{"title":"面向智能制造的深度学习和智能系统","authors":"Mu-Yen Chen, E. Lughofer, E. Eğrioğlu","doi":"10.1080/17517575.2021.1898050","DOIUrl":null,"url":null,"abstract":"Machine learning has been applied to solve complex problems in human society for years. The success of machine learning is because of the support of computing capabilities as well as sensing technology. An evolution of artificial intelligence and data-driven approaches will soon cause considerable impacts on the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to public as artificial intelligence infiltrates our world, and more specifically, our lives. With the integration and extensive applications of the new generation of information technologies (such as cloud computing, IoT, big data, deep learning, AVG) in manufacturing industry, a number of countries have put forward their national advanced manufacturing development strategies, such as Industry 4.0 in Germany, Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems) in the USA, as well as Made in China 2025 and Internet Plus Manufacturing in China. Smart Manufacturing and the Smart Factory enables all information about the manufacturing process to be available when and where it is needed across entire manufacturing supply chains and product lifecycles. Smart Manufacturing is being predicted as the next Industrial Revolution or Industry 4.0. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the advances in the contextualisation of data. However, with neither the intelligent system support nor the support of data science technology, ‘smart’ cannot be achieved.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"16 1","pages":"189 - 192"},"PeriodicalIF":4.4000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Deep learning and intelligent system towards smart manufacturing\",\"authors\":\"Mu-Yen Chen, E. Lughofer, E. Eğrioğlu\",\"doi\":\"10.1080/17517575.2021.1898050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning has been applied to solve complex problems in human society for years. The success of machine learning is because of the support of computing capabilities as well as sensing technology. An evolution of artificial intelligence and data-driven approaches will soon cause considerable impacts on the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to public as artificial intelligence infiltrates our world, and more specifically, our lives. With the integration and extensive applications of the new generation of information technologies (such as cloud computing, IoT, big data, deep learning, AVG) in manufacturing industry, a number of countries have put forward their national advanced manufacturing development strategies, such as Industry 4.0 in Germany, Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems) in the USA, as well as Made in China 2025 and Internet Plus Manufacturing in China. Smart Manufacturing and the Smart Factory enables all information about the manufacturing process to be available when and where it is needed across entire manufacturing supply chains and product lifecycles. Smart Manufacturing is being predicted as the next Industrial Revolution or Industry 4.0. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the advances in the contextualisation of data. However, with neither the intelligent system support nor the support of data science technology, ‘smart’ cannot be achieved.\",\"PeriodicalId\":11750,\"journal\":{\"name\":\"Enterprise Information Systems\",\"volume\":\"16 1\",\"pages\":\"189 - 192\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2022-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Enterprise Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/17517575.2021.1898050\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Enterprise Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/17517575.2021.1898050","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Deep learning and intelligent system towards smart manufacturing
Machine learning has been applied to solve complex problems in human society for years. The success of machine learning is because of the support of computing capabilities as well as sensing technology. An evolution of artificial intelligence and data-driven approaches will soon cause considerable impacts on the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to public as artificial intelligence infiltrates our world, and more specifically, our lives. With the integration and extensive applications of the new generation of information technologies (such as cloud computing, IoT, big data, deep learning, AVG) in manufacturing industry, a number of countries have put forward their national advanced manufacturing development strategies, such as Industry 4.0 in Germany, Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems) in the USA, as well as Made in China 2025 and Internet Plus Manufacturing in China. Smart Manufacturing and the Smart Factory enables all information about the manufacturing process to be available when and where it is needed across entire manufacturing supply chains and product lifecycles. Smart Manufacturing is being predicted as the next Industrial Revolution or Industry 4.0. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the advances in the contextualisation of data. However, with neither the intelligent system support nor the support of data science technology, ‘smart’ cannot be achieved.
期刊介绍:
Enterprise Information Systems (EIS) focusses on both the technical and applications aspects of EIS technology, and the complex and cross-disciplinary problems of enterprise integration that arise in integrating extended enterprises in a contemporary global supply chain environment. Techniques developed in mathematical science, computer science, manufacturing engineering, and operations management used in the design or operation of EIS will also be considered.