Big data meet cyber-physical systems: A panoramic survey
The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which
are foreseen to revolutionize our world via creating new services and applications in a …
are foreseen to revolutionize our world via creating new services and applications in a …
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
The machine fault diagnosis is being considered in a larger-scale complex system with
numerous measurements from diverse subsystems or components, where the collected data …
numerous measurements from diverse subsystems or components, where the collected data …
Microrank: End-to-end latency issue localization with extended spectrum analysis in microservice environments
With the advantages of flexible scalability and fast delivery, microservice has become a
popular software architecture in the modern IT industry. However, the explosion in the …
popular software architecture in the modern IT industry. However, the explosion in the …
Enabling cyber‐physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber‐security
Cyber‐physical systems (CPS) help create new services and applications by revolutionising
our world in different fields through their tight interactions and automated decisions. This is …
our world in different fields through their tight interactions and automated decisions. This is …
Anomaly detection models for smart home security
S Ramapatruni, SN Narayanan, S Mittal… - 2019 IEEE 5th Intl …, 2019 - ieeexplore.ieee.org
Recent years have seen significant growth in the adoption of smart homes devices. These
devices provide convenience, security, and energy efficiency to users. For example, smart …
devices provide convenience, security, and energy efficiency to users. For example, smart …
Deep convolutional clustering-based time series anomaly detection
This paper presents a novel approach for anomaly detection in industrial processes. The
system solely relies on unlabeled data and employs a 1D-convolutional neural network …
system solely relies on unlabeled data and employs a 1D-convolutional neural network …
Exploring inherent sensor redundancy for automotive anomaly detection
The increasing autonomy and connectivity have been transitioning automobiles to complex
and open architectures that are vulnerable to malicious attacks beyond conventional cyber …
and open architectures that are vulnerable to malicious attacks beyond conventional cyber …
Rlad: Time series anomaly detection through reinforcement learning and active learning
We introduce a new semi-supervised, time series anomaly detection algorithm that uses
deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to …
deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to …
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
W Yang, C Liu, D Jiang - Renewable energy, 2018 - Elsevier
The vast installment of wind turbines and the development of condition monitoring system
provides large amounts of operational data for condition monitoring and health …
provides large amounts of operational data for condition monitoring and health …
Exploiting consistency among heterogeneous sensors for vehicle anomaly detection
Modern vehicles house many advanced components; sensors and Electronic Control Units
(ECUs)—now numbering in the 100s. These components provide various advanced safety …
(ECUs)—now numbering in the 100s. These components provide various advanced safety …