ABSTRACT
SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.
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.