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Integration of smart grid with Industry 5.0: Applications, challenges and solutions

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Measurement: Energy

Measurement: Energy 2025年 5卷

作者： Sunawar khan Tehseen Mazhar Tariq Shahzad Muhammad Amir khan Ateeq Ur Rehman Habib Hamam Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar 62300 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia School of Computing Gachon University Seongnam 13120 Republic of Korea Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Introduction In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two. Objectives In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage. Method ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0. Findings and discussion In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures. Conclusion Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.

关键词： Smart grid Energy efficiency Industry 5.0 Haman-machine collaboration Advanced metering infrastructure (AMI) Real-time data analytics

Hydrometeorological Network of Namal Valley, Pakistan

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Scientific data 2025年第1期12卷 979页

作者： Muhammad Sheraz Talha Manzoor Malik Jahan Khan Usman Ali Hamza Tariq Shabeh Ul Hasson Centre for Water Informatics and Technology Lahore University of Management Sciences (LUMS) Lahore Pakistan. Department of Computer Science Lahore University of Management Sciences (LUMS) Lahore Pakistan. School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan. Faculty of Computing Engineering and Media De Montfort University Leicester LE1 9BH UK. HAREME Lab Institute of Geography CEN Universität Hamburg Bundesstrasse 55 Hamburg 20146 Germany. shabeh.hasson@uni-hamburg.de.

This article describes data collected from a sensor network deployed in the catchment of The Namal Lake, situated in Mianwali district, Pakistan. The lake serves as a reservoir for the Namal Dam. The sensor network consists of 14 unique observation points in a watershed spanning a 425 sq km area. The data includes measurements of precipitation at a resolution of 0.246 mm at all sensor locations and measurements of water level at a 1 mm resolution at 7 locations (including the lake itself). The first sensor installation was made in November 2020 with the last one deployed in July 2022. The associated time series for both variables is generated at a 10-minute sampling frequency. Multiple aggregations of the dataset have been made available at hourly, daily, and monthly timescales. The data can be used for calibrating hydrological models for various applications such as flood prediction, watershed management, and decision-making for reservoir operation. The dataset also contributes to understanding the hydrometeorological processes in similar small catchments at the Monsoon Margin.

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A few-shot learning graph multi-trajectory evolution network for forecasting multimodal baby connectivity development from a baseline timepoint

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arXiv 2021年

作者： Bessadok, Alaa Nebli, Ahmed Mahjoub, Mohamed Ali Li, Gang Lin, Weili Shen, Dinggang Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Mannouba Tunisia Department of Radiology and BRIC University of North Carolina Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development United Imaging Intelligence Co. Ltd. Shanghai China

Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: "Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities?" To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https://***/basiralab/GmTE-Net. © 2021, CC BY-NC-ND.

关键词： Trajectories

Social media emotion macroscopes reflect emotional experiences in society at large

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arXiv 2021年

作者： Garcia, David Pellert, Max Lasser, Jana Metzler, Hannah Graz University of Technology Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz8010 Austria Complexity Science Hub Vienna Vienna1080 Austria Medical University of Vienna Center for Medical Statistics Informatics and Intelligent Systems Vienna1090 Austria Medical University of Vienna Department of Social and Preventive Medicine Center for Public Health Vienna1090 Austria

Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences1. Millions of texts from social media can be processed with computational methods to study emotions2,3 over time and across regions4,5. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level6,7 and between static regional aggregates of social media emotion and subjective well-being at the population level8, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others9 rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness10 and algorithmic confounding11, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

An improved simulation model for pedestrian crowd evacuation

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TechRxiv

TechRxiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani KRG Sulaymaniyah46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler KRG Erbil44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Bathurst SydneyNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil KRG Erbil44001 Iraq Department of Information Technology Lebanese French University KRG Erbil44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan West Bengal Burdwan713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation

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arXiv 2021年

作者： Tang, Song Yang, Yan Ma, Zhiyuan Hendrich, Norman Zeng, Fanyu Ge, Shuzhi Sam Zhang, Changshui Zhang, Jianwei The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Group Department of Informatics Universität Hamburg Hamburg Germany The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Lab. for Novel Software Technology Nanjing University Nanjing China The Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore The Department of Automation Tsinghua University Beijing China

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is available at https://***/tntek/N2DCX. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach

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arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Wang, Yu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care. Utilizing population models to address this challenge is a potential solution, but collecting patient data for training population models in a privacy-conscious manner is challenging, especially given that such data is often stored on personal devices. Considering the privacy protection and addressing the "cold start" problem in diabetes care, we propose "GluADFL", blood Glucose prediction by Asynchronous Decentralized Federated Learning. We compared GluADFL with eight baseline methods using four distinct T1D datasets, comprising 298 participants, which demonstrated its superior performance in accurately predicting BG levels for cross-patient analysis. Furthermore, patients’ data might be stored and shared across various communication networks in GluADFL, ranging from highly interconnected (e.g., random, performs the best among others) to more structured topologies (e.g., cluster and ring), suitable for various social networks. The asynchronous training framework supports flexible participation. By adjusting the ratios of inactive participants, we found it remains stable if less than 70% are inactive. Our results confirm that GluADFL offers a practical, privacy-preserving solution for BG prediction in T1D, significantly enhancing the quality of diabetes management. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

ecUWB: A Energy-Centric Communication Scheme for Unstable WiFi Based Backscatter Systems : (Invited Paper)

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ecUWB: A Energy-Centric Communication Scheme for Unstable Wi...

International Conference on computer Communications and Networks (ICCCN)

作者： Yue Xu Jingwen Zhang Hao Zhou Xiaoyan Wang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology Univ. of Science and Technology of China China College of Engineering Ibaraki University Japan Department of Mathematical and Systems Engineering Shizuoka University Japan Information Systems Architecture Research Division National Institute of Informatics Japan

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

关键词： Wireless fidelity Backscatter Energy harvesting Receivers Systems architecture Power supplies Modulation