ABSTRACT
The pandemic of COVID-19, which has been the current global challenge, seemed to have shattered all the trust and hope in the current technologies. Millions have been affected and yet the technologist couldn't defend the onslaught mostly due to the massiveness and quickness of the spread. Even though the vaccination is in eyesight, still, survival is a tedious task due to poor clarity about the whereabouts of the infected and poor monitoring systems. Internet of Things (IoT) has revolutionized the health care system and can be useful for proper monitoring of COVID-19 patients, by employing an interconnected network to invigorate the Internet of Medical things. In this study, we explore the possibilities of the current IoT techniques in monitoring and guiding the patients. This in turn can help the uninfected to be informed and guided to keep tracking their own movement with respect to the infected. This will help the readers in identifying varieties of IoMT applications that can be extended in monitoring the COVID-19 infected patients. Hence, this article will motivate the thriving researchers to innovate their ideas over it, ultimately helping the society in fighting the pandemic situation. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
The accelerated global progress in the research and development of automobile products, and the use of new technologies, such as the Internet, cloud computing and big data, to coordinate development platforms in different regions and fields, can reduce the duration and cost of development and testing. Specifically, in the context of the current coronavirus disease (COVID-19) pandemic, which has caused great obstacles to normal logistics and transportation, personnel exchanges and information communication, platforms that can support global operation are significant for product testing and validation, because they eliminate the need for the transportation of personnel and equipment. Therefore, the establishment of a distributed test and validation platform for automotive powertrain systems, which can integrate software and hardware testing, is important in terms of both scientific research and industrialization. The main technical difficulties associated with such test and validation platforms include data transmission and the control of the transmission effect. A distributed test and validation platform for a fuel cell electric vehicle powertrain system is proposed herein. The two-time-scale Markov chain is used to simulate the delay between two places (China and Germany), and the least-squares support vector machine (LSSVM) method is used to optimize the transmission effect. The results show that the two-time-scale Markov chain model can effectively simulate the delay between two nations, and that its probability distribution is close to the measured value. The LSSVM method is effectively optimized for all four indicators (velocity, fuel cell output power, battery output power and electric motor output torque). This method can be effectively used in the remote development test validation of vehicle powertrain system. © 2021 SAE Technical Papers. All rights reserved.