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East China Jiaotong University

EducationNanchang, China
About: East China Jiaotong University is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Resonator & Band-pass filter. The organization has 5298 authors who have published 4767 publications receiving 36749 citations.

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Journal ArticleDOI
TL;DR: This paper advocates four new deep learning models, namely, 2-D convolutional neural network, 3-D-CNN, recurrent 2- D CNN, recurrent R-2-D CNN, and recurrent 3- D-CNN for hyperspectral image classification.
Abstract: Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

307 citations

Journal ArticleDOI
TL;DR: In this article, the propagation of vibration generated by a harmonic or a constant load moving along a layered beam resting on the layered half-space is investigated theoretically in a railway track, where the ground is modelled as a number of parallel viscoelastic layers overlying an elastic half space or a rigid foundation.

302 citations

Journal ArticleDOI
TL;DR: In this paper, a dynamic computational model for the vehicle and track coupling system is developed by means of finite element method in numerical implementation, where the coupling system was divided into two parts; lower structure and upper structure.

295 citations

Journal ArticleDOI
TL;DR: Experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity, so the approach has possibility for bearing incipient fault diagnosis.
Abstract: A bearing fault diagnosis method has been proposed based on multi-scale entropy (MSE) and adaptive neuro-fuzzy inference system (ANFIS), in order to tackle the nonlinearity existing in bearing vibration as well as the uncertainty inherent in the diagnostic information. MSE refers to the calculation of entropies (e.g. appropriate entropy, sample entropy) across a sequence of scales, which takes into account not only the dynamic nonlinearity but also the interaction and coupling effects between mechanical components, thus providing much more information regarding machinery operating condition in comparison with traditional single scale-based entropy. ANFIS can benefit from the decision-making under uncertainty enabled by fuzzy logic as well as from learning and adaptation that neural networks provide. In this study, MSE and ANFIS are employed for feature extraction and fault recognition, respectively. Experiments were conducted on electrical motor bearings with three different fault categories and several levels of fault severity. The experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity. Thus, the proposed approach has possibility for bearing incipient fault diagnosis.

291 citations

Journal ArticleDOI
TL;DR: In this article, a conductive nanocomposite hydrogel comprised of oxidized multi-walled carbon nanotubes (oxCNTs) and polyacrylamide (PAAm) is developed.

268 citations


Showing all 5335 results

Ming Zhou8871328405
Ji-Huan He8747741081
Xiangdong Yao6923515163
Zhijun Zhang6436218147
Zheng-Guang Wu6328412968
Xiang-Yang Liu6241312600
Xiangyu Wang5944912630
Gang Wei491646615
Guangming Xie463128107
Zao Yi421684343
Yizao Wan391824774
Huaicheng Yan351664128
Zhigang Deng341644510
Wenjun Luo341056328
Fanglian Yao331012837
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No. of papers from the Institution in previous years