Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Optimization method for forecasting confirmed cases of COVID-19 in China

Abstract: In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios

Abstract: Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.

Dendrite-free lithium deposition with self-aligned nanorod structure.

Abstract: Suppressing lithium (Li) dendrite growth is one of the most critical challenges for the development of Li metal batteries. Here, we report for the first time the growth of dendrite-free lithium films with a self-aligned and highly compacted nanorod structure when the film was deposited in the electrolyte consisting of 1.0 M LiPF6 in propylene carbonate with 0.05 M CsPF6 as an additive. Evolution of both the surface and the cross-sectional morphologies of the Li films during repeated Li deposition/stripping processes were systematically investigated. It is found that the formation of the compact Li nanorod structure is preceded by a solid electrolyte interphase (SEI) layer formed on the surface of the substrate. Electrochemical analysis indicates that an initial reduction process occurred at ∼2.05 V vs Li/Li+ before Li deposition is responsible for the formation of the initial SEI, while the X-ray photoelectron spectroscopy indicates that the presence of CsPF6 additive can largely enhance the formation of ...