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In this work, acoustic emission (AE) monitoring was correlated with in-situ optical microscopy observation and electron backscatter diffraction (EBSD) measurements to investigate the evolution of a single stress corrosion crack in SUS420J2 stainless steel subjected to chloride droplet corrosion. A single dominant crack evolution was observed to transition from a slow initiation of active path corrosion-dominant cracking to a rapid propagation of hydrogen-assisted cracking. The initiation-to-propagation was concomitant with a significant increase in the number of AE events. In addition, a cluster analysis of the AE features including traditional waveform parameters and fast Fourier transform (FFT)-derived frequency components was performed using k-means algorithms. Two AE clusters with different frequency levels were extracted. Correlated with the EBSD-derived kernel average misorientation (KAM) map of crack path, low-frequency AE cluster was found to correspond with the location of plastic deformation in the propagation region. High-frequency AE cluster is supposed to be from the cracking process. The correlation between AE feature and SCC progression is expected to provide an AE signals-based in-situ insight into the SCC monitoring.

Nowadays, due to the growing need for remote care and the constantly increasing popularity of mobile devices, a large amount of mobile applications for remote care support has been developed. Although mobile phones are very suitable for young people, there are still many problems related to remote health care of the elderly. Due to hearing loss or limited movements, it is difficult for the elderly to contact their families or doctors via real-time video call. In this paper, we introduce a new remote health-care system based on moving robots intended for the elderly at home. Since the proposed system is an online system, the elderly can contact their families and doctors quickly anytime and anywhere. Besides call, our system involves the accurate indoor object detection algorithms and automatic health data collection, which are not included in existing remote care systems. Therefore, the proposed system solves some challenging problems related to the elderly care. The experiment has shown that the proposed care system achieves excellent performance and provides good user experience.