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14th International Conference on Measurement, MEASUREMENT 2023

作者： Otomanski, Przemyslaw Pawlowski, Eligiusz Szlachta, Anna Poznan University of Technology Institute of Electrical Engineering and Electronics Piotrowo street 3A Poznań60-965 Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Nadbystrzycka street 38A Lublin20-618 Poland Rzeszow University of Technology Department of Metrology and Measurement Systems W. Pola street 2 Rzeszow35-959 Poland

ISBN: (纸本)9788097262976

The paper discusses the topic of determining the current-voltage characteristics of two-terminal passive components. A virtual measurement instrument created in the LabVIEW environment using the NI USB-6211 data acquisition device is presented, as well as the results of tests performed for example elements. The described approach can also be applied to other measurement situations, such as for studying the self-heating effect of RTD. © 2023 institute of Measurement Science, Slovak Academy o.

关键词： Current voltage characteristics

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

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European Control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Moving Target Defense Approach for Secure Relay Selection in Vehicular Networks

TechRxiv

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TechRxiv 2023年

作者： Ghourab, Esraa M. Naser, Shimaa Muhaidat, Sami Bariah, Lina Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Damiani, Ernesto The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University United Arab Emirates The Department of Systems and Computer Engineering Carleton University Canada Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates Center Department of Electrical Engineering and Computer Science Khalifa University United Arab Emirates The Department of Electrical Engineering Tampere University Finland

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task, due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. The moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions, based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of the network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks has several limitations, such as they are not well-suited for highly dynamic environments;they require high synchronization modules that are resource-intensive and difficult to implement;and finally, they rely heavily on the attack-defense models, which may not always be accurate or comprehensive to use. In this paper, we propose an intelligent spatiotemporal diversification MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. Specifically, we design benign random data injection patterns to meet the security and reliability requirements of the vehicular network. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. In order to optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are c

关键词： Iterative methods

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiang, Senwei Ye, Minxiang Zhu, Shiqiang Gu, Jason Xie, Anhuan Men, Zehua Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou310000 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers;an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude © 2022 IEEE.

关键词： Aircraft landing

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE International Conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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Optimal Placement of Voltage Regulator and Capacitor for Distribution System

Optimal Placement of Voltage Regulator and Capacitor for Dis...

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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Rajalakshmi, M. Addisu, Molla Jeyakkannan, N. Renuka Devi, G. Karthik, C. Jyothi Engineering College Department of Mechatronics Thrissur India Woldia University Department of Electrical and Computer Engineering Ethiopia Manakula Vinayagar Institute of Technology Deprtment of Robotics and Automation Puduchery India

ISBN: (纸本)9781665439862

The radial distribution system (RDS) is the ultimate link between the high-voltage transmission system (HVT) and the user in most electrical systems in Ethiopia. The Distribution System (DS) is divided into different areas for proper operation and supply of electricity demand. The study in this paper applies specifically to the Gondar feeder, which consists of 60 nodes with 3,413 MW of total capacity, in power distribution lines. For the modulation of voltages and DS node power loss indices, the fuzzy membership functions are used. A fluid expert system (FES) with a collection of heuristic legislation will determine the voltage regulator and the capacitor suitability index. The proposed method discusses and compares the effectiveness and results obtained from the voltage control system and the optimal placement of capacitors. © 2021 IEEE.

关键词： Voltage regulators

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Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

Secured Cluster-Based Electricity Theft Detectors Against Bl...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation

arXiv

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arXiv 2024年

作者： Xie, Peijia Xia, Bingyi Hu, Anjun Zhao, Ziqi Meng, Lingxiao Sun, Zhirui Gao, Xuheng Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real-world scenarios, where it successfully executes multiple-trolley collection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Generating Realistic Sound with Prosthetic Hand: A Reinforcement Learning Approach

Generating Realistic Sound with Prosthetic Hand: A Reinforce...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Taemoon Jeong Sankalp Yamsani Jooyoung Hong Kyungseo Park Joohyung Kim Sungjoon Choi Department of Artificial Intelligence Korea University Seoul Korea Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign IL USA Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DG-IST) Daegu South Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this study, we tackle the complex task of enabling prosthetic hands to accurately reproduce sounds, a crucial aspect for distinguishing between different materials through auditory feedback. Sound identification, such as discerning a drywall tap from that on a brick wall, significantly enhances the functionality and user experience of prosthetic devices. However, achieving this level of auditory feedback in prosthetic hands poses considerable challenges. We utilize reinforcement learning (RL) techniques to train prosthetic hands in emulating human-like sound characteristics, focusing on key auditory signals like amplitude and onset timing. Our approach integrates a detailed analysis of these sound attributes to direct the prosthetic hand’s movements for the sound generation that mimics natural human actions. We developed a tailored reward function incorporating amplitude, onset strength, and timing criteria to ensure the prosthetic hand’s movements align closely with the intended human-like sound output.

关键词： MIMICs Focusing Reinforcement learning User experience Timing Object recognition engineering in medicine and biology Prosthetic hand

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

A Data-Informed Analysis of Scalable Supervision for Safety ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Cameron Hickert Zhongxia Yan Cathy Wu Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a ‘scalable supervision’ concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs — which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied — and (ii) aggregation across larger regions.

关键词： Road transportation Scalability System performance Predictive models Safety Reliability Vehicle dynamics Autonomous vehicles Queueing analysis Load modeling

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