Journal ArticleDOI
A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition
Chengjin Qin,Honggan Yu,Jianfeng Tao,Chengjin Qin,Liu Mingyang,Dengyu Xiao,Hao Sun,Chengliang Liu +7 more
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TLDR
In-situ data collected from a Singapore project (stacked twin bored tunnels) was used to prove the superiority of the proposed constrained dense convolutional autoencoder and DNN-based semi-supervised method.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2022-02-15. It has received 40 citations till now. The article focuses on the topics: Autoencoder & Computer science.read more
Citations
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Journal ArticleDOI
An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine
Chengjin Qin,Gang Shi,Jianfeng Tao,Hongbin Yu,Yanrui Jin,Dengyu Xiao,Zhinan Zhang,Chengliang Liu +7 more
TL;DR: Wang et al. as discussed by the authors proposed an adaptive hierarchical decomposition-based method (AHDM) for multi-step forecast of cutterhead torque, where only original torque signal is utilized as input and is decomposed adaptively to reduce its complexity and improve the forecast performance under complex geological environment and working conditions.
Journal ArticleDOI
Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring
TL;DR: In this article , a monitoring approach based on concentrated velocity synchronous linear chirplet transform (CVSLCT) is proposed for robotic drilling chatter, where the acceleration signals are compartmentalized into several bands of equal frequency width.
Journal ArticleDOI
A Novel Interpretable Method Based on Dual-Level Attentional Deep Neural Network for Actual Multilabel Arrhythmia Detection
Yanrui Jin,Jinlei Liu,Yunqing Liu,Chengjin Qin,Zhiyuan Li,Dengyu Xiao,Liqun Zhao,Chengliang Liu +7 more
TL;DR: An accurate and interpretable model for multilabel ECG signals, called dual-level attentional convolutional long short-term memory neural network (DLA-CLSTM), which can improve the accuracy by 22.50% and the F1-macro by 20.51% on average.
Journal ArticleDOI
Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides
Stefan Abela,Sharon Anderson +1 more
TL;DR: In this paper , the authors developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA), for spatially explicit prediction of landslide susceptibility.
Journal ArticleDOI
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
TL;DR: In this paper , a semi-supervised learning approach is proposed to detect and diagnose unseen and unknown faults in gear systems using a deep convolutional generative adversarial network (DCGAN).
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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ImageNet classification with deep convolutional neural networks
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