Machine Learning Algorithms for Cyber Physical Systems
摘要截稿:
全文截稿: 2019-04-10
影响因子: 6.471
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 1区
• 小类 : 工程:机械 - 1区
Overview
Cyber physical systems are integrations of computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. Cyber physical systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Nowadays, machines can learn und develop self-maintaining procedures, meaning that those systems can provide a broad range of substantial improvements if introduced in production. Due to the highly complex intertwining among different components, cyber physical systems poses fundamental challenges in multiple aspects, such as real-time data processing, efficient parallel computing, data sensing and collection, and distributed computing. Over the last decade, machine learning algorithms have achieved impressive results providing solutions to practical large-scale complex problems. Not surprisingly, machine learning is being used in cyber physical systems-systems that are integrations of computation with physical processes. The special issue will include current and future research directions in machine learning algorithms for cyber physical systems in aerospace, automotive, medical, energy and other sectors such as manufacturing.
The following is a non-exhaustive list of topics considered for this special issue:
Learning theory (supervised/unsupervised) for cyber physical systems
Machine Learning Algorithms for Vibration & noise control
Machine Learning Algorithms for Integrated systems
Machine Learning based Signal processing for the understanding of mechanical systems
Machine Learning for industrial predictive maintenance
Machine Learning for vibration testing in cyber physical systems
Fault diagnosis in cyber physical systems using Machine Learning
Deep learning for cyber physical systems
Incremental learning for cyber physical systems
Structure learning for cyber physical systems
Signal transformations in cyber physical systems using machine learning
Mathematical foundations of machine learning for cyber physical systems
Machine learning algorithms for signal detection and synchronization in cyber physical systems
Machine learning algorithms for localization, positioning and tracking techniques in cyber physical systems