Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS).
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
Moreover, with a graph learning module, our model learns a sparse adjacency matrix to explicitly capture the stable interrelation structure information among multiple time series data channels for interpretable reconstruction of series patterns.
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.
Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted.