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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gaiardelli, Sebastiano Dall'ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Fummi, Franco Di Cataldo, Santa Vinco, Sara University Of Verona Department Of Engineering For Innovation Medicine Italy Politecnico Di Torino Department Of Control And Computer Engineering Italy

ISBN: (纸本)9798350340266

The Industry 4.0 paradigm has deeply changed classical manufacturing by introducing data-based analytics and decision-support strategies. At the state of the art, data used for manufacturing monitoring is mostly originated by sensors, that undergo a fusion step to align different data sources. However, this data is only relative to the monitored process, and it does not include the corresponding operating conditions and parameters, that are known by the Manufacturing Execution System (MES). Such information is currently either not included or labeled by hand, thus incurring in errors and limiting the amount of available labeled data. To overcome this issue and go beyond the sole data fusion of sensor data, this paper proposes an infrastructure that automatically label time series generated by sensors with information extracted from the MES, to achieve enhanced monitoring of the production process. The relevance of the proposed solution and the possibilities opened by its application are stressed with the application to a robotic arm. © 2024 IEEE.

关键词： Anomaly detection

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Neuro-Based Higher Order Sliding Mode control for Perturbed Nonlinear Systems

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Intelligent Automation & Soft Computing 2023年第4期36卷 385-400页

作者： Ahmed M.Elmogy Wael M.Elawady Computer Engineering Department Prince Sattam Bin Abdelaziz UniversityAlkharj24567KSA Department of Computers and Control Engineering Tanta UniversityTanta13457Egypt

One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with *** researchers have been working on developing such type of *** of the most efﬁ-cient techniques employed to develop such controllers is sliding mode control(SMC).However,the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical *** this paper,the drawbacks of low order traditional sliding mode control(FOTSMC)are resolved by presenting a novel adaptive radial basis function neural network–based generalized rth order sliding mode control strategy for nth order uncertain nonlinear *** proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under *** approximation errors are completely considered in the developed *** proposed approach can be used with any order of sliding mode and thus can be generally used with various types of *** global sta-bility of the proposed control approach is proved through Lyapunov stability *** proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling *** simulations results show that the proposed approach provide superior perfor-mance compared with other approaches in the literature.

关键词： SMC nonlinear systems PID Lyapunov stability radial basis function neural networks

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Demonstrating the Potential of Visualization in Education with the Manim Python Library: Examples from Algorithms and Data Structures 47

Demonstrating the Potential of Visualization in Education wi...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Marković, Milorad Kaštelan, Ivan University Of Novi Sad Department Of Computing And Control Engineering Sub-department Of Computer Engineering And Communications Novi Sad Serbia

ISBN: (纸本)9798350382495

Visualization is a powerful tool for learning and teaching complex concepts, especially in the field of computer science. However, creating effective and engaging visualizations can be challenging and time-consuming for educators and students. This paper demonstrates how the Manim Python library, a free and open-source project for creating mathematical animations, can be used to create visualizations that illustrate algorithms and data structures in an undergraduate course. The paper showcases several examples of visualizations created with the Manim library, and explains how they help the students to understand the logic, behavior, and performance of the algorithms and data structures. The paper also discusses the benefits and limitations of the Manim library, and suggests some directions for future research and development. © 2024 IEEE.

关键词： Python

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Routing with ART: Adaptive Routing for P4 Switches With In-Network Decision Trees

Routing with ART: Adaptive Routing for P4 Switches With In-N...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Angi, Antonino Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350351255

Recent advances in Machine Learning (ML) brought several advantages also within computer network management. For programmable data planes, however, it is more challenging to benefit from these advantages, given their limited resource capabilities colliding with the complexity of ML models. In this paper, we propose ART, an attempt to simplify ML-based solutions for routing, so that they can "fit", i.e., be executed, on P4 switches. To provide such model simplification, ART relies on efficient knowledge distillation techniques, converting, in particular, Deep Reinforcement Learning (DRL) models into a simpler Decision Tree (DT). Our evaluation results validate the accuracy of the extracted model and the application of the model logic directly into switches with little impact, paving the way for a more reactive data plane programmability via machine learning integration. © 2024 IEEE.

关键词： Adversarial machine learning

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Cost-aware cloud workflow scheduling using DRL and simulated annealing

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Digital Communications and Networks 2024年第6期10卷 1590-1599页

作者： Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng School of Control and Computer Engineering North China Electric Power UniversityBeijing 100026China School of Mathematics Southwest Jiaotong UniversityChengdu 610032China Department of Computer Science University of ReadingReading RG66AHUK

Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

关键词： Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling Workflow

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration 5

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Mousavi, Mohammad Hossein Moradi, Hassan Rouzbehi, Kumars Razi University Department of Computer and Electrical Engineering Kermanshah Iran University of Seville Department of System Engineering and Automatic Control Seville Spain

ISBN: (纸本)9798350377170

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment. © 2025 IEEE.

关键词： Wind power integration

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AdaLightLog: Enhancing Application Logs Anomaly Detection via Adaptive Federating Learning 19th

AdaLightLog: Enhancing Application Logs Anomaly Detection vi...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Menegatti, Danilo De Santis, Emanuele Felli, Stefano Giuseppi, Alessandro Department of Computer Control and Management Engineering ‘Antonio Ruberti’ Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842597

The role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly produce logs of their activities. Moreover, many CIs, located in different locations or even belonging to different companies, may share similar application software for controlling the CIs themselves. The goal of this work is to use such logs to perform automatic anomaly detection in a federated learning (FL) paradigm, which ensures that no data is exchanged between sites to train the anomaly detection models, but each learning agent learns on its own data, leveraging the knowledge acquired by the other agents. Our proposed approach - AdaLightLog - which implements a modified FL paradigm with adaptive loss functions at local servers side and weighted averaging of local server models, so to differentiate the quality of the different local servers’ models in the global averaging, is tested against state-of-the-art methods and shows an improvement in performance in terms of accuracy, precision and recall with respect to the standard FL implementation (FedAvg). Furthermore, a comparison between different metrics for the adaptive loss functions and the dynamic weights is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Radar style transfer for metric robot localisation on lidar maps

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 139-148页

作者： Huan Yin Yue Wang Jun Wu Rong Xiong College of Control Science and Engineering Zhejiang UniversityHangzhouChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyKowloonHong KongChina

Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关键词： lidar local maps

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Dynamic Mode Decomposition for Individualized Model Predictive control with Application to Type 1 Diabetes 32

Dynamic Mode Decomposition for Individualized Model Predicti...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Becchetti, Valentina Atanasious, Mohab M.H. Menegatti, Danilo Baldisseri, Federico Giuseppi, Alessandro Sapienza University of Rome Department of Computer Control and Management Engineering 'Antonio Ruberti' Italy

ISBN: (纸本)9798350395440

Type 1 diabetes is one of the main issues in current medical research. If not properly managed, it can lead to serious long-term complications such as blindness, stroke, coma and, ultimately, death. In recent years, research endeavors have focused on the development of a fully automated system for glucose control through automated insulin injections, known as Artificial Pancreas, which, however, is not yet robust enough for practical use. Currently, the UVA/Padova simulator provides the most accurate model for effectively representing the insulin-glucose dynamics of individual diabetic patients. As it is highly complex, consisting of 18 strongly nonlinear and time-varying equations, the objective of this work is to linearize it and reduce its dimensionality exploiting Dynamic Mode Decomposition with control. This allows for the synthesis of a controller based on Linear Model Predictive control to be applied to the original model. This is justified by the need to integrate the control algorithm into portable devices with limited computational capabilities. The proposed dimensionality reduction method is validated through in-vitro simulations showing that an accurate representation of the system dynamics is effectively preserved, while conveniently reducing required computational power. © 2024 IEEE.

关键词： Dynamic mode decomposition

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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