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.

Distributed Tracking of Nonlinear Multiagent Systems Under Directed Switching Topology: An Observer-Based Protocol

Abstract: This paper deals with a consensus tracking problem for multiagent systems (MASs) with Lipschitz-type nonlinear dynamics and directed switching topology. Unlike most existing works where the relative full state measurements of neighboring agents are utilized, it is assumed that only the relative output measurements of neighboring agents are available for coordination. To achieve consensus tracking in the considered MASs, a new class of observer-based protocols is proposed. By appropriately constructing some topology-dependent multiple Lyapunov functions, it is theoretically shown that distributed consensus tracking in the closed-loop MASs equipped with the designed protocols can be ensured if each possible topology contains a directed spanning tree rooted at the leader and the dwell time for the switchings among different topology is less than a derived positive quantity. Interestingly, it is found that the communication topology for observers’ states may be independent with that of the feedback signals. The derived results are further extended to the case of directed switching topology with only average dwell time constraints. Finally, the effectiveness of the analytical results is demonstrated via numerical simulations.

Low-temperature plasma-enhanced atomic layer deposition of tin oxide electron selective layers for highly efficient planar perovskite solar cells

Abstract: Recent progress has shown that low-temperature processed tin oxide (SnO2) is an excellent electron selective layer (ESL) material for fabricating highly efficient organic–inorganic metal-halide perovskite solar cells with a planar cell structure. Low-temperature processing and a planar cell structure are desirable characteristics for large-scale device manufacturing due to their associated low costs and processing simplicity. Here, we report that plasma-enhanced atomic layer deposition (PEALD) is able to lower the deposition temperature of SnO2 ESLs to below 100 °C and still achieve high device performance. With C60-self-assembled monolayer passivation, our PEALD SnO2 ESLs deposited at ∼100 °C led to average power conversion efficiencies higher than 18% (maximum of 19.03%) and 15% (maximum of 16.80%) under reverse voltage scan for solar cells fabricated on glass and flexible polymer substrates, respectively. Our results thus demonstrate the potential of the low-temperature PEALD process of SnO2 ESLs for large-scale manufacturing of efficient perovskite solar cells.

DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Abstract: Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.

Three-Dimensional Hierarchical Architectures Derived from Surface-Mounted Metal-Organic Framework Membranes for Enhanced Electrocatalysis.

Abstract: Inspired by the rapid development of metal-organic-framework-derived materials in various applications, a facile synthetic strategy was developed for fabrication of 3D hierarchical nanoarchitectures. A surface-mounted metal-organic framework membrane was pyrolyzed at a range of temperatures to produce catalysts with excellent trifunctional electrocatalytic efficiencies for the oxygen reduction, hydrogen evolution, and oxygen evolution reactions.