Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Single-atomic cobalt sites embedded in hierarchically ordered porous nitrogen-doped carbon as a superior bifunctional electrocatalyst.

Abstract: Exploring efficient and cost-effective catalysts to replace precious metal catalysts, such as Pt, for electrocatalytic oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER) holds great promise for renewable energy technologies. Herein, we prepare a type of Co catalyst with single-atomic Co sites embedded in hierarchically ordered porous N-doped carbon (Co-SAS/HOPNC) through a facile dual-template cooperative pyrolysis approach. The desirable combination of highly dispersed isolated atomic Co-N4 active sites, large surface area, high porosity, and good conductivity gives rise to an excellent catalytic performance. The catalyst exhibits outstanding performance for ORR in alkaline medium with a half-wave potential (E1/2) of 0.892 V, which is 53 mV more positive than that of Pt/C, as well as a high tolerance of methanol and great stability. The catalyst also shows a remarkable catalytic performance for HER with distinctly high turnover frequencies of 0.41 and 3.8 s−1 at an overpotential of 100 and 200 mV, respectively, together with a long-term durability in acidic condition. Experiments and density functional theory (DFT) calculations reveal that the atomically isolated single Co sites and the structural advantages of the unique 3D hierarchical porous architecture synergistically contribute to the high catalytic activity.

A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method

Abstract: A novel FMEA approach is proposed using combination weighting and fuzzy VIKOR.Combination of fuzzy AHP and entropy method is applied for risk factor weighting.Fuzzy VIKOR method is used to determine the risk priorities of failure modes.An empirical example is offered to illustrate the effectiveness of the new method. Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans' subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.

Synchronization Control for A Class of Discrete Time-Delay Complex Dynamical Networks: A Dynamic Event-Triggered Approach

Abstract: This paper is concerned with the synchronization control problem for a class of discrete time-delay complex dynamical networks under a dynamic event-triggered mechanism. For the efficiency of energy utilization, we make the first attempt to introduce a dynamic event-triggering strategy into the design of synchronization controllers for complex dynamical networks. A new discrete-time version of the dynamic event-triggering mechanism is proposed in terms of the absolute errors between control input updates. By constructing an appropriate Lyapunov functional, the dynamics of each network node combined with the introduced event-triggering mechanism are first analyzed, and a sufficient condition is then provided under which the synchronization error dynamics is exponentially ultimately bounded. Subsequently, a set of the desired synchronization controllers is designed by solving a matrix inequality. Finally, a simulation example is provided to verify the effectiveness of the proposed dynamic event-triggered synchronization control scheme.

Cross-Modal Self-Attention Network for Referring Image Segmentation

Abstract: We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their representations. They do not sufficiently capture long-range correlations between these two modalities. In this paper, we propose a cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the input image. In addition, we propose a gated multi-level fusion module to selectively integrate self-attentive cross-modal features corresponding to different levels in the image. This module controls the information flow of features at different levels. We validate the proposed approach on four evaluation datasets. Our proposed approach consistently outperforms existing state-of-the-art methods.