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Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring by Shin, Junsik; Park, Junyoung; Park, Jongwoong is a scholarly article available to read on EtoBox.

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Regular structural monitoring of port structure is crucial to cope with rapid degeneration owing to its exposure to saline and collisional environment. However, most of the inspections are being done visually by human in irregular-basis. To overcome the complication, lots of research related to vibration-based monitoring system with sensor has been devised. Nonetheless, it was difficult to measure ambient vibration due to port's diminutive amplitude and specify the exact timing of berthing, which is the major excitation source. This study developed a novel cloud-AI based wireless sensor system with high-sensitivity accelerometer M-A352, which has 0.2uG/sqrt(Hz) noise density, 0.003mg of ultra-low noise feature, and 1000Hz of sampling frequency. The sensor is triggered based on either predefined schedule or long rangefinder. After that, the detection of ship is done by AI object detection technique called Faster R-CNN with backbone network of ResNet for the convolution part. Coordinate and size of the detected anchor box is further processed to certify the berthing ship. Collected data are automatically sent to the cloud server through LTE CAT 1 modem within 10Mbps. The system was i

Author

Shin, Junsik; Park, Junyoung; Park, Jongwoong

Published

2022

Language

EN