Data Mode Related Interpretable Transformer Network for Predictive Modeling and Key Sample Analysis in Industrial Processes
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[1] W. Gui,et al. Cloud-Edge Collaborative Method for Industrial Process Monitoring Based on Error-Triggered Dictionary Learning , 2022, IEEE Transactions on Industrial Informatics.
[2] Chunjie Yang,et al. DSTED: A Denoising Spatial–Temporal Encoder–Decoder Framework for Multistep Prediction of Burn-Through Point in Sintering Process , 2022, IEEE Transactions on Industrial Electronics.
[3] Yonggang Li,et al. MPA-RNN: A Novel Attention-Based Recurrent Neural Networks for Total Nitrogen Prediction , 2022, IEEE Transactions on Industrial Informatics.
[4] Xiaofeng Yuan,et al. Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application , 2022, IEEE Transactions on Industrial Informatics.
[5] Jinliang Ding,et al. High-Dimensional Data Global Sensitivity Analysis Based on Deep Soft Sensor Model , 2022, IEEE Transactions on Cybernetics.
[6] Xiaofeng Yuan,et al. Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling , 2022, Adv. Eng. Informatics.
[7] Tingwen Huang,et al. A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals With An Imbalanced Data Structure , 2022, IEEE Transactions on Industrial Electronics.
[8] Han Liu,et al. A Hybrid Mechanism- and Data-Driven Soft Sensor Based on the Generative Adversarial Network and Gated Recurrent Unit , 2021, IEEE Sensors Journal.
[9] Jianmin Wang,et al. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting , 2021, NeurIPS.
[10] Yuan Lin,et al. An Improved JITL Method for Soft Sensing of Multimodal Industrial Processes for Search Efficiency , 2021, Journal of Physics: Conference Series.
[11] Gilberto Reynoso-Meza,et al. Feature selection and regularization of interpretable soft sensors using evolutionary multi-objective optimization design procedures , 2021 .
[12] Xiaochen Hao,et al. Online cement clinker quality monitoring: A soft sensor model based on multivariate time series analysis and CNN. , 2021, ISA transactions.
[13] Xiaofeng Yuan,et al. A Just-in-Time Fine-Tuning Framework for Deep Learning of SAE in Adaptive Data-Driven Modeling of Time-Varying Industrial Processes , 2021, IEEE Sensors Journal.
[14] Enrico Zio,et al. Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants , 2021 .
[15] Zhiqiang Ge,et al. A Survey on Deep Learning for Data-Driven Soft Sensors , 2021, IEEE Transactions on Industrial Informatics.
[16] Hui Xiong,et al. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting , 2020, AAAI.
[17] Anuradha Bhamidipaty,et al. A Transformer-based Framework for Multivariate Time Series Representation Learning , 2020, KDD.
[18] Henry Leung,et al. Multiseries Featural LSTM for Partial Periodic Time-Series Prediction: A Case Study for Steel Industry , 2020, IEEE Transactions on Instrumentation and Measurement.
[19] Zhiqiang Ge,et al. Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application , 2020, IEEE Transactions on Cybernetics.
[20] Rodolfo C.C. Flesch,et al. Data-Driven Soft Sensor for the Estimation of Sound Power Levels of Refrigeration Compressors Through Vibration Measurements , 2020, IEEE Transactions on Industrial Electronics.
[21] Lin Li,et al. Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network , 2020, IEEE Transactions on Industrial Informatics.
[22] Xiaofeng Yuan,et al. Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development , 2020, IEEE Transactions on Industrial Electronics.
[23] ChangKyoo Yoo,et al. Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders , 2020 .
[24] Yongfang Xie,et al. A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification , 2020, Neurocomputing.
[25] Biao Huang,et al. A deep learning just-in-time modeling approach for soft sensor based on variational autoencoder , 2020 .
[26] D. Burbidge,et al. Central , 2020, The Oxford Handbook of Kenyan Politics.
[27] Jinjun Xiong,et al. On Interpretability of Artificial Neural Networks: A Survey , 2020, IEEE Transactions on Radiation and Plasma Medical Sciences.
[28] Bo Sun,et al. A method for detecting high-frequency oscillations using semi-supervised k-means and mean shift clustering , 2019, Neurocomputing.
[29] Wenhu Chen,et al. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting , 2019, NeurIPS.
[30] K. Kvaal,et al. Explicit and interpretable nonlinear soft sensor models for influent surveillance at a full-scale wastewater treatment plant , 2019, Journal of Process Control.
[31] Jafar Sadeghi,et al. Soft Sensor Modeling Based on Multi-State-Dependent Parameter Models and Application for Quality Monitoring in Industrial Sulfur Recovery Process , 2018, IEEE Sensors Journal.
[32] Biao Huang,et al. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE , 2018, IEEE Transactions on Industrial Informatics.
[33] Quanshi Zhang,et al. Visual interpretability for deep learning: a survey , 2018, Frontiers of Information Technology & Electronic Engineering.
[34] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[35] Guokun Lai,et al. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.
[36] Wen Tan,et al. Process Monitoring for Multimodal Processes With Mode-Reachability Constraints , 2017, IEEE Transactions on Industrial Electronics.
[37] Eva Patel,et al. Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model , 2020 .
[38] Xiaofei Yang,et al. A new similarity combining reconstruction coefficient with pairwise distance for agglomerative clustering , 2020, Inf. Sci..
[39] P. Antsaklis. INTELLIGENT CONTROL , 1994 .