Name
Title | ||
|---|---|---|
Standard for the Quantification of a Sterilization Effect Using an Artificial Intelligence Disinfection Robot |
Abstract | ||
|---|---|---|
Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21237776 | SENSORS |
Keywords | DocType | Volume |
COVID-19, deep learning, disinfection robot, object detection, ultraviolet disinfection (UVD) | Journal | 21 |
Issue | ISSN | Citations |
23 | 1424-8220 | 1 |
PageRank | References | Authors |
0.37 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heeju Hong | 1 | 1 | 0.37 |
| WonKook Shin | 2 | 1 | 0.37 |
| Jieun Oh | 3 | 1 | 0.37 |
| SunWoo Lee | 4 | 1 | 0.37 |
| TaeYoung Kim | 5 | 1 | 0.37 |
| WooSub Lee | 6 | 1 | 0.37 |
| JongSuk Choi | 7 | 1 | 0.37 |
| SeungBeum Suh | 8 | 1 | 0.37 |
| KangGeon Kim | 9 | 1 | 0.37 |