面向智能制造的深度学习和智能系统

IF 4.4 4区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mu-Yen Chen, E. Lughofer, E. Eğrioğlu
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引用次数: 3

摘要

机器学习已经应用于解决人类社会的复杂问题很多年了。机器学习的成功是因为计算能力和传感技术的支持。人工智能和数据驱动方法的发展将很快对该领域产生重大影响。搜索引擎、图像识别、生物识别、语音和手写识别、自然语言处理,甚至医疗诊断和金融信用评级都是常见的例子。很明显,随着人工智能渗透到我们的世界,更具体地说,渗透到我们的生活中,将给公众带来许多挑战。随着云计算、物联网、大数据、深度学习、AVG等新一代信息技术在制造业领域的融合和广泛应用,多个国家纷纷提出了国家先进制造业发展战略,如德国的工业4.0、美国的工业互联网和基于CPS (Cyber-Physical Systems)的制造系统、中国的“中国制造2025”、“互联网+制造”等。智能制造和智能工厂能够在整个制造供应链和产品生命周期中随时随地提供有关制造过程的所有信息。智能制造被预测为下一次工业革命或工业4.0。而且,与近年来的许多其他进步一样,这一切都与技术连接和数据情境化的进步有关。然而,没有智能系统的支持,也没有数据科学技术的支持,“智能”是无法实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Enterprise Information Systems
Enterprise Information Systems 工程技术-计算机:信息系统
CiteScore
11.00
自引率
6.80%
发文量
24
审稿时长
6 months
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.
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