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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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Effective Spline-GMPPT Approach Performance for Photovoltaic systems under Uniform Irradiance and Partial Shading Conditions

Effective Spline-GMPPT Approach Performance for Photovoltaic...

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Energy Transition and Security (ICETS), International Conference on

作者： Saidi Ahmed Abdelouahed Touhami Draoui Abdelghani Meziane Mohamed Amine Harrouz Abdelkader Department of Electrical Engineering Laboratory of Sustainable Development and Computer Science Ahmed Draia University Algeria Department of Electrical Engineering Laboratory of Smart Grid and Renewable Energy University of Tahri Mohamed Bechar Algeria Laboratory of Environmental and Energy Systems (LSEE) University Center Ali Kafi Tindouf Algeria NAAMA University Center Naama Algeria

In the literature, there are several criteria techniques classified to achieve the point of maximum power tracking (MPPT) in photovoltaic (PV) power systems, such as accuracy, speed, and simplicity. These criteria are often trade-offs; in this case, the higher accuracy generally achieved at the expense of two other criteria: speed and *** contribution proposes a new technique based on the Spline-Global-MPPT approach to provide its reliability in multiple criteria as an accurate, fast, and simple technique to find the MPP of PV power generation systems under two cases, such as uniform irradiance and partial shading conditions (PSCs), in the total distorted characteristics of the PV string. A cubic spline interpolation approach has proposed in this method, which defines from a few simple points an approximate *** localize the GMPP under standard (uniform) irradiance conditions, there are a huge number of interpolation-based approaches proposed in the literature. Unfortunately, this interpolation approach became incapable of detecting the point of the global maximum power (GMPP) under PSCs. The MPP of the system predicted by this new Spline-GMPPT method using a limited sample set of current and voltage, and it maintains this position as long as the external conditions stay unchanged. In the last part, the simulation results prove the reliability and robustness of the proposed approach.

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Symbiotic Control of Uncertain Dynamical systems*

Symbiotic Control of Uncertain Dynamical Systems*

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IEEE Conference on Decision and Control

作者： Tansel Yucelen Selahattin Burak Sarsılmaz Emre Yildirim Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS http://lacis.eng.usf.edu/) University of South Florida Tampa Florida United States of America Department of Electrical and Computer Engineering Utah State University Logan Utah United States of America

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider both the fixed-gain control and adaptive learning architectures to suppress the effects of uncertainties. We note that fixed-gain control provides more predictable closed-loop system behavior, but it comes at the cost of knowing uncertainty bounds. On the other hand, adaptive learning removes the requirement of this knowledge at the expense of less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that integrates the advantages of both fixed-gain control and adaptive learning architectures. In particular, the proposed framework utilizes both control architectures to suppress the negative effects of uncertainties with more predictable closed-loop system behavior and without the knowledge of uncertainty bounds. Both parametric and nonparametric uncertainties are considered, where we use neural networks to approximate the unknown uncertainty basis for the latter case. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

关键词： Symbiosis Hands Adaptive learning Uncertainty Upper bound Costs Neurons Closed loop systems Dynamical systems Biological neural networks

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Operational strategies for a photovoltaic-thermal system integrated with an electric air-to-water heat pump

Operational strategies for a photovoltaic-thermal system int...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Giuseppe Russo Andreas V. Olympios Chandan Pandey Annamaria Buonomano Christos N. Markides Department of Chemical Engineering Clean Energy Processes (CEP) Laboratory and Sargent Centre for Process Systems Engineering Imperial College London UK Department of Industrial Engineering University of Naples Federico II Naples Italy Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

In this study, operational strategies are investigated for an air-based photovoltaic-thermal (PV-T) system integrated with a domestic air-to-water heat pump. The PV-T system is used to simultaneously generate electricity and heat ambient air, which serves as the heat source for the heat pump. By utilising a variable-speed fan to regulate airflow, the PV-T system outlet temperature is adjusted, and the focus is on how different outlet temperatures impact the PVT system electrical and thermal efficiency, heat pump performance, and overall electricity requirements for heating. The performance of the integrated system is assessed for a case study of a household application in north Italy. The optimal PV-T outlet temperature is found to be between 30 – 35 °C, resulting in only 5% less electricity production than an equivalent PV system with equal nominal electricity capacity, while reducing overall electricity required for heating by over 15% compared to PV-integrated heat pumps.

关键词： Photovoltaic systems Fans Temperature Heat pumps Electricity Buildings Water heating Production Temperature control Resistance heating

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

arXiv

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arXiv 2024年

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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Multiple Redundant Weighted Cluster Path Scheduling Scheme for Damaged Sensor-Based Wireless Sensor Network

Multiple Redundant Weighted Cluster Path Scheduling Scheme f...

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IEEE Future Networks World Forum (FNWF)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Houbing Song Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Department of Electrical & Electronic Engineering Science University of Cyprus Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Information Systems University of Maryland Baltimore Country Baltimore MD USA

In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.

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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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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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