標(biāo)題: Titlebook: Artificial Intelligence for Smart Manufacturing; Methods, Application Kim Phuc Tran Book 2023 The Editor(s) (if applicable) and The Author( [打印本頁(yè)] 作者: Embolism 時(shí)間: 2025-3-21 19:01
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing影響因子(影響力)
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing被引頻次
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書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing讀者反饋
書(shū)目名稱(chēng)Artificial Intelligence for Smart Manufacturing讀者反饋學(xué)科排名
作者: AGOG 時(shí)間: 2025-3-21 23:56
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作者: shrill 時(shí)間: 2025-3-22 03:35
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作者: Conserve 時(shí)間: 2025-3-22 07:01 作者: 過(guò)分 時(shí)間: 2025-3-22 10:12 作者: Commonplace 時(shí)間: 2025-3-22 16:29 作者: ablate 時(shí)間: 2025-3-22 20:54 作者: SLAY 時(shí)間: 2025-3-22 21:13
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作者: brother 時(shí)間: 2025-3-23 05:24
,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作者: Atmosphere 時(shí)間: 2025-3-23 09:18
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作者: Collision 時(shí)間: 2025-3-23 10:35
1614-7839 lications based on AI techniques.Helps readers and practitio.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作者: scrape 時(shí)間: 2025-3-23 15:32
Approaches to Help in Organizations,tional solutions, which are often non-industrialized, with a new data-based method learned from the simulation of the plant behaviors and using recurrent neural networks (RNN) with short-term and long-term memory (Long short-term memory, LSTM).作者: Vaginismus 時(shí)間: 2025-3-23 18:38 作者: conference 時(shí)間: 2025-3-24 01:59
https://doi.org/10.1007/978-3-322-93671-4ate the operating status of the papermaking process. The verification results showed that the proposed model has a fault prediction accuracy of 76.8% and a recall rate of 72.5%, which allows anomalous data to be observed in advance, providing valuable time for subsequent fault diagnosis.作者: 多產(chǎn)子 時(shí)間: 2025-3-24 06:26 作者: epicardium 時(shí)間: 2025-3-24 10:30 作者: 手銬 時(shí)間: 2025-3-24 13:20 作者: 釘牢 時(shí)間: 2025-3-24 16:36 作者: CLIFF 時(shí)間: 2025-3-24 21:57
Multi-objective Optimization of Flexible Flow-Shop Intelligent Scheduling Based on a Hybrid Intelli optimize the established two-stage intelligent scheduling model. Finally, a papermaking production process is taken as an example to comprehensively evaluate the performance of the model and the hybrid intelligent algorithm. The experimental results show that the model and algorithm can effectively solve the presented problem.作者: Synapse 時(shí)間: 2025-3-25 02:59 作者: REIGN 時(shí)間: 2025-3-25 06:31 作者: LAITY 時(shí)間: 2025-3-25 10:59 作者: CEDE 時(shí)間: 2025-3-25 12:52
Fallen Women in the Nineteenth-Century Novelificial Intelligence. Then, several difficulties and opportunities in the implementation of these techniques for quality control in Industry 5.0 are discussed. Finally, a case study on monitoring wine production in the food industry is also considered to show the performance of Machine Learning-based techniques for quality control.作者: orthopedist 時(shí)間: 2025-3-25 18:34 作者: forbid 時(shí)間: 2025-3-25 22:49
Quality Control for Smart Manufacturing in Industry 5.0,ificial Intelligence. Then, several difficulties and opportunities in the implementation of these techniques for quality control in Industry 5.0 are discussed. Finally, a case study on monitoring wine production in the food industry is also considered to show the performance of Machine Learning-based techniques for quality control.作者: LINES 時(shí)間: 2025-3-26 03:17 作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-26 05:42
Artificial Intelligence for Smart Manufacturing978-3-031-30510-8Series ISSN 1614-7839 Series E-ISSN 2196-999X 作者: Root494 時(shí)間: 2025-3-26 09:18
The Steel Sector in the Global Economylaborate on the essential issues related to the applications and the potential of Artificial Intelligence algorithms in Smart Manufacturing. We will introduce crucial topics that will be discussed in the following chapters of the book.作者: 芳香一點(diǎn) 時(shí)間: 2025-3-26 15:35
https://doi.org/10.1007/978-3-031-30510-8Smart manufacturing; Machine learning; Reinforcement Learning; Deep learning; Smart Condition Monitoring作者: 熱心 時(shí)間: 2025-3-26 17:20 作者: AMPLE 時(shí)間: 2025-3-27 00:56 作者: conquer 時(shí)間: 2025-3-27 01:51 作者: 結(jié)果 時(shí)間: 2025-3-27 07:43 作者: crease 時(shí)間: 2025-3-27 10:41 作者: 最有利 時(shí)間: 2025-3-27 15:10 作者: Allowance 時(shí)間: 2025-3-27 19:11 作者: 動(dòng)作謎 時(shí)間: 2025-3-28 00:45
https://doi.org/10.1007/978-3-322-93671-4ontrol (SPC) problems, the existing machine learning approaches have some limitations. For instance, most of them are designed for cases in which in-control (IC) process observations at different time points are assumed to be independent and identically distributed. In practice, however, serial corr作者: 成份 時(shí)間: 2025-3-28 05:10
https://doi.org/10.1007/978-3-322-93671-4ocess and the high risk derived from severe consequences on the paper mills in case of production failure. Whereas the paper manufacturing process is continuous that is difficult to be warned early of faults. To address such issues, this Chapter proposes a data-driven approach to predict fault in th作者: llibretto 時(shí)間: 2025-3-28 09:54 作者: 劇毒 時(shí)間: 2025-3-28 11:53 作者: 砍伐 時(shí)間: 2025-3-28 17:31
Die Entwicklung klinischer Hypothesen,transition from industry 4.0 to 5.0, smart manufacturing proves the efficiency in industry, where systems become increasingly complex, producing massive data, necessitating more demand for transparency, privacy, and performance. Federated learning has demonstrated its effectiveness in various applic作者: 值得尊敬 時(shí)間: 2025-3-28 19:50
,Demenz – ein vielf?ltiges Krankheitsbild, systems. In the context of Industry 4.0, these systems become more complex and can be monitored by different types of sensors. The quality and completeness of data are crucial factors for the success of any PHM task in this paradigm. Here, we investigate the possibility of exploiting additional dat作者: BAIL 時(shí)間: 2025-3-29 01:30
,Demenz – ein vielf?ltiges Krankheitsbild,e Internet of Things (IoT), cloud computing, and other leading technologies. As technology continues to grow and expand, the concept of a new Industry 5.0 paradigm could be investigated. Industry 5.0 aims to transform the manufacturing sector into a more sustainable, human-centric, and resilient man作者: 放縱 時(shí)間: 2025-3-29 06:08 作者: perpetual 時(shí)間: 2025-3-29 10:10
Approaches to Help in Organizations,ors delivering binary signals that can be modeled as Discrete Event Systems. This chapter presents an intelligent diagnostic solution to replace traditional solutions, which are often non-industrialized, with a new data-based method learned from the simulation of the plant behaviors and using recurr作者: 裹住 時(shí)間: 2025-3-29 12:57 作者: Meditative 時(shí)間: 2025-3-29 16:12 作者: 高興一回 時(shí)間: 2025-3-29 20:31 作者: 600 時(shí)間: 2025-3-30 01:09
