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Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends

IEEE Communications Surveys and Tutorials 2025年

作者： Zhang, Shiying Li, Jun Shi, Long Ding, Ming Nguyen, Dinh C. Chen, Wen Han, Zhu Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Southeast University School of Information Science and Engineering Nanjing210096 China CSIRO Data61 EveleighNSW Australia University of Alabama Department of Electrical and Computer Engineering Huntsville United States Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China 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 emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. The Industrial Metaverse is used for product design, production operations, quality inspection, and testing. However, there is limited understanding of the enabling technologies associated with it, including the specific industrial scenarios targeted by each technology and the potential migration of technologies from other domains to the industrial sector. This paper provides a comprehensive survey of the latest literature on the Industrial Metaverse. We first analyze its advantages for industrial production, then review key enabling technologies such as blockchain (BC), privacy-preserving computing (PPC), digital twin (DT), fifth/sixth generation mobile communication technology (5G/6G), XR, and artificial intelligence (AI), and explore how these technologies support different aspects of industrial production. We also present major challenges in the Industrial Metaverse, including privacy and security concerns, resource limitations, and interoperability constraints, along with existing solutions. Finally, we outline several open issues and future research directions. © 1998-2012 IEEE.

关键词： artificial intelligence blockchain digital twin extended reality fifth/sixth generation mobile communication technology industrial Metaverse privacy-preserving computing

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A Paradigm Shift in Ethereum Network Analysis Through Google BigQuery,Portation, and Visualization

A Paradigm Shift in Ethereum Network Analysis Through Google...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： N.Suresh Kumar S. Mirdula Pushpa Singh T Gayathri Jayanthi S Anandraj M School of Computer Science and Engineering Jain Deemed-to-be University Ramanagara India Department of Electronic and Communication Engineering SRM TRP Engineering College Trichy India Department of Computer Science and Engineering IILM University Greater Noida India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India Department of Information Technology Guru Nanak Institute of Technology Ibrahimpatnam India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

The primary objective of this research is to comprehensively explore and analyze the dynamics of the Ethereum network using innovative methodologies and system architectures. The study aims to extract meaningful statistics from the Ethereum blockchain, focusing on account activity, popularity trends, and the distribution of transactions. Through rigorous data collection, robust query construction, and advanced analytical techniques, the research seeks to provide valuable insights into how the Ethereum network has evolved, particularly in the aftermath of the “crypto bubble explosion.” The overarching goal is to contribute to the understanding of Ethereum's structural patterns, user behaviors, and the impact of external factors on the network. The research has yielded significant results, unveiling key insights into Ethereum network dynamics. The data collection phase, facilitated by Google BigQuery, successfully captured and filtered relevant information from a specific block range post the “crypto bubble explosion.” The SQL queries, strategically designed for active account identification and popularity assessment, demonstrated efficiency and accuracy in handling the vast Ethereum dataset. The proposed system, introducing the novel methodology of “portation,” showcased its efficiency in extracting and interpreting Ethereum blockchain data using Google BigQuery. The system architecture, as illustrated in the diagram, proved to be a well-coordinated and dynamic framework, emphasizing the seamless flow of data and processes.

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Development of a Voice-Enabled Automated Sleep Survey System 46

Development of a Voice-Enabled Automated Sleep Survey System

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Qiu, Yang Mohanty, Anwesh Owens, Robert L. Nemati, Shamim University of California San Diego Computer Science and Engineering La Jolla United States University of California San Diego Department of Biomedical Informatics La Jolla United States University of California San Diego Department of Medicine La Jolla United States

ISBN: (纸本)9798350371499

To enhance sleep quality in hospitalized patients, we developed a conversational agent that streamlines the collection and analysis of sleep data. The system employs the Richards-Campbell Sleep Questionnaire, supplemented by additional questions about environmental factors such as room temperature and lighting, to comprehensively evaluate sleep disturbances experienced by patients. By processing patients' spoken responses, the agent identifies environmental and care-related factors impacting sleep, which may allow for non-pharmacological interventions to enhance sleep quality. The integration of advanced conversational AI technologies, including GPT-4 and large language models, is a key feature of this system, enabling nuanced interpretation and structuring of patient feedback. This approach not only streamlines sleep assessment in hospital settings but also aligns with the shift towards patient-centric healthcare. By offering detailed insights into factors affecting sleep, the system showcases its potential with a high recognition accuracy, underscoring its potentially valuable role in advancing healthcare sleep quality management. © 2024 IEEE.

关键词： Sleep research

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Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

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Complex System Modeling and Simulation 2023年第3期3卷 169-190页

作者： Yinan Guo Yao Huang Shirong Ge Yizhe Zhang Ersong Jiang Bin Cheng Shengxiang Yang School of Mechanical Electronic and Information Engineering China University of Mining and Technology(Beijing)Beijing 100083China Inner Mongolia Research Institute China University of Mining and Technology(Beijing)Ordos 017010China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK.

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy *** studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred ***,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all *** that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance *** a problem-solver,an improved artificial bee colony algorithm is put *** especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific *** order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively *** a stagnant food source,a knowledge-driven initialization is developed to generate a feasible *** experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

关键词： electric trackless rubber-tyred vehicles low-carbon routing artificial bee colony algorithm

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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AgentMixer: Multi-Agent Correlated Policy Factorization 39

AgentMixer: Multi-Agent Correlated Policy Factorization

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zhiyuan Zhao, Wenshuai Wu, Lijun Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an asymmetric learning failure due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a strategy modification called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce Individual-Global-Consistency to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an ϵ-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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A 37 GHz Millimeter-Wave Antenna Array for 5G Communication Terminals

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computers, Materials & Continua 2023年第4期75卷 1317-1330页

作者： Jalal Khan Sadiq Ullah Usman Ali Ladislau Matekovits Farooq Ahmad Tahir Muhammad Inam Abbasi Telecommunication Engineering Department University of Engineering and TechnologyMardan23200Pakistan Department of Electronics and Telecommunications Politecnico di TorinoTurin10129Italy Istituto di Elettronica e di Ingegneria Dell’Informazione e Delle Telecomunicazioni National Research Council of ItalyTurin10129Italy Department of Measurements and Optical Electronics Politehnica University TimisoaraTimisoara300006Romania Research Institute for Microwave and Millimeter-Wave Studies(RIMMS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Department of Electronics and Computer Engineering Technology(JTKEK) Faculty of Electrical and Electronic Engineering Technology(FTKEE)Universiti Teknikal Malaysia Melaka(UTeM)Hang Tuah JayaDurian TunggalMelaka76100Malaysia

This work presents,design and specific absorption rate(SAR)analysis of a 37GHz antenna,for 5th Generation(5G)*** proposed antenna comprises of 4-elements of rectangular patch and an even *** radiating element is composed of copper material supported by Rogers RT5880 substrate of thickness,0.254 mm,dielectric constant(εr),2.2,and loss tangent,*** 4-elements array antenna is compact in size with a dimension of 8mm×20mm in length and *** radiating patch is excited with a 50 ohms connector i.e.,*** antenna resonates in the frequency band of 37 GHz,that covers the 5G *** antenna behavior is studied both in free space and in the proximity of the human *** models of the human body,i.e.,belly,hand,and head(contain skin,fat,muscles,and bone)are considered for on-body *** resonant frequency,the antenna gives a boresight gain of 11.6 *** antenna radiates efficiently with a radiated efficiency of more than 90%.Also,it is observed that the antenna detunes to the lowest in the proximity of the human body,but still a good impedance matching is achieved considering the−10 dB ***,SAR is also being *** safe limit of 2 W/kg for any 10 g of biological tissue,specified by the European International Electro Technical Commission(IEC)has been *** calculated values of SAR for human body models,i.e.,belly,hand and head are 1.82,1.81 and 1.09 W/kg,*** SAR values are less than the international recommendations for the three ***,the simulated and measured results of the antenna are in close agreement,which makes it,a potential candidate for the fifth-generation smart phones and other handheld devices.

关键词： 5G antenna array SAR millimeter-wave smart phones

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Control of Grid-Connected Inverters Using PLL for Synchronization and Harmonic Mitigation

Control of Grid-Connected Inverters Using PLL for Synchroniz...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Kamran Yousefpour Mohammad Yousefpour Seyyed Javad Hosseini Molla Mansour Taheri Andani Sepideh Alaei Varnousdaderani Hamid Taheri Andani Department of Electrical and Computer Engineering Mississippi State University Azad University Department of Electrical and Computer Engineering Babol Noshirvani University of Technology School of Manufacturing Systems and Networks Arizona State University Department of Computer Engineeering Azad University Department of Electronic Engineering Isfahan University of Technology

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents the design and simulation of a single-phase grid-connected inverter control system, focusing on enhancing power quality and dynamic performance. The control system comprises a resonant controller for real and reactive power regulation, ensuring sinusoidal current injection into the grid. Additionally, a Phase-Locked Loop (PLL) is incorporated to improve synchronization with the grid voltage, generating accurate reference signals for current control and mitigating harmonic disturbances. The PLL design leverages specific gain parameters, $\boldsymbol{\mu}_{\mathbf{1}},\ \boldsymbol{\mu}_{\mathbf{2}}$ , and $\boldsymbol{\mu}_{\mathbf{3}}$ , optimized for a critically damped response. A comprehensive simulation study evaluates the system's performance under different conditions, including soft-start operation, transient power commands, and grid voltage distortion with low-order harmonics (Sth, 7th, LIth, and 13th). Results highlight the system's robust dynamic response, low total harmonic distortion (THD), and effective harmonic attenuation, demonstrating its suitability for grid-connected distributed energy resource (DER) applications. This work provides a scalable and reliable control framework for next-generation inverter systems operating in modern, complex grid environments.

关键词： Total harmonic distortion System performance Power quality Power system harmonics Harmonic analysis Inverters Synchronization Phase locked loops Voltage control Transient analysis

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Advancing Precision in Lung Cancer Subtyping: Integration of Machine Learning Feature Selection with MLP 7

Advancing Precision in Lung Cancer Subtyping: Integration of...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Yu, Hao Zhan, Siyu Liu, Shuang Guo, Lu Huang, Rui School Of Optoelectronic Science And Engineering University Of Electronic Science And Technology Of China Chengdu China School Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China Yingcai Experimental College University Of Electronic Science And Technology Of China Chengdu China University Of Electronic Science And Technology Of China Sichuan Provincial People's Hospital Department Of Pulmonary And Critical Care Medicine Chengdu China Hepatobiliary & Pancreatic Cen Sichuan Provincial People's Hospital Chengdu China

ISBN: (纸本)9798350361445

This study focuses on improving the accuracy of lung cancer subtype classification by integrating machine learning feature selection with a deep learning model, namely, a Multi-Layer Perceptron (MLP). Using gene expression data from the TCGA database, we employed threshold variance and chi-square test feature selection, normalized the selected gene features using z-score normalization, and finally applied MLP for deep learning classification. Through comparisons with traditional machine learning models such as KNN, SVM, and Random Forest, we comprehensively examined multiple evaluation metrics, including accuracy, recall, precision, and F1 score. The results indicate that our MLP approach exhibits significant advantages over traditional methods in all aspects, particularly achieving a notable improvement in accuracy ranging from 2.6% to 12.8%. Overall, our deep learning classification method demonstrates outstanding performance in lung cancer subtype tasks, providing robust support for the application of deep learning in the biomedical field. © 2024 IEEE.

关键词： Gene expression

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GAN-generated Synthetic Data and SVM-based Feature Selection for Improved Cardiovascular Disease Prediction

GAN-generated Synthetic Data and SVM-based Feature Selection...

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Medical Technologies National Conference (TIPTEKNO)

作者： Amel Sulaiman Mandan Nidhal Mohsin Hazzaa Oktay Yildiz Computer Science Department Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

Early diagnosis requires cardiovascular disease forecasting. Past patient interests can improve machine learning predictions. This study uses a Generative Adversarial Network (GAN) to augment data to address the issue of insufficient datasets. Our objective is to use the GAN and feature selection (FS) to tackle the issue of class imbalance. The study assesses the efficacy of three different machine learning (ML) algorithms in predicting coronary artery disease (CAD). The UCI repository provided the Z-Alizadeh sani dataset for our analysis. A GAN was used to balance an imbalanced dataset. We used GAN to augment data and synthesise data to improve the models. Support Vector Machine (SVM) as feature selection tool was used to extract more effective attributes. We trained and tested CAD prediction using machine learning algorithms using 5-fold cross validation. We compared the machine-learning algorithm’s outcomes with and without GAN-based data enhancement and feature selection to evaluate its predicted accuracy. According to results GAN and feature selection improved accuracy, sensitivity, and specificity for all algorithms, suggesting that generated data and selected attributes can identify positive and negative cases.

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