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arXiv 2022年

作者： Pan, Xiao Chen, Boli Dai, Li Timotheou, Stelios Evangelou, Simos A. The Dept. of Electrical and Electronic Engineering Imperial College London London United Kingdom The Dept. of Electronic and Electrical Engineering University College London London United Kingdom The School of Automation Beijing Institute of Technology Beijing China The Dept. of Electrical and Computer Engineering The KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Cyprus

The development of connected and automated vehicles is the key to improving urban mobility safety and efficiency. This paper focuses on cooperative vehicle management at a signal-free intersection with consideration of vehicle modeling uncertainties and sensor measurement disturbances. The problem is approached by a hierarchical robust control strategy in a decentralized traffic coordination framework where optimal control and tube-based robust model predictive control methods are designed to hierarchically solve the optimal crossing order and the velocity trajectories of a group of CAVs in terms of energy consumption and throughput. To capture the energy consumption of each vehicle, their powertrain system is modeled in line with an electric drive system. With a suitable relaxation and spatial modeling approach, the optimization problems in the proposed strategy can be formulated as convex second-order cone programs, which provide a unique and computationally efficient solution. A rigorous proof of the equivalence between the convexified and the original problems is also provided. Simulation results illustrate the effectiveness and robustness of the proposed strategy and reveal the impact of traffic density on the control solution. The study of the Pareto optimal solutions for the energy-time objective shows that a minor reduction in journey time can considerably reduce energy consumption, which emphasizes the necessity of optimizing their trade-off. Finally, the numerical comparisons carried out for different prediction horizons and sampling intervals provide insight into the control design. Copyright © 2022, The Authors. All rights reserved.

关键词： Robust control

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Graph neural networks for decentralized path planning 19

Graph neural networks for decentralized path planning

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19th International Conference on Autonomous Agents and Multiagent systems, AAMAS 2020

作者： Li, Qingbiao Gama, Fernando Ribeiro, Alejandro Prorok, Amanda Dept. of Computer Science and Technology University of Cambridge United Kingdom Dept. of Electrical and Systems Engineering University of Pennsylvania United States

ISBN: (纸本)9781450375184

We propose a combined model that automatically synthesizes local communication and decision-making policies for agents navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among agents. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations involving teams of agents in cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model's capability to generalize to previously unseen cases (involving larger environments and larger agent teams). © 2020 International Foundation for Autonomous Agents and Multiagent systems (IFAAMAS). All rights reserved.

关键词： Motion planning

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

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IFAC-PapersOnLine 2022年第35期55卷 37-42页

作者： Matthis H. de Lange Chris Verhoek Valentin Preda Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600 MB The Netherlands ESTEC European Space Agency Noordwijk 2200 AG The Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest 1111 Hungary

Obtaining models that can be used for flight control is of outmost importance to ensure reliable guidance and navigation of spacecrafts, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert an existing, high-fidelity nonlinear model of the atmospheric flight dynamics of a GPRV to a Linear Parameter-Varying (LPV) form that enables high-performance navigation control design. Application of existing systematic conversion methods for such complicated nonlinear models often result in complex LPV representations, which are not suitable for controller synthesis. We apply and compare state-of-the-art conversion techniques on the GPRV model, including learning based approaches, to optimize the complexity and conservatism of the resulting LPV embedding. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is efficiently chosen.

关键词： linear parameter-varying systems scheduling reduction spacecraft modeling aerodynamics principle component analysis deep neural networks

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Federated Learning for the Metaverse: A Short Survey

Federated Learning for the Metaverse: A Short Survey

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Gokul Yenduri Dasaradharami Reddy K Gautam Srivastava Supriya Y Ramalingam M Thippa Reddy Gadekallu Feras M. Awaysheh Vellore Instituite of Technology Vellore Tamil Nadu India Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Delta Research Center Institute of Computer Science Tartu University Tartu Estonia

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises transformative experiences. Its potential is tempered by data collection, confidentiality, and privacy concerns. In tandem with AI-enabled technologies, edge computing can provide a practical solution to overcoming many of these challenges. This paper argues that combining AI-enabled technologies with edge computing-specifically via federated learning (FL) can address these challenges. FL, as a privacy-centric distributed machine learning (ML) approach, enables knowledge sharing among Metaverse clients without compromising user data. However, while FL posits potential resolutions for the Metaverse, a discernible lacuna remains in the comprehensive study of its overarching consequences. The literature misses an extensive study of adopting these new deployment architectures, giving it significant research impact. This paper bridges this knowledge gap, exploring the symbiosis between the Metaverse and FL. We first introduce the foundational concepts of both domains and detail enabling technologies such as digital twins, the Internet of Things, brain-computer interfaces, blockchain, and extended reality. Next, we delve into practical applications of FL within the metaverse, spanning sectors like healthcare, education, e-commerce, gaming, and the military. Finally, the paper highlights the key challenges and future directions for integrating FL within the metaverse ecosystem.

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Enhancement of Near-infrared Light Absorption by Nanoimprinted Light Trapping Structure Implemented into Si Heterojunction Solar Cells

Enhancement of Near-infrared Light Absorption by Nanoimprint...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Yasuyoshi Kurokawa Yoshiko Iseki Kazuhiro Gotoh Satoru Miyamoto Ryo Ozaki Kyotaro Nakamura Yoshio Ohshita Noritaka Usami Dept. Materials Process Engineering Nagoya University Nagoya Japan Dept. Electrical and Information Engineering Niigata University Niigata Japan Intereisciplinaty Research Center for Carbon-Neutral Technologies Niigata University Niigata Japan Dept. Advanced Science and Technology Toyota Technological Institute Nagoya Japan Institute of Materials and Systems for Sustainability Nagoya University Nagoya Japan Institutes of Innovation for Future Society Nagoya University Nagoya Japan

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

Lightweight and flexible PV modules are required to apply PV to buildings and vehicles. One way to realize lightweight and flexible PV is to make the silicon substrate thinner. However, thinner substrates result in lower near-infrared light absorption. Although light trapping structure (LTS) can increase light absorption, conventional alkali-etched pyramid textures are not specialized for near-infrared light and are insufficient to improve near-infrared light absorption. Therefore, in this study, a honeycomb LTS was implemented into the rear side of Si heterojunction (SHJ) solar cells by a nanoimprinting method, which enables us to prepare submicron-sized LTS on solar cells over a large area. As a result, in the near-infrared range of more than 1000 nm, the reflectance of the sample with honeycomb LTS was reduced compared to that without the LTS. The reflectance at 1050 nm was reduced from 59.2 to 41.5%. The external quantum efficiency (EQE) in the near-infrared range of more than 1000 nm was also increased by the LTS, leading to the improvement of the short-circuit current density ( $Jsc$ ) by 0.02 mA/cm 2 .

关键词： Reflectivity Photovoltaic systems Absorption Short-circuit currents Heterojunctions Light trapping Silicon Etching Resins Substrates

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USDOT Tier-1 University Transportation center for Advancing Cybersecurity Research and Education

USDOT Tier-1 University Transportation Center for Advancing ...

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Mobility, Operations, Services and Technologies (MOST), IEEE International Conference on

作者： Yunpeng Jack Zhang Kailai Wang Lingguang Song Amaury Lendasse Houbing Herbert Song Zhu Han Rakesh Verma Arlei Silva Carlos Rubio-Medrano Zhixia Richard Li Guohui Zhang Dept. of Information Science Technology University of Houston Houston TX USA Dept. of Construction Management University of Houston Houston TX USA Dept. of Information Systems University of Maryland Baltimore County Baltimore MD USA Dept. of Electrical & Computer Engineering University of Houston Houston TX USA Dept. of Computer Science University of Houston Houston TX USA Dept. of Computer Science Rice University Houston TX USA Dept. of Computer Science Texas A&M University-Corpus Christi Corpus Christi TX USA Dept. of Civil and Architectural Engineering and Construction Management University of Cincinnati Cincinnati OH USA Dept. Civil and Environmental Engineering University of Hawai’i at Mānoa Honolulu HI USA

The Transportation Cybersecurity center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Mohamed Elsersy Ahmed Sherif Ahmad Abdel-Aliem Imam Mohammad M. R. Khan Mamun Kasem Khalil Mohamed Haitham Computer and Information Systems Dept. Higher Colleges of Technology Abu Dhabi UAE School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA College of Osteopathic Medicine William Carey University Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA Electrical and Computer Engineering Department University of Mississippi MS USA University of Prince Edward Island (UPEI) Cairo Egypt

Alzheimer’s disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection.

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Mitigating Epilepsy by Stabilizing Linear Fractional-Order systems

Mitigating Epilepsy by Stabilizing Linear Fractional-Order S...

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American Control Conference (ACC)

作者： Emily A. Reed Guilherme Ramos Paul Bogdan Sérgio Pequito Ming Hsieh Electrical and Computer Engineering Department University of Southern California USA Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems.

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Enabling Smart Agriculture in Nigeria: Application of Digital-Twin technology 1

Enabling Smart Agriculture in Nigeria: Application of Digita...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Elijah, Olakunle Rahim, Sharul Kamal A. Emmanuel, Abioye Abiodun Salihu, Yahaya Otuoze Usman, Zubair Gatugel Jimoh, Adinoyi Momoh Wireless Communication Center School of Electrical Engineering University of Technology Malaysia Johor Bahru Malaysia School of Electrical Engineering University of Technology Malaysia Control and Mechatronics Engr. Dept. Johor Bahru Malaysia Kaduna Polytechnic Computer Engineering Department Kaduna Nigeria Saudi Telecommunication Company Riyadh Saudi Arabia

ISBN: (纸本)9781665434935

The digital twin (DT) is an emerging technology and one of the industry 4.0 technologies. The research in the application of DT in agriculture is currently attracting research interest. In this paper, we provide an overview of DT and discuss possible areas in which it can enable smart agriculture in Nigeria. The benefits and challenges of deploying the DT are also presented. Finally, methods that can be adopted in the application of DT towards solving the problems facing Nigeria's agriculture sector are proposed. © 2021 IEEE.

关键词： Agriculture

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