Integrating Knowledge Graphs and Digital Twins for Heritage Building Conservation DOI Creative Commons
Haidar Hosamo, Silvia Mazzetto

Buildings, Journal Year: 2024, Volume and Issue: 15(1), P. 16 - 16

Published: Dec. 24, 2024

This study presents a framework for integrating digital twins and knowledge graphs to enhance heritage building conservation, addressing challenges in environmental stress management, material degradation, structural integrity while preserving historical authenticity. Using validated synthetic data, the enables real-time monitoring, predictive maintenance, emergency response through twin connected graph. Four scenarios were simulated evaluate system performance: high humidity exceeding 75% threshold triggered alerts limestone maintenance; temperature fluctuations caused strain levels up 0.009 units load-bearing components at 35 °C, necessitating inspection; cumulative degradation monitoring projected re-plastering needs by month eight as plaster index approached 85%; sudden impact events responses, with spikes over 0.004 prompting within 2.5 s. Response times averaged 50 ms under normal conditions, peaking 150 during high-frequency updates, showing robust Application Programming Interface (API) performance data synchronization. SPARQL (SPARQL Protocol RDF Query Language) queries graph facilitated proactive maintenance scheduling, reducing reactive interventions supporting sustainable especially suited humid–temperate climates. offers novel, structured approach that bridges modern technology preservation needs, both urgent conservation long-term sustainability ensure resilience of buildings.

Language: Английский

Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems DOI Creative Commons
Marek Nagy,

Marcel Figura,

Katarína Valašková

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(6), P. 981 - 981

Published: March 17, 2025

In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. While its popularity among companies grows, most studies highlight theoretical benefits, with few providing empirical evidence on economic impact. This study aims fill this gap by quantifying the performance of manufacturing in Visegrad Group countries through PdM algorithms. The purpose our research assess whether these generate higher operational profits and lower sales costs. Using descriptive statistics, non-parametric tests, Hodges–Lehmann median difference estimate, linear regression, authors analysed data 1094 enterprises. Results show that significantly improves performance, variations based geographic scope. Regression analysis confirmed as an essential predictor even after considering factors like company size, legal structure, Enterprises more effective cost management net were likely adopt PdM, revealed decision tree analysis. Our findings provide benefits algorithms their potential enhance competitiveness, offering a valuable foundation for business managers make informed investment decisions encouraging further other industries.

Language: Английский

Citations

2
A dynamically updatable knowledge graph construction method for computer-aided process planning and design DOI
En Xia Zhang, Shengwen Zhang,

Jia-Le Jia

et al.

Journal of Engineering Design, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 34

Published: Feb. 20, 2025

Language: Английский

Citations

1
Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing DOI Creative Commons
Y Wan, Zhenyuan Chen, Ying Liu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103212 - 103212

Published: Feb. 22, 2025

Language: Английский

Citations

0
Conclusions, Challenges, and Future Directions of Advanced Technologies in Fashion Supply Chain DOI
Huy Truong Quang, Rajkishore Nayak, Rudrajeet Pal

et al.

Springer series in fashion business, Journal Year: 2025, Volume and Issue: unknown, P. 331 - 348

Published: Jan. 1, 2025

Language: Английский

Citations

0
AI in Smart Manufacturing DOI
Josué Román Martínez-Mireles, Jazmín Rodriguez-Flores, Marco Antonio García-Márquez

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 463 - 494

Published: March 7, 2025

This chapter provides a comprehensive overview of the integration artificial intelligence (AI) and related technologies in manufacturing sector, detonating Smart Manufacturing. It discusses pivotal role AI enhancing operational efficiency, optimizing production processes, improving product quality through data-driven decision-making, including IoT Big Data integration. All these are applied in. predictive maintenance, control, supply chain optimization, showcasing real-world case studies examples. Additionally, it addresses challenges opportunities associated with implementing interaction of. AI, IoT, Industry 4.0, data manufacturing, emphasizing importance fostering culture innovation continuous improvement. The findings underscore transformative potential driving evolution smart ultimately contributing to increased competitiveness rapidly changing global economy.

Language: Английский

Citations

0
Intelligent configuration management in modular production systems: Integrating operational semantics with knowledge graphs DOI Creative Commons
Hamood Ur Rehman, Fan Mo, Jack C. Chaplin

et al.

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 610 - 625

Published: April 9, 2025

Language: Английский

Citations

0
Explainable knowledge graph embeddings for industrial process monitoring & control DOI
Michael Weyns, Thibault Blyau, Bram Steenwinckel

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103242 - 103242

Published: May 1, 2025

Language: Английский

Citations

0
Robotics, Automation, and Artificial Intelligence's Future in the Automotive Sector DOI

E. Devaraj,

Ismail Kakaravada, Shiv Prasad Yadav

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 281 - 310

Published: April 18, 2025

AI is now an everyday aspect of many people's daily lives worldwide. Businesses have more potential to use optimise some risky or repetitive processes that were previously handled by humans, but people are often afraid due concerns about privacy and lost job opportunities. Meanwhile, has also become a cross-cultural phenomenon. Perceptions thought change between cultures, advanced emerging economies may different ideas how will develop in the future. Therefore, order comprehend relationship culture usage AI, it crucial look into individual organisational variances attitudes towards trust based on cultural differences.

Language: Английский

Citations

0
Mechanism of knowledge management system self-learning case and knowledge matching based on bidirectional dimension reduction DOI
Xiaoqian Zhou, Ziao Cao, Sajjad Alam

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110985 - 110985

Published: May 3, 2025

Language: Английский

Citations

0
Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining DOI Creative Commons
Zhiwei Cheng, Luyu Ding, Cheng Peng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(10), P. 5235 - 5235

Published: May 8, 2025

Background: Accurate estrus identification in dairy cows is essential for enhancing reproductive efficiency and economic performance. The dispersed nature of data individual cow differences pose significant challenges accurate identification. Methods: This study gathered from 812 literature sources using Python 3.9 crawler technology. were then preprocessed CiteSpace 6.4. We constructed a knowledge graph depicting physiological, behavioral, appearance changes during through entity relationship extraction. To uncover potential relationships within the graph, we applied compared two association rule algorithms: FP-Growth Apriori. utilized Boolean functions derived learning to validate ability rules identify normal estrus. Additionally, employed an enhanced Iforest-OCSVM anomaly detection model assess performance detecting abnormal Furthermore, optimized Incremental Algorithm Dynamic Knowledge Expansion. Results: Based on initial with 86 entities 9 relationships, mining added 17 new strong marked by ‘with’, its completeness providing deeper insights into behaviors physiological changes. these exhibited notable effectiveness both detection, validating their robustness practical applications. algorithm’s optimization bolstered scalability, making it more adaptable future expansions complex integrations. Conclusions: By constructing that integrates estrus, established comprehensive framework understanding Association mining, particularly algorithm, enriching content offering demonstrated utility accuracy, robust foundation multi-dimensional research.

Language: Английский

Citations

0