Introduction to the Special Issue on Artificial Intelligence and Cyber-Physical Systems - Part 2

Abstract

low-latency monitoring, out-of-distribution detection, and preventive maintenance.In “Fog-supported Low Latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach” , Sahnoun et al. designed a new monitoring tool for system disruption related to the localization of mobile resources. In “Efficient Out-of-Distribution Detection Using Latent Space of β -VAE for Cyber-Physical Systems” , Ramakrishna et al. tackled the problem that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD) . Detecting OOD conditions at runtime is critical for the safety of CPS. The authors proposed an approach to design and train a single β -Variational Autoencoder de-tector with a partially disentangled latent space sensitive to variations in image features to detect OOD images and identify the most likely feature(s) responsible for the OOD. In “A Hybrid Deep Learning Framework for Intelligent Predictive Maintenance of Cyber-Physical Systems” , Sai et al. proposed a practical and effective hybrid deep learning multi-task framework, which integrates the advantages of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, to reflect the relatedness of remaining useful life prediction with health status detection process in the CPS environment. The proposed framework can provide strong support for the health management and maintenance strategy development of complex multi-object systems.