论文标题
开发基于AI-Cloud的高敏性无线智能传感器，用于端口结构监视
Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring
论文作者
论文摘要
端口结构的定期结构监测对于应对盐水和碰撞环境的快速变性至关重要。但是，大多数检查是由人类在不规则基础上的视觉上进行的。为了克服并发症，已经设计了许多与传感器的基于振动的监测系统有关的研究。尽管如此，由于端口的幅度较小，很难测量环境振动，并指定了泊位的确切时机，这是主要的激发源。这项研究开发了具有高敏感加速度计M-A352的新型基于云-AI的无线传感器系统，该系统具有0.2ug/sqrt（Hz）噪声密度，0.003mg的超低噪声特征和1000Hz的采样频率。基于预定义的时间表或远程测距仪触发传感器。之后，通过AI对象检测技术（称为更快的R-CNN）对卷积部分进行旋转的RESNET网络进行检测。进一步处理检测到的锚箱的协调和大小以证明泊位船。收集的数据将通过LTE CAT 1调制解调器自动发送到10Mbps内的云服务器。该系统已在韩国的实际港口领域安装了几天，作为对拟议系统的初步研究。另外，分析了加速度，坡度和温度数据，以表明基于振动的端口条件评估的可能性。
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 installed in the actual port field in Korea for few days as a preliminary investigation of proposed system. Additionally, acceleration, slope, and temperature data are analyzed to suggest the possibility of vibration-based port condition assessment.
