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10th International Conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Bertozzi, Nicolò Geraci, Anna Bergamasco, Letizia Ferrera, Enrico Pristeri, Edoardo Pastrone, Claudio Fondazione Links Via Pier Carlo Boggio 61 Turin10138 Italy Department of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy

ISBN: (纸本)9789897587504

This paper presents a novel distributed architecture designed to spawn digital twin solutions to improve energy efficiency in energy-intensive industrial scenarios. By executing user-defined workflows, our platform enables the implementation of real-time monitoring, forecasting, and simulation microservices to enhance decision-making strategies for optimizing industrial processes. Leveraging a stateless centralized orchestration mechanism built around an Apache Kafka-based backbone, the platform ensures scalability, fault tolerance, and efficient handling of heterogeneous data. Key features include intuitive workflow configuration, asynchronous communication for streamlined workflow execution, and API-driven scheduling for dynamic, event-based task management. This platform will be deployed and validated in several energy-intensive industrial scenarios, supporting the management of energy systems of different plants, to prove its effectiveness across a wide range of energy management challenges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Foundries

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MARS: Multi-Agent Deep Reinforcement Learning for Complex Environment Exploration

MARS: Multi-Agent Deep Reinforcement Learning for Complex En...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Francesco Gervino Andrea Eirale Marcello Chiaberge Alessio Sacco Guido Marchetto Claudio Casetti Department of Control and Computer Engineering Politecnico di Torino Italy Department of Electronics and Telecommunications Politecnico di Torino Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Autonomous exploration of complex, unknown environments is a cutting-edge task not entirely solved by the scientific community. When an agent needs to explore a maze without any a priori information about the environment, the lack of proper destinations and explicit task objectives make traditional navigation policies inappropriate. While the literature presents some sporadic deterministic systems able to face the tasks, learning approaches still need an adequate investigation which could prove them to be more suitable and versatile for this purpose. In this paper, we present MARS, a path planner that exploits swarms of robots to optimize the exploration of complex unknown environments, such as mazes. To make the solution scalable, the proposed method exploits two cooperating modules: local and global planners. The local planner is modeled as a Markov Decision Process (MDP) and trained as a Reinforcement Learning (RL) multi-agent system. Each agent has access to image representations of a section of the global map, always centered in the robot reference frame, and decides the next navigation goal to complete the local exploration. The global planner is a deterministic system that recovers the navigation when a local solution is unavailable. The robots share the explored section with peers when they meet in a rendez-vous. We compared our approach to a single deterministic agent, a single RL agent and a close-to-optimal deterministic approach which deploys five greedy agents. The simulation results demonstrate MARS' efficiency, reaching near-optimal levels in significantly less time.

关键词： Mars Navigation Simulation Markov decision processes Image representation Deep reinforcement learning Robots Faces Multi-agent systems

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

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engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

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Early Functional Safety and PPA Evaluation of Digital Designs

Early Functional Safety and PPA Evaluation of Digital Design...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Bartolomucci, Michelangelo Kingston, David Cupaiuolo, Teo Nardi, Alessandra Cantoro, Riccardo Politecnico di Torino Department of Control and Computer Engineering Italy Synopsys Inc. United States

ISBN: (纸本)9783982674100

The use of semiconductor devices in safety-critical scenarios is increasing in both quantity and complexity. This paper presents a novel approach to support safety requirements from RTL exploration through to implementation, with the aid of a Safety Specification Format (SSF), thereby minimizing costly development iterations and reducing the Time-To-Market. An assessment of the results is given for the CV32E40P open source RISC-V processor. © 2025 EDAA.

关键词： Electronic design automation

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

Lyapunov-Inspired Deep Reinforcement Learning for Robot Navi...

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Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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Achieving near-optimal convergence for distributed minimax optimization with adaptive stepsizes 24

Achieving near-optimal convergence for distributed minimax o...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yan Huang Xiang Li Yipeng Shen Niao He Jinming Xu College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9798331314385

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of Õ(ε-(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results.

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Customer Attitudes Toward Circular Economy in the E-Waste Context: A Survey Assessing Sustainable Consumption Dynamics

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IEEE engineering Management Review 2023年第4期51卷 28-45页

作者： Jabbour, Charbel Jose Chiappetta Colasante, Annarita D'Adamo, Idiano Rosa, Paolo Sassanelli, Claudio NEOMA Business School Department Information Systems Supply Chain and Decision Making Mont-Saint-AignanCedex 76130 France Unitelma Sapienza-University of Rome Department of Law and Economics Rome00161 Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Politecnico di Milano Department of Management Economics and Industrial Engineering Milan20133 Italy Politecnico di Bari Department of Mechanics Mathematics and Management Bari70125 Italy

Waste from electrical and electronic equipment (WEEE) is a relevant concern in Europe. Even if relevant improvements have been made in terms of material recovery technologies and policy measures, there remains a significant gap between the amount of WEEE generated versus collected in the EU. This study is based on an online survey whose aim was to assess the attitude of students (i.e., individuals who dispose with a higher frequency electronic product) to buy/substitute three iconic product types (i.e., smartphones, notebooks, televisions), and their openness toward circular practices (i.e., recycling, reconditioning, reuse). Results have shown that: a) new products represent the greatest market share, b) reliability and quality were the most significant factors influencing the purchase decision in all product types, and c) circular premium was not recognized by students for any product type. From a social perspective, a responsible consumption pattern emerged, though a sustainability bias was evident with respect to products' residual value. From an economic perspective, these findings may inform companies about the orientation of young consumers and provide key insights for new business strategies. Results suggest that a proper waste management strategy also requires a behavioral change of people who should adopt it. © 1973-2011 IEEE.

关键词： Electronic Waste

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Security Flaws From Time-Varying Encoding in High Speed MDI QKD

Security Flaws From Time-Varying Encoding in High Speed MDI ...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Gnanapandithan, Amita Lo, Hoi-Kwong Qian, Li Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Physics University of Toronto Canada Center for Quantum Information and Quantum Control University of Toronto Canada Department of Physics University of Hong Kong Hong Kong

ISBN: (纸本)9781957171258

Electro-optic phase modulators are commonly used for polarization and phase encoding in quantum key distribution. Here, a novel state preparation flaw which arises during high speed electro-optic phase modulation is identified and characterized. The impact of this state preparation flaw on the secure key rate is quantified. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Phase modulation

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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian 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 Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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