Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Computer science & MIMO. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Clinical findings of patients with coronavirus disease 2019 in Jiangsu province, China: A retrospective, multi-center study.

Abstract: Limited data are available for clinical characteristics of patients with coronavirus disease 2019 (COVID-19) outside Wuhan. This study aimed to describe the clinical characteristics of COVID-19 and identify the risk factors for severe illness of COVID-19 in Jiangsu province, China. Clinical data of hospitalized COVID-19 patients were retrospectively collected in 8 hospitals from 8 cities of Jiangsu province, China. Clinical findings of COVID-19 patients were described and risk factors for severe illness of COVID-19 were analyzed. By Feb 10, 2020, 202 hospitalized patients with COVID-19 were enrolled. The median age of patients was 44.0 years (interquartile range, 33.0-54.0). 55 (27.2%) patients had comorbidities. At the onset of illness, the common symptoms were fever (156 [77.2%]) and cough (120 [59.4%]). 66 (32.7%) patients had lymphopenia. 193 (95.5%) patients had abnormal radiological findings. 11 (5.4%) patients were admitted to the intensive care unit and none of the patients died. 23 (11.4%) patients had severe illness. Severe illness of COVID-19 was independently associated with body mass index (BMI) ≥ 28 kg/m2 (odds ratio [OR], 9.219; 95% confidence interval [CI], 2.731 to 31.126; P<0.001) and a known history of type 2 diabetes (OR, 4.326; 95% CI, 1.059 to 17.668; P = 0.041). In this case series in Jiangsu Province, COVID-19 patients had less severe symptoms and had better outcomes than the initial COVID-19 patients in Wuhan. The BMI ≥ 28 kg/m2 and a known history of type 2 diabetes were independent risk factors of severe illness in patients with COVID-19.

Linear Parameter-Varying Controller Design for Four-Wheel Independently Actuated Electric Ground Vehicles With Active Steering Systems

Abstract: This paper presents a linear parameter-varying (LPV) control strategy to preserve stability and improve handling of a four-wheel independently actuated electric ground vehicle in spite of in-wheel motors and/or steering system faults. Different types of actuator faults including loss-of-effectiveness fault, additive fault, and the fault makes an actuator's control effect stuck-at-fixed-level, are considered simultaneously. To attenuate the effects of disturbance and address the challenging problem, a novel fault-tolerant (FT) robust linear quadratic regulator (LQR)-based $H_{\infty}$ controller using the LPV method is proposed. With the LQR-based $H_{\infty}$ control, the tradeoff between the tracking performance and the control input energy is achieved, and the effect from the external disturbance to the controlled outputs is minimized. The eigenvalue positions of the system matrix of the closed-loop system are also incorporated to tradeoff between the control inputs and the transient responses. The vehicle states, including vehicle yaw rate, lateral and longitudinal velocities, are simultaneously controlled to track their respective references. Simulations for different fault types and various driving scenarios are carried out with a high-fidelity, CarSim®, full-vehicle model. Simulation results show the effectiveness of the proposed FT control approach.

Semantic, Hierarchical, Online Clustering of Web Search Results

Abstract: We propose a Semantic, Hierarchical, Online Clustering (SHOC) approach to automatically organizing Web search results into groups. SHOC combines the power of two novel techniques, key phrase discovery and orthogonal clustering, to generate clusters which are both reasonable and readable. Moreover, SHOC can work for multiple languages: not only English but also oriental languages like Chinese. The main contribution of this paper includes the following. (1) The benefits of using key phrases as Web document features are discussed. A key phrase discovery algorithm based on suffix array is presented. This algorithm is highly effective and efficient no matter how large the language’s alphabet is. (2) The concept of orthogonal clustering is proposed for general clustering problems. The reason why matrix Singular Value Decomposition (SVD) can provide solution to orthogonal clustering is strictly proved. The orthogonal clustering has a solid mathematics foundation and many advantages over traditional heuristic clustering algorithms.

Size Dependence of Defect-Induced Room Temperature Ferromagnetism in Undoped ZnO Nanoparticles

Abstract: We report the intrinsic room-temperature ferromagnetism in undoped ZnO nanoparticles with different sizes synthesized by a wet chemical method at different temperatures. Electron paramagnetic resonance, X-ray photoelectron spectroscopy, and photoluminescence measurements demonstrate clearly the singly charged oxygen vacancies are the main defects, and the relative occupancy of that decreases with increasing sizes and annealing temperatures. Importantly, a direct correlation between the ferromagnetism and the relative concentration of the singly charged oxygen vacancies is established, which suggests that the singly charged oxygen vacancies play a crucial role in modulating ferromagnetic behaviors. Moreover, the size-dependent ferromagnetism can be manipulated conveniently by changing of the surface–volume ratio, which is in favor of future electronic and spintronic application.