3K: Knowledge-Enriched Digital Twin Framework DOI
Erkan Karabulut, Paul Groth, Viktoriya Degeler

et al.

Published: Nov. 19, 2024

Language: Английский

Digital twin (DT) and extended reality (XR) for building energy management DOI

Seungkeun Yeom,

Juui Kim,

Hyuna Kang

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 323, P. 114746 - 114746

Published: Aug. 31, 2024

Language: Английский

Citations

10
The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review DOI Creative Commons

Nabil Mchirgui,

Nordine Quadar, Habib Kraiem

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 10933 - 10933

Published: Nov. 25, 2024

This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet increasing energy demands new requirements renewable source integration, DTs offer promising solutions enhance monitoring, control, optimization these systems. In this paper, we examine concept context grids, their requirements, challenges, integration with Internet Things (IoT) Artificial Intelligence (AI). We also discuss different asset management, system operation, disaster response. paper analyzes current including data interoperability, cost, ethical considerations. Through case studies from various sectors Canada, illustrate real-world implementation impact DTs. Finally, emerging trends future directions, highlighting potential revolutionize networks contribute more efficient, reliable, sustainable

Language: Английский

Citations

8
A comprehensive survey of Network Digital Twin architecture, capabilities, challenges, and requirements for Edge-Cloud Continuum DOI
Syed Mohsan Raza, Roberto Minerva, Noël Crespi

et al.

Computer Communications, Journal Year: 2025, Volume and Issue: unknown, P. 108144 - 108144

Published: March 1, 2025

Language: Английский

Citations

1
An automatic unsafe states reasoning approach towards Industry 5.0’s human-centered manufacturing via Digital Twin DOI
Haoqi Wang, Guangwei Wang, Hao Li

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102792 - 102792

Published: Sept. 6, 2024

Language: Английский

Citations

5
An Expandable and Generalized Method for Equipment Information Reflection in Digital Twin Workshop Systems DOI
Yueze Zhang, Dongjie Zhang, Jun Yan

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2024, Volume and Issue: 89, P. 102763 - 102763

Published: March 27, 2024

Language: Английский

Citations

3
Fundamentals of Conceptual Modeling DOI
Heinrich C. Mayr, Bernhard Thalheim

Lecture notes in business information processing, Journal Year: 2025, Volume and Issue: unknown, P. 301 - 324

Published: Jan. 1, 2025

Language: Английский

Citations

0
Unraveling media perspectives: a comprehensive methodology combining large language models, topic modeling, sentiment analysis, and ontology learning to analyse media bias DOI
Orlando Jähde,

Thorsten Weber,

Rüdiger Buchkremer

et al.

Journal of Computational Social Science, Journal Year: 2025, Volume and Issue: 8(2)

Published: Feb. 25, 2025

Language: Английский

Citations

0
A System-of-Systems Approach for Deploying Containerized Construction Digital Twins Using Linked Data DOI
Philipp Hagedorn, Ekaterina Petrova, Markus König

et al.

Lecture notes in civil engineering, Journal Year: 2025, Volume and Issue: unknown, P. 478 - 491

Published: Jan. 1, 2025

Language: Английский

Citations

0
Knowledge-enhanced ontology-to-vector for automated ontology concept enrichment in BIM DOI
Yan Wei, Li Xiao

Journal of Industrial Information Integration, Journal Year: 2025, Volume and Issue: unknown, P. 100836 - 100836

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx DOI Creative Commons

Chaimae Kanzouai,

Soukaina Bouarourou, Abderrahim Zannou

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(4), P. 140 - 140

Published: March 25, 2025

The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially decentralized edge computing environments. In this paper, an advanced framework FedProx with ontology-driven standardization is proposed, which can meet such comprehensively. On the one hand, it guarantee semantic consistency across different kinds using unified ontology, so that from multiple sources could be seamlessly integrated; on other solves non-IID issues limited resources servers by FedProx. Experimental findings indicate outperforms FedAvg, a remarkable accuracy level 89.4%, having higher convergence rates, attaining 30% saving communication overhead through gradient compression. addition, ontology alignment procedure yielded 95% success rate, thereby uniform preprocessing domains, including traffic monitoring parking management. model demonstrates outstanding scalability flexibility to new devices, while maintaining high performance during evolution. These highlight its great potential for deployment smart cities, environmental monitoring, IoT-based ecosystems, creation more efficient integrated solutions these areas.

Language: Английский

Citations

0