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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks
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Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

机译：几何深度学习：工业4.0网络中的深度学习-物理复杂网络

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摘要

In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment.
机译：在不久的将来，与工业4.0相关的价值流将由相互连接的网络物理要素形成，这些要素形成复杂的网络，实时生成大量数据。在这种情况下，对精益管理系统的持续改进感兴趣的行业领导者的成功或失败取决于他们识别非欧陆域（例如动态社会技术复杂网络）中构造的这些大数据中的行为模式的能力。我们认为，一般的人工智能，尤其是深度学习的人工智能，可能会在网络物理系统的精益管理中帮助找到4.0工业环境中有用的行为模式。但是，尽管这些技术在过去意味着解决复杂问题的方式发生了转变，但是传统的深度学习方法（专注于图像或视频分析）都具有常规结构，无法在此特定领域中提供帮助。这就是为什么这项工作着重提出几何深度精益学习的原因，这是一种数学方法，用于描述诸如网络物理工业4.0图形上的卷积和池化之类的深度精益学习操作。几何深度精益学习预计将为组织的可持续发展提供积极支持，因为客户和供应商应能够在为双方创造新业务的流程的质量和效率上达到更高的透明度和可追溯性水平，从而产生新的产品，服务和产品。网络物理环境中的合作机会。

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