Search Results - stacked sparse autoencoder–based deep neural network

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network by Sohaib, Muhammad, Kim, Jong-Myon

“… In this study, using complex envelope spectra and stacked sparse autoencoder…”
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Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network by Han, Zhezhe, Hossain, Md. Moinul, Wang, Yuwei, Li, Jian, Xu, Chuanlong

“…•A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved…”
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Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network by Nguyen, Cong Dai, Prosvirin, Alexander E., Kim, Cheol Hong, Kim, Jong-Myon

“…Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration…”
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Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery by Qi, Yumei, Shen, Changqing, Wang, Dong, Shi, Juanjuan, Jiang, Xingxing, Zhu, Zhongkui

“… Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper…”
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Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification by Vijay Anand, R., Magesh, G., Alagiri, I., Brahmam, Madala Guru, Senthil Kumar, C., Kesavan, M., Abdullah, Azween Bin

“…ABSTRACT In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process…”
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A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction by Deng, Lei, Fan, Chao, Zeng, Zhiwen

“… Results In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based…”
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Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images by Xing, Chen, Yang, Xiaoquan, Ma, Li

“… Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning…”
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Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review by Yang, Zheng, Xu, Binbin, Luo, Wei, Chen, Fei

“… In the past decades, the vigorous development of deep learning (DL) brings new opportunities for IFD, especially the representation learning based on Autoencoder (AE…”
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An efficient method for autoencoder‐based collaborative filtering by Wang, Yi‐Lei, Tang, Wen‐Zhe, Yang, Xian‐Jun, Wu, Ying‐Jie, Chen, Fu‐Ji

“… With rapid development in deep learning, neural network‐based CF models have gained great attention in the recent years, especially autoencoder‐based CF model…”
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Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder by Coutinho, Maria G. F., Torquato, Matheus F., Fernandes, Marcelo A. C.

“… Therefore, the objective of this paper is to propose a neural network hardware implementation to be used in deep learning applications…”
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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network by Aslam, Muhammad Aqeel, Xue, Cuili, Chen, Yunsheng, Zhang, Amin, Liu, Manhua, Wang, Kan, Cui, Daxiang

“… In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples…”
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Deep neural network for semi-automatic classification of term and preterm uterine recordings by Chen, Lili, Xu, Huoyao

“… For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based…”
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Optimizing MobileNetV2 for improved accuracy in early gastric cancer detection based on dynamic pelican optimizer by Zhou, Guoping, He, Qiyu, Liu, Xiaoli, Kai, Xinghua, Cao, Weikang, Ding, Junning, Zhuang, Bufeng, Xu, Shuhua, Thwin, Myo

“… The proposed approach utilizes a customized deep learning model called MobileNetV2, which is optimized using a Dynamic variant of the Pelican Optimization Algorithm (DPOA…”
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SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines by Xiao, Xiaocong, Liu, Jianxun, Liu, Deshun, Tang, Yufei, Dai, Juchuan, Zhang, Fan

“… To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP…”
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A stacked sparse autoencoder based architecture for Punjabi and English spoken language classification using MFCC features by Arora, Vaibhav, Sood, Pulkit, Keshari, Kumar Utkarsh

“… A number of shallow architectures namely Soft-max classifier, SVM and deep architectures namely Artificial Neural Networks, SVM with Sparse Auto encoder and Softmax with sparse auto encoder…”
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Rock mass type prediction for tunnel boring machine using a novel semi-supervised method by Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Xiao, Dengyu, Sun, Hao, Liu, Chengliang

“…•A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor…”
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Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia by Kim, Junghoe, Calhoun, Vince D., Shim, Eunsoo, Lee, Jong-Hwan

“…). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech…”
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Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning by Sun, Jiedi, Yan, Changhong, Wen, Jiangtao

“… Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines…”
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Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features by Ahmed, H.O.A., Wong, M.L.D., Nandi, A.K.

“…•Uses compressive sensing and sparse over-complete feature learning.•Uses the unsupervised sparse autoencoder for learning feature representations…”
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The Deep Neural Network Based Classification of Fingers Pattern Using Electromyography by Ahmad, Jawad, Butt, Ammar Mohsin, Hussain, Mohsin, Akbar, Muhammad Azeem, Rehman, Waheed Ur

“…) is used to extract a total of 500 feature vectors from five fingers that are used to train an autoencoder based five-layered Deep Neural Network (DNN…”
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