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arXiv 2022年

作者： Stanojev, Ognjen Guo, Yi Aristidou, Petros Hug, Gabriela EEH Power Systems Laboratory ETH Zurich Switzerland Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus Cyprus

The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators. Since the principal providers of ancillary services are being phased out, new flexibility and reserve providers are needed. The proliferation of Distributed Energy Resources (DERs) in modern distribution networks has opened new possibilities for distribution system operators, enabling them to fill the market gap by harnessing the DER flexibility. This paper introduces a novel centralized MPC-based controller that enables the concurrent provision of voltage support, primary and secondary frequency control by adjusting the setpoints of a heterogeneous group of DERs in active distribution grids. The input-multirate control framework is used to accommodate the distinct timescales and provision requirements of each ancillary service and to ensure that the available resources are properly allocated. Furthermore, an efficient way for incorporating network constraints in the formulation is proposed, where network decomposition is applied to a linear power flow formulation together with network reduction. In addition, different timescale dynamics of the employed DERs and their capability curves are included. The performance of the proposed controller is evaluated on several case studies via dynamic simulations of the IEEE 33-bus system. © 2022, CC BY.

关键词： Model predictive control

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Forecasting Hierarchical Time Series Using Non-Linear Mappings

SSRN

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SSRN 2024年

作者： Wickramasuriya, Shanika L. Bandara, Kasun Hewamalage, Hansika Perera, Maneesha Department of Econometrics and Business Statistics Monash University Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia School of Computer Science & Engineering University of New South Wales Sydney Australia Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia

Forecast reconciliation is a procedure for forecasting multivariate time series with linear aggregation constraints to ensure that forecasts satisfy the same set of constraints. Most state-of-the-art reconciliation methods reconcile the forecasts using linear mappings to project the base forecasts onto a coherent subspace. They also use assumptions such as the forecasts are unbiased and residuals are jointly covariance stationary. In this study, we propose a non-linear forecast reconciliation approach by using machine learning methods, relaxing the assumptions of traditional linear reconciliation approaches. We introduce a novel loss function incorporating non-linear mappings to obtain a set of coherent forecasts from the individual base forecasts. To obtain the weights of the non-linear mapping between the base forecasts, we train a feed-forward neural network with the proposed loss function using transformed fitted values of the base models as inputs to the network. We evaluate the proposed methodology against state-of-the-art linear reconciliation approaches on multiple benchmark datasets. The results indicate that the proposed method works well when there is a reasonable number of observations in comparison to the number of bottom-level and non-bottom level series. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Reducing Network Distance Measurement Overhead: A Tensor Completion Solution With a New Minimum Sampling Bound

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IEEE/ACM Transactions on Networking 2024年

作者： Tian, Jiazheng Xie, Kun Wang, Xin Wen, Jigang Xie, Gaogang Cao, Jiannong Liang, Wei Li, Kenli Hunan University College of Computer Science and Electronics Engineering Changsha410082 China Ministry of Education Key Laboratory of 'Fusion Computing of Supercomputing and Artificial Intelligence' Changsha410082 China State of New York University at Stony Brook Department of Electrical and Computer Engineering Stony BrookNY11794 United States Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411199 China Chinese Academy of Sciences Computer Network Information Center Beijing100080 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measurement costs by utilizing a small amount of measured data to estimate the entire network's distance data. Unfortunately, current tensor completion methods still suffer from issues such as complex sample selection, high measurement overhead, slow recovery, and low inference accuracy. To address the aforementioned challenges, we present an online network-wide distance measurement scheme. In this approach, continuous distance data are structured into sliding-window-based tensors. Our method incorporates a lightweight sample selection algorithm with a lowest sampling bound and a rapid, accurate unmeasured data inference algorithm. We have conducted extensive experiments using four real network distance datasets and two citywide crowd flow datasets. The empirical evaluations demonstrate the effectiveness of our approach, particularly in reducing measurement costs and enhancing data recovery accuracy. © 1993-2012 IEEE.

关键词： Distance measurement

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Optimal Sizing of Isolated Renewable Power systems with Ammonia Synthesis: Model and Solution Approach

arXiv

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arXiv 2023年

作者： Yu, Zhipeng Lin, Jin Liu, Feng Li, Jiarong Zhao, Yuxuan Song, Yonghua The State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Sichuan Energy Internet Reasearch Institute Tsinghua University Chengdu610213 China The Department of Electrical and Computer Engineering University of Macau 999078 China The Department of Electrical Engineering Tsinghua University Beijing100087 China

Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multiyear uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multiyear uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3610 RMB/t) in long-run simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Ammonia

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot systems in Power Line Inspection Tasks

arXiv

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arXiv 2023年

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal The Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Systems Universidade Federal da Paraiba Brazil The GRVC Robotics Laboratory University of Seville Spain

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

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Quality of Service (QoS)-driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

arXiv

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

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

引用

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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Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence

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IEEE Communications Surveys and Tutorials 2025年

作者： Zeng, Liekang Ye, Shengyuan Chen, Xu Zhang, Xiaoxi Ren, Ju Tang, Jian Yang, Yang Shen, Xuemin Sherman Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangzhou511453 China Ministry of Education School of Computer Science and Engineering Sun Yat-sen University The Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Midea Group Shanghai201702 China Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services. Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them - GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI. © 1998-2012 IEEE.

关键词： Contrastive Learning

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Intelligent Autonomous-Robot Control for Medical Applications

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computers, Materials & Continua 2021年第8期68卷 2189-2203页

作者： Rihem Farkh Haykel Marouani Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a *** has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected *** COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a *** artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic *** officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent *** propose an advanced controller for a service robot to be used in *** type of robot is deployed to deliver food and dispense medications to individual *** autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its *** criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

关键词： Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system

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Versatile Video Coding Performance Evaluation for Tiled 360° Videos 28

Versatile Video Coding Performance Evaluation for Tiled 360...

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28th European Wireless Conference, EW 2023

作者： Mahmoud, Moatasim Rizou, Stamatia Kantartzis, Nikolaos V. Valiandi, Ioanna Lazaridis, Pavlos I. Zaharis, Zaharias D. Panayides, Andreas S. Karagiannidis, George K. Singular Logic Aristotle University of Thessaloniki Greece R&D Department Singular Logic Athens14564 Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon

ISBN: (纸本)9783800762262

Recently, virtual reality (VR) and 360° videos have been increasingly adopted in a wide range of applications. However, the huge sizes of 360° videos necessitate the use of powerful compression tools to allow for efficient storage and streaming of these videos. In this article, we compare the performance of four different video codecs, including AV1 and versatile video coding (VVC), when applied to a dataset of 360° videos. We conduct our evaluation according to peak signal-to-noise ratio (PSNR)-based 360° video quality assessment (VQA) metrics. As omnidirectional videos are usually streamed as independent tiles, we also investigate the tiling effect on VVC compression performance by comparing full-frame-based compression to tiled segment compression using grids of 8x8 and 16x16 tiles. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Virtual reality

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