Search Results - stacked sparse autoencoder((sae OR sage))

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS by Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng

“…Background While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy…”
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A Stacked Autoencoder With Sparse Bayesian Regression for End-Point Prediction Problems in Steelmaking Process by Liu, Chang, Tang, Lixin, Liu, Jiyin

“… Considering the importance of data representation in modeling, the original data are inputted to a stacked autoencoder (SAE…”
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Building feature space of extreme learning machine with sparse denoising stacked-autoencoder by Cao, Le-le, Huang, Wen-bing, Sun, Fu-chun

“… Deep learning algorithms such as stacked autoencoder (SAE) and deep belief network (DBN) are built on learning several levels of representation of the input…”
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Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders by Esmaeiloghli, Saeid, Lima, Alexandre, Sadeghi, Behnam

“… Robust sparse…”
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A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals With An Imbalanced Data Structure by Yang, Dongsheng, Qin, Jia, Pang, Yongheng, Huang, Tingwen

“… To fill this research gap, in this article, a novel double-stacked autoencoder (DSAE) is proposed for a fast and accurate judgment of power transformer health conditions with an imbalanced data structure…”
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A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings by Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng

“… As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data…”
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Ischemic stroke lesion segmentation using stacked sparse autoencoder by Praveen, G.B., Agrawal, Anita, Sundaram, Ponraj, Sardesai, Sanjay

“… In this work, we propose an unsupervised featured learning approach based on stacked sparse autoencoder (SSAE…”
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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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Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier by Liu, Jinping, Xu, Longcheng, Xie, Yongfang, Ma, Tianyu, Wang, Jie, Tang, Zhaohui, Gui, Weihua, Yin, Huazhan, Jahanshahi, Hadi

“…This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE…”
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SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares by Ye, Zhenyu, Wu, Ping, He, Yuchen, Pan, Haipeng

“…‐related process monitoring method via integrating stacked sparse autoencoder (SSAE) with KPLS (SSAE‐KPLS). First, an SSAE model is employed to exploit the nonlinearity within process variables…”
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Novel semi-supervised sparse stacked autoencoder integrated with local linear embedding for industrial soft sensing by He, Yan-Lin, Jiang, Yu, Gao, Hui-Hui, Xu, Yuan, Zhu, Qun-Xiong

“…, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE…”
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Sparse stacked autoencoder network for complex system monitoring with industrial applications by Deng, Ziwei, Li, Yuxuan, Zhu, Hongqiu, Huang, Keke, Tang, Zhaohui, Wang, Zhen

“…•A fault classification and isolation method based on SAE is proposed for complex system…”
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Embedded stacked group sparse autoencoder ensemble with L1 regularization and manifold reduction by Li, Yongming, Lei, Yan, Wang, Pin, Jiang, Mingfeng, Liu, Yuchuan

“… Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful tools that learn deep features from original features, so they are popular for classification problems…”
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Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis by Jabir, Brahim, Nadif, Bendaoud, Diez, Isabel De la Torre, Garay, Helena, Noya, Irene Delgado

“…: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE…”
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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis by Luo, Shuyang, Huang, Xufeng, Wang, Yanzhi, Luo, Rongmin, Zhou, Qi

“… Stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm…”
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Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection by Zhao, Chunhui, Zhang, Lili

“… Second, stacked autoencoders (SAE) are employed on the sparse matrix for spectral deep features and on the low-rank matrix for spatial deep features, respectively…”
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Six Phase Transmission Line Protection Using Bat Algorithm Tuned Stacked Sparse Autoencoder by Rao Althi, Tirupathi, Koley, Ebha, Ghosh, Subhojit, Shukla, Sunil Kumar

“…Six-phase transmission lines have the capability to address the continually evolving power demand. It allows upgrading the power transfer capability of the…”
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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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Classification of Power Quality Disturbances via Deep Learning by Ma, Jian, Zhang, Jun, Xiao, Luxin, Chen, Kexu, Wu, Jianhua

“…). Stacked autoencoder, as a deep learning framework, is employed to extract high-level features of PQDs for classification…”
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K-Means Clustering Optimizing Deep Stacked Sparse Autoencoder by Bi, Yandong, Wang, Peng, Guo, Xuchao, Wang, Zhijun, Cheng, Shuhan

“… How to speed up training is a problem deserving of study. In order to accelerate training, K-means clustering optimizing deep stacked sparse autoencoder (K-means sparse SAE…”
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