and paving the way towards the deployment of more resilient SC systems. Our results show that the considered split configurations of all models under test are up to 10% more critically vulnerable to faults than the corresponding original architecture.

关键词： Performance evaluation Sensitivity Computational modeling Neurons Artificial neural networks Tail computer architecture

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Max Flow Vulnerability of Undirected Planar Networks 32

Max Flow Vulnerability of Undirected Planar Networks

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Balzotti, Lorenzo Franciosa, Paolo G. Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Statistical Science Sapienza University of Rome p.le Aldo Moro 5 Rome00185 Italy

In the real world, planar networks commonly model utility distribution networks such as gas pipelines, water distribution, city's electric grid, and transportation networks like railways and highways. Evaluating the vulnerability of these networks is crucial on several point of view: in a military context in order to prevent terrorist attack, or for maintenance to prevent wear and tear or natural disasters from inflicting extremely severe damage. This work aims to evaluate the vulnerability of undirected planar network studying the decrease of maximum st-flow when an edge or a vertex is removed, that is, we propose algorithms for computing the max flow vitality of edges and vertices. Our algorithms are applied to real world scenarios, and we prove that high vitality values are well approximated in time close to the time currently required to compute st-max flow O(n log log n). All our algorithms work in O(n) space. © 2024 Copyright for this paper by its authors.

关键词： Natural gas pipelines

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