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International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

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Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Communication Network Layer State Estimation Measurement Mod...

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Innovative Smart Grid Technologies (ISGT)

作者： Reynold Mathieu Sharon Boamah Austin Cooper Dennis Agnew Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Pacific Northwest National Laboratory Richland WA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in the generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing an enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects of th

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Explainable agency: human preferences for simple or complex explanations

arXiv

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arXiv 2024年

作者： Blom, Michelle Singh, Ronal Miller, Tim Sonenberg, Liz Trentelman, Kerry Saulwick, Adam School of Computing and Information Systems The University of Melbourne Melbourne Australia Defence Science and Technology Group Adelaide Australia CSIRO Data61 Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of ‘simple vs. complex’ becomes critical when an artificial agent seeks to explain its decisions or predictions to humans. We present a model for abstracting causal reasoning chains for the purpose of explanation. This model uses a set of rules to progressively abstract different types of causal information in causal proof traces. We perform online studies using 123 Amazon MTurk participants and with five industry experts over two domains: maritime patrol and weather prediction. We found participants’ satisfaction with generated explanations was based on the consistency of relationships among the causes (coherence) that explain an event;and that the important question is not whether people prefer simple or complex explanations, but what types of causal information are relevant to individuals in specific contexts. Copyright © 2024, The Authors. All rights reserved.

关键词： Abstracting

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Microgrid Frequency Stability: A Proactive Scheme Based on Dynamic Predictions

Microgrid Frequency Stability: A Proactive Scheme Based on D...

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Innovative Smart Grid Technologies (ISGT)

作者： Gian Paramo Arturo Bretas Sean Meyn Department of Electrical and Computer Engineering University of Florida Gainesville FL G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Distributed Systems Group Pacific Northwest National Laboratory Richland USA

The dynamic nature of microgrids introduces challenges in the context of frequency stability. This work presents a framework where the future state of microgrid frequency is predicted and corrective actions are optimized. Predictions are generated through Bayesian filters leveraging synchronized data acquired via PMUs. Taking a proactive approach makes it possible to optimize corrective actions considering dynamic system conditions. Testing is conducted via Matlab simulations. The performance of the solution presented in this work is compared to traditional load-shedding schemes and to predictive solutions found in literature. The results indicate that the proposed framework outperforms both. Some of the advantages of this framework include a reduction in amount of load dropped during compensation, the use of adaptive parameters which eliminates the need to simulate contingency conditions, and dynamic uncertainty quantification provided the particle filter.

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Lab-on-CMOS Resonant Micro-Calorimeter

Lab-on-CMOS Resonant Micro-Calorimeter

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Rafel Perelló-Roig Jaume Verd Sebastià Bota Toshikazu Nishida Jaume Segura Electronic Systems Group (GSE-UIB) the University of the Balearic Islands Palma Spain Health Research Institute of the Balearic Islands Palma Spain Department of Electrical and Computer Engineering University of Florida USA

Micro-calorimetry targeting sub-nanowatt resolution is fundamental for high-resolution thermal applications in the biological and chemical domains. Also, CMOS compatibility is crucial for system-on-chip devices in line with current technological trends, as well as point-of-care portability, low-cost batch production, ease of fabrication, and miniaturization represent significant strengths. However, compatibilization issues like size disparity, bonding strength, lack of standardization and electrical-fluidic ports mixture, faced when integrating a CMOS die within a microfluidics platform represent major challenges up to date. In this work, we demonstrate a Lab-on-CMOS micro-calorimeter combining a CMOS-MEMS 4-MHz CC-Beam as resonant temperature sensing element and a PDMS-based microfluidics platform for micro-channel routing to achieve sub-mK temperature resolution and ultra-fast response in a 500-pL reaction chamber.

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Safe Reinforcement Learning via Confidence-Based Filters

arXiv

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arXiv 2022年

作者： Curi, Sebastian Lederer, Armin Hirche, Sandra Krause, Andreas Learning & Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Germany

— Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach for certifying state safety constraints for nominal policies learnt via standard RL techniques, based on probabilistic dynamics models. Our approach is based on a reformulation of state constraints in terms of cost functions, reducing safety verification to a standard RL task. By exploiting the concept of hallucinating inputs, we extend this formulation to determine a "backup" policy which is safe for the unknown system with high probability. Finally, the nominal policy is minimally adjusted at every time step during a roll-out towards the backup policy, such that safe recovery can be guaranteed afterwards. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach. © 2022, CC BY.

关键词： Reinforcement learning

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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COLLABORATIVE INTELLIGENCE FOR VEHICULAR INTERNET OF THINGS

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China Communications 2021年第7期18卷 I0003-I0006页

作者： Celimuge Wu Kok-Lim Alvin Yau Carlos Tavares Calafate Lei Zhong University of Electro-Communications since IEEE TCGCC Special Interest Group on Green Internet IEEE TCBD Special Interest Group on Big Data with Computational Intelligence Department of Computing and Information Systems Sunway University Department of Computer Engineering at the Technical University of Valencia(UPV)in Spain Connected Advanced Development Division at Toyota Motor Corporation

Future vehicular Internet-of-Things(IoT)systems feature a large number of devices and multi-access environments where different types of communication,computing,and storage resources must be efficiently *** the same time,novel services such as cooperative autonomous driving and intelligent transportation systems(ITS),that demand unprecedented high accuracy,ultra-low latency,and large bandwidth,are emerging.

关键词： INTERNET services utilized

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Implementational Aspects for the Characterization of Low-Inertia in Power systems

Implementational Aspects for the Characterization of Low-Ine...

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Annual North-American Power Symposium

作者： M. D. Baquedano-Aguilar Nader Aljohani Sean P. Meyn Arturo Bretas Department of Electrical & Computer Engineering University of Florida Gainesville Florida Pacific Northwest National Laboratory Distributed Systems Group Richland WA

ISBN: (纸本)9781665499224

The replacement of conventional generating units with non-conventional renewable energy resources impacts the reliability of power systems. With high integration of renewable-based generation resources, there is a reduction in total system inertia, which could lead to systems with less time to react for frequency restoration in case of unexpected unit failures. Though several power system testbeds are available for studying and analysing different stability phenomena, none are suitable for characterizing, or analyzing low-inertia conditions. In this paper, a power system simulation environment is introduced for investigating control architectures for power grids, with special attention to the impact of high penetration of inverter-based generation resources (IBRs). The testbed is applied to the WSCC 9- bus test system, modified so that a range of IBR penetration levels can be analyzed (from zero to 80%). The testbed is suitable for studying both fast and slow electromechanical phenomena. A conclusion from numerical experiments is that high levels of IBRs do not necessarily result in low-inertia. This is due in part to the layers of control in the grid already in place to avoid frequency excursions.

关键词： Renewable energy sources Time-frequency analysis System dynamics Simulation Power system stability Power grids Stability analysis

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Deep Reinforcement Learning-Based Resource Allocation for Secure RIS-aided UAV Communication

Deep Reinforcement Learning-Based Resource Allocation for Se...

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IEEE Conference on Vehicular Technology (VTC)

作者： Amjad Iqbal Ala'a Al-Habashna Gabriel Wainer Faouzi Bouali Gary Boudreau Khan Wali Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Centre for Future Transport & Cities Coventry University Coventry United Kingdom Ericsson Canada Kanata ON Canada Agricultural Biosystems Engineering Group Wageningen University & Research The Netherlands

We investigate the use of reconfigurable intelligent surfaces (RISs) in wireless networks to maximize the sum secrecy rate (i.e., the sum maximum rate that can be communicated under perfect secrecy). Specifically, we focus on a network that utilizes RIS-assisted unmanned aerial vehicles (UAVs) under imperfect channel state information (CSI). Our objective is to maximize the sum secrecy rate while dealing with the presence of multiple eavesdroppers. To achieve this, we jointly optimize the active (UAV) and passive (RIS) beamforming together with the UAV's trajectories. The formulated problem is non-convex due to the coupling of CSI with the maneuverability of the UAV. To overcome this challenge, we propose a policy-based deep reinforcement learning (DRL) approach that solves the non-convex optimization problem in a centralized fashion. Finally, simulation results show that our proposed approach significantly improves average sum secrecy rates over conventional approaches.

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