Disruptive Trends in Automation Technology
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (206 p.)Innehållstyp: - text
- computer
- online resource
- 9783725812110
- 9783725812127
- Computing and Information Technology
- Computer science
- CNN architecture
- DevOps
- Juglans regia
- MLOps
- Machine Learning
- SOIC package misalignment
- aerial pollination
- artificial pollination technologies
- automated troubleshooting
- continuous software engineering
- control loop
- critical systems
- cross-pollination
- cyber exercise
- cyber security
- dielectric fluid hydrodynamics
- digital twin
- electricity market
- exhaustive search
- explainable AI
- food supply chain
- force control
- high-voltage testing
- intelligent fault diagnosis
- linear active disturbance rejection control
- literature review
- machine learning
- modelling
- monitoring
- normalization techniques
- overall controller efficiency
- performance
- pollination drone
- powered parafoil system
- proactive SaaS support
- problem root cause analysis
- real-time product activity detection
- resource efficiency
- robotics
- self-compatibility
- single-input single-output
- soft sensor
- stability
- surface-mount devices (SMDs)
- time-series analysis
- trajectory tracking control
- twin delayed deep deterministic policy gradient
- vibration
- walnut blight disease
- wastewater treatm
Open Access Unrestricted online access star
The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data, and artificial intelligence are all driving the transformation of conventional simulators to digital twins.
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eng
Freely available e-book