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Institution

Xi'an Jiaotong University

EducationXi'an, China
About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.


Papers
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Journal ArticleDOI
TL;DR: The experimental results show that the proposed deep separable convolutional network (DSCN) is able to provide accurate RUL prediction results based on the raw multi-sensor data and is superior to some existing data-driven prognostics approaches.

206 citations

Book ChapterDOI
01 Jan 2018
TL;DR: This chapter provides with informative clues and tips associated with clinical features and pathological presentations and keeps pace with the latest development in molecular biology and immunohistochemistry to assist the readers in the establishment of accurate diagnosis.
Abstract: Skin tumor is the most intriguing topic for the clinicians. This chapter covers a wide range of issues and provides with informative clues and tips associated with clinical features and pathological presentations and keeps pace with the latest development in molecular biology and immunohistochemistry to assist the readers in the establishment of accurate diagnosis. These following disorders are elaborately selected and arranged from a histopathological perspective.

206 citations

Journal ArticleDOI
TL;DR: A variant long-short-term memory (LSTM) neural network (NN), called AST-L STM NN, is designed to guarantee the performance of proposed prognostic framework, and is well-trained separately for the prediction of SOH and RUL.

206 citations

Journal ArticleDOI
TL;DR: It is proved that an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks, can approximate any Lebesgue p-integrable function on a compact input set.
Abstract: Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.

206 citations

Journal ArticleDOI
TL;DR: In this article, a tentative mechanism for the enhanced photocatalysis of the SnO 2 /ZnO/TiO 2 composite catalyst has been proposed, which could be attributed to the increased separation of the charge carriers, which depress the charge pair recombination and prolonged the electron lifetime in the composite structure.

206 citations


Authors

Showing all 86109 results

NameH-indexPapersCitations
Feng Zhang1721278181865
Yang Yang1642704144071
Jian Yang1421818111166
Lei Zhang130231286950
Yang Liu1292506122380
Jian Zhou128300791402
Chao Zhang127311984711
Bin Wang126222674364
Xin Wang121150364930
Bo Wang119290584863
Xuan Zhang119153065398
Jian Liu117209073156
Andrey L. Rogach11757646820
Yadong Yin11543164401
Xin Li114277871389
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023306
20221,657
202111,508
202011,183
201910,012
20188,215