| （英） |
In the manufacturing industry, smart factories that autonomously aggregate and analyze information and optimize production and inspection line through cyber-physical systems are attracting attention.In smart factories, it is important to accurately recognise the indoor wireless environment to aggregate information from sensors and automated guided vehicles with high accuracy.However, in a smart factory, various obstacles and metal objects, including machinery, robots, automated vehicles, and humans, cause complex fluctuations in the wireless environment.In this study, we conducted fixed-point monitoring experiments on the reception power and communication quality of local 5G within a smart factory. We analyzed the impact of fluctuations in received power caused by moving objects such as human flow and robots, as well as metal objects like machinery, and conducted propagation estimation using machine learning. |