Search Results - "stacked autoencoder SAE"

A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes by Yuan, Xiaofeng, Ou, Chen, Wang, Yalin, Yang, Chunhua, Gui, Weihua

“…•A semi-supervised autoencoder (SS-AE) is first developed as the basic network to extract quality-related features.•By hierarchically stacking multiple SS-AEs,…”
Get full text

Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE by Yuan, Xiaofeng, Ou, Chen, Wang, Yalin, Yang, Chunhua, Gui, Weihua

“…Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial…”
Get full text

Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE by Yuan, Xiaofeng, Huang, Biao, Wang, Yalin, Yang, Chunhua, Gui, Weihua

“…In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation…”
Get full text

High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder by Ma, Suliang, Chen, Mingxuan, Wu, Jianwen, Wang, Yuhao, Jia, Bowen, Jiang, Yuan

“…In recent years, machine learning techniques have been applied to test the fault type in high-voltage circuit breakers (HVCBs). Most related research involves…”
Get full text

Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application by Sun, Qingqiang, Ge, Zhiqiang

“…These days, data-driven soft sensors have been widely applied to estimate the difficult-to-measure quality variables in the industrial process. How to extract…”
Get full text

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

“…Artificial intelligence is the general trend in the field of power equipment fault diagnosis. However, limited by operation characteristics and data defects,…”
Get full text

Deep Learning-Based Classification of Hyperspectral Data by Chen, Yushi, Lin, Zhouhan, Zhao, Xing, Wang, Gang, Gu, Yanfeng

“…Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with…”
Get full text

Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts by Kao, I-Feng, Liou, Jia-Yi, Lee, Meng-Hsin, Chang, Fi-John

“…•Use Stacked Autoencoder (SAE) to reduce the dimension of regional inundation data.•Use PCA to adjust the network structure and initialize network weights of…”
Get full text

Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data by Sun, Qingqiang, Ge, Zhiqiang

“…Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness…”
Get full text

Kernel-Based Multilayer Extreme Learning Machines for Representation Learning by Wong, Chi Man, Vong, Chi Man, Wong, Pak Kin, Cao, Jiuwen

“…Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE,…”
Get full text

Stacked Fisher autoencoder for SAR change detection by Liu, Ganchao, Li, Lingling, Jiao, Licheng, Dong, Yongsheng, Li, Xuelong

“…•The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the…”
Get full text

Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application by Lei, Yongxiang, Karimi, Hamid Reza, Cen, Lihui, Chen, Xiaofang, Xie, Yongfang

“…Data-driven soft modeling has been extensively used for industrial processes to estimate key quality indicators which are hard to measure by some physical…”
Get full text

Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes by Chen, Junming, Fan, Shaosheng, Yang, Chunhua, Zhou, Can, Zhu, Hongqiu, Li, Yonggang

“…Deep learning based soft analyzers are important for modern industrial process monitoring and measurement, which aim to establish prediction models between…”
Get full text

Rotor-Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier by Cheng, Fangzhou, Wang, Jun, Qu, Liyan, Qiao, Wei

“…Fault diagnosis of drivetrain gearboxes is a prominent challenge in wind turbine condition monitoring. Many machine learning algorithms have been applied to…”
Get full text

Visualization of defects in CFRP-reinforced steel structures using improved eddy current pulsed thermography by Xie, Jing, Xu, Changhang, Wu, Changwei, Gao, Lemei, Chen, Guoming, Li, Guozhen, Song, Gangbing

“…Carbon Fiber Reinforced Plastic (CFRP) has been increasingly utilized to repair damaged steel structures, and inspection of the resulted CFRP-reinforced steel…”
Get full text

Quality-driven deep feature representation learning and its industrial application to soft sensors by Song, Xiao-Lu, Zhang, Ning, Shi, Yilin, He, Yan-Lin, Xu, Yuan, Zhu, Qun-Xiong

“…Establishing effective soft sensors relies on feature representation that is capable of capturing critical information. Stacked AutoEncoder (SAE) is able to…”
Get full text

Rock mass quality classification based on deep learning: A feasibility study for stacked autoencoders by Sheng, Danjie, Yu, Jin, Tan, Fei, Tong, Defu, Yan, Tianjun, Lv, Jiahe

“…Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable stability assessment. To develop a tool that can deliver…”
Get full text

Adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder for soft sensor modeling of industrial process by Ni, Mingming, Li, Shaojun

“…•A new feature extraction method which introduces the correlation coefficient and the dominant variable into the stacked autoencoder has been developed.•An…”
Get full text

Sensing OFDM Signal: A Deep Learning Approach by Cheng, Qingqing, Shi, Zhenguo, Nguyen, Diep N., Dutkiewicz, Eryk

“…Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of…”
Get full text

Deep learning architecture for air quality predictions by Li, Xiang, Peng, Ling, Hu, Yuan, Shao, Jing, Chi, Tianhe

“…With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens…”
Get full text