Design Suggestion of Epidemic Prevention System for Shared Car Based on Scenario and Data

Entering the post-epidemic era, the travel demand for shared cars is increasing day by day. In the normalized epidemic prevention and control, epidemic prevention in shared cars needs to be designed systematically. This paper analyzes the existing risk of COVID-19 propagation based on two perspectives: scenario and data, and discusses the existing means of protection. Then based on the existing measures, the design suggestions are given from two aspects: scenario-based and data-based. Based on the scenario, the layout design and disinfection is implemented in regard to various ways that COVID-19 is transmitted;based on data, travel data integration should be promoted to achieve macro-structural dynamic adjustment and integrated governance from the overall transportation system. In the context of the industries, the shared car industry should response to new trend immediately and implement innovative ideas to obtain a service that is better suited for individuals in the post-epidemic era. In the end, several major functions of design in terms of developing the urban transportation system are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Design of Contactless Intelligent Epidemic Prevention System based on PYNQ

To combat the COVID-19 virus, extensive studies are being conducted on contactless alternative methods for measuring body temperature and detecting whether masks are being worn. This study investigated a system that employed ZYNQ-7020 as the main controller. The accurate non-contact measurement of a persons body temperature was realized by the calibration and compensation of infrared sensor data collected by an MLX90614 thermopile. In addition, OpenCV and a convolutional neural network (CNN) algorithm were used for face image recognition and to detect whether a person was wearing a mask. A mask detection algorithm was developed based on high-level synthesis and implemented by Python productivity for Zynq (PYNQ). The PYNQ test results were output through a high-definition multimedia interface. Users could also view the real-time human body temperature and face image recognition results through the web. The system achieved a temperature measurement distance of 30 cm, with a measurement error of ± 0.3 ◦C and measurement range of 2560 ◦C. The system also had an alarm function with a buzzer that could be used for an over-temperature alert. The algorithm used the characteristics of field programmable gate array parallel computing to improve the CNN calculation in image processing. A variety of optimization strategies were implemented to achieve hardware acceleration, which improved the mask recognition rate. [ FROM AUTHOR] Copyright of Engineering Letters is the property of Newswood Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)