An LLM-enabled human demonstration-assisted hybrid robot skill synthesis approach for human-robot collaborative assembly DOI
Yue Yin, Ke Wan, Chengxi Li

et al.

CIRP Annals, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Reinforcement learning algorithms: A brief survey DOI
Ashish Kumar Shakya, G. N. Pillai, Sohom Chakrabarty

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 231, P. 120495 - 120495

Published: May 23, 2023

Language: Английский

Citations

182

A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation DOI
Yue Yin, Pai Zheng, Chengxi Li

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2022, Volume and Issue: 81, P. 102515 - 102515

Published: Dec. 30, 2022

Language: Английский

Citations

163

Revolutionizing manufacturing: A comprehensive overview of additive manufacturing processes, materials, developments, and challenges DOI
Kumar Kanishka, Bappa Acherjee

Journal of Manufacturing Processes, Journal Year: 2023, Volume and Issue: 107, P. 574 - 619

Published: Nov. 8, 2023

Language: Английский

Citations

118

Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network DOI
Liqiao Xia, Yongshi Liang, Jiewu Leng

et al.

Reliability Engineering & System Safety, Journal Year: 2022, Volume and Issue: 232, P. 109068 - 109068

Published: Dec. 28, 2022

Language: Английский

Citations

80

Incidences of artificial intelligence in contemporary education DOI Open Access
José Ramón Sanabria Navarro, Yahilina Silveira Pérez, Digna Dionisia Pérez Bravo

et al.

Comunicar, Journal Year: 2023, Volume and Issue: 31(77)

Published: June 18, 2023

The term 'Artificial Intelligence' was coined in 1956 at a conference Dartmouth College and since then it has undergone constant development evolved radically. Prominent pioneers of the include John McCarthy, Marvin Minsky, Allen Newell, Herbert A. Simon. application AI education worldwide increased dramatically with its importance growing an increasing rate. objective this research is to bibliometrically analyze applications contemporary education. methodology includes Prisma articles three fundamental databases: Scopus (n=390), Mendeley (n=113), Science Direct (n=3,594). A total n=4,097 English Spanish were analyzed. systematic literature review recent works employed mixed approach using quantitative qualitative methods. It inferred by authors that revolutionizing offering personalized efficient solutions improve students’ learning. One main conclusions education, students are one groups most affected AI. Furthermore, human intelligence teachers plays role they adapt their methodologies leverage new technologies. Finally, worth noting decisions made schools universities support educational models based on technology. El término «Inteligencia Artificial» fue acuñado en una conferencia College, y desde entonces, este ha experimentado un desarrollo constante evolucionado de manera significativa. Algunos los pioneros más destacados incluyen Newell La aplicación la inteligencia artificial educación aumentado considerablemente nivel mundial dinámica era digital. objetivo investigación es analizar bibliométricamente las incidencias IA contemporánea. metodología contiene tres bases datos fundamentales (n=113) (n=3.594), para n=4.097 artículos idioma inglés español. revisión sistematizada literatura reciente tiene enfoque mixto, cuantitativos cualitativos empleando varios paradigmas función del objetivo, se obtiene que revolucionado educación, ofreciendo soluciones personalizadas eficientes mejorar el aprendizaje estudiantes. En principales conclusiones plantea términos teóricos mayor impacto están estudiantes como elemento principal Por otra parte, profesores juegan papel proceso través sus metodologías uso estas tecnologías. Así mismo currículos educacionales mediante toma decisiones colegios universidades apostando por nuevos modelos tecnológicos educativos.

Citations

60

Synergistic industrial agglomeration, new quality productive forces and high-quality development of the manufacturing industry DOI
Yi Liu,

HE Zheng-chu

International Review of Economics & Finance, Journal Year: 2024, Volume and Issue: 94, P. 103373 - 103373

Published: May 27, 2024

Language: Английский

Citations

53

Machine learning-assisted self-powered intelligent sensing systems based on triboelectricity DOI

Zhiyu Tian,

Jun Li, Liqiang Liu

et al.

Nano Energy, Journal Year: 2023, Volume and Issue: 113, P. 108559 - 108559

Published: May 26, 2023

Language: Английский

Citations

51

Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions DOI Creative Commons
Robert X. Gao, Jörg Krüger, Marion Merklein

et al.

CIRP Annals, Journal Year: 2024, Volume and Issue: 73(2), P. 723 - 749

Published: Jan. 1, 2024

Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since beginning 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research development in manufacturing. This paper highlights manufacturing, ranging from production system design planning to process modeling, optimization, quality assurance, maintenance, automated assembly disassembly. In addition, presents an overview representative manufacturing problems matching solutions, perspective future leverage towards realization smart

Language: Английский

Citations

40

Deep reinforcement learning for multi-objective optimization in BIM-based green building design DOI
Yue Pan, Yuxuan Shen,

Jianjun Qin

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105598 - 105598

Published: July 4, 2024

Language: Английский

Citations

25

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future DOI Creative Commons
Abu Danish Aiman Bin Abu Sofian, Xun Sun,

Vijai Kumar Gupta

et al.

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(3), P. 1593 - 1617

Published: Jan. 16, 2024

This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for roles in energy fuel sectors. These when integrated with ML techniques, exhibit enhanced functionalities, optimizing systems, storage, conversion. Detailed case studies reveal of biobased polymers applications industry, further showcasing how bolsters efficiency innovation. The intersection also marks advancements biochemical production, emphasizing innovations drug delivery medical device development. underscores imperative harnessing convergence future global sustainability endeavors collective evidence presented asserts immense promise this union holds steering a innovative trajectory.

Language: Английский

Citations

19