Southeast University

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

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

Abstract: In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.

Facile Photochemical Synthesis of Au/Pt/g-C3N4 with Plasmon-Enhanced Photocatalytic Activity for Antibiotic Degradation

Abstract: A novel plasmonic photocatalyst, Au/Pt/g-C3N4, was prepared by a facile calcination-photodeposition technique. The samples were characterized by X-ray diffraction, energy-dispersive spectroscopy, transmission electron microscopy, and UV–vis diffuse reflectance spectroscopy, and the results demonstrated that the Au and Pt nanoparticles (7–15 nm) were well-dispersed on the surfaces of g-C3N4. The Au/Pt codecorated g-C3N4 heterostructure displayed enhanced photocatalytic activity for antibiotic tetracycline hydrochloride (TC-HCl) degradation, and the degradation rate was 3.4 times higher than that of pure g-C3N4 under visible light irradiation. The enhancement of photocatalytic activity could be attributed to the surface plasmon resonance effect of Au and electron-sink function of Pt nanoparticles, which improve the optical absorption property and photogenerated charge carriers separation of g-C3N4, synergistically facilitating the photocatalysis process. Finally, a possible photocatalytic mechanism for degr...

Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information

Abstract: Hesitant fuzzy set (HFS), which allows the membership degree of an element to a set represented by several possible values, is considered as a powerful tool to express uncertain information in the process of multi-attribute decision making (MADM) problems. In this paper, we develop a novel approach based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in hesitant fuzzy elements and the information about attribute weights is incomplete. There are two key issues being addressed in this approach. The first one is to establish an optimization model based on the maximizing deviation method, which can be used to determine the attribute weights. According to the idea of the TOPSIS of Hwang and Yoon [1], the second one is to calculate the relative closeness coefficient of each alternative to the hesitant positive-ideal solution, based on which the considered alternatives are ranked and then the most desirable one is selected. An energy policy selection problem is used to illustrate the detailed implementation process of the proposed approach, and demonstrate its validity and applicability. Finally, the extended results in interval-valued hesitant fuzzy situations are also pointed out.

Consensus of Multi-Agent Systems With Diverse Input and Communication Delays

Abstract: The consensus problem for multi-agent systems with input and communication delays is studied based on the frequency-domain analysis. Two decentralized consensus conditions are obtained, one of which is given for the systems based on undirected graphs with diverse input delays and the other is for the systems based on directed graphs with diverse communication delays and input delays. For the systems with both communication delays and input delays, the consensus condition is dependent on input delays but independent of communication delays.