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Titlebook: Artificial Intelligence for Smart Manufacturing; Methods, Application Kim Phuc Tran Book 2023 The Editor(s) (if applicable) and The Author(

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發(fā)表于 2025-3-21 19:01:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Artificial Intelligence for Smart Manufacturing
期刊簡(jiǎn)稱(chēng)Methods, Application
影響因子2023Kim Phuc Tran
視頻videohttp://file.papertrans.cn/163/162392/162392.mp4
發(fā)行地址Introduces the latest knowledge of the application of AI on smart manufacturing.Includes ready-to-use algorithms for smart manufacturing applications based on AI techniques.Helps readers and practitio
學(xué)科分類(lèi)Springer Series in Reliability Engineering
圖書(shū)封面Titlebook: Artificial Intelligence for Smart Manufacturing; Methods, Application Kim Phuc Tran Book 2023 The Editor(s) (if applicable) and The Author(
影響因子.This book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI)..As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical applicationof these tools in real-world scenarios..Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challe
Pindex Book 2023
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https://doi.org/10.1007/978-3-322-93671-4onparametric longitudinal modeling and sequential data decorrelation algorithms. The modified machine learning control charts can well accommodate time-varying IC process distribution and serial data correlation. Numerical studies show that their performance are improved substantially for monitoring
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Die Entwicklung klinischer Hypothesen,and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modeling, and garment fit assessment should be integr
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Dynamic Process Monitoring Using Machine Learning Control Charts,onparametric longitudinal modeling and sequential data decorrelation algorithms. The modified machine learning control charts can well accommodate time-varying IC process distribution and serial data correlation. Numerical studies show that their performance are improved substantially for monitoring
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,Personalized Pattern Recommendation System of Men’s Shirts,and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modeling, and garment fit assessment should be integr
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Wearable Technology for Smart Manufacturing in Industry 5.0,algorithm. In recent years, federated learning has been implemented with deep learning and AI to enhance powerful computing with big data, stable accuracy, and ensure the security of edge devices. In this chapter, the first objective is to survey the applications of wearable Internet of Things devic
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