Transfer Learning in wastewater treatment plants control: Measuring the transfer suitability
暂无分享,去创建一个
[1] B. Cai,et al. Fault Diagnosis Methodology of Redundant Closed-Loop Feedback Control Systems: Subsea Blowout Preventer System as a Case Study , 2023, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[2] J. Vicario,et al. Transfer Learning Suitability Metric for ANN-based Industrial Controllers , 2022, 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA).
[3] Gai-ge Wang,et al. Architecture evolution of convolutional neural network using monarch butterfly optimization , 2022, Journal of Ambient Intelligence and Humanized Computing.
[4] Shuying Lai,et al. A Hybrid Cloud and Edge Control Strategy for Demand Responses Using Deep Reinforcement Learning and Transfer Learning , 2022, IEEE Transactions on Cloud Computing.
[5] Guido De Roeck,et al. An efficient stochastic-based coupled model for damage identification in plate structures , 2021, Engineering Failure Analysis.
[6] Houda Labiod,et al. Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis , 2021, 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA).
[7] Ramón Vilanova,et al. Transfer Learning Approach for the Design of Basic Control Loops in Wastewater Treatment Plants , 2021, 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ).
[8] Ramón Vilanova,et al. Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers , 2021, Sensors.
[9] Baoping Cai,et al. Data-driven early fault diagnostic methodology of permanent magnet synchronous motor , 2021, Expert Syst. Appl..
[10] Enda Barrett,et al. Transfer learning applied to DRL-Based heat pump control to leverage microgrid energy efficiency , 2021, Smart Energy.
[11] Magd Abdel Wahab,et al. A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks , 2021, Computers & Structures.
[12] Ramon Vilanova,et al. Control of high-order processes: repeated-pole plus dead-time models' identification , 2021, International Journal of Control.
[13] Magd Abdel Wahab,et al. An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates , 2021 .
[14] Paulo Novais,et al. Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities , 2021, Electronics.
[15] Sai Tang,et al. Model Predictive Control Using Artificial Neural Network for Power Converters , 2021, IEEE Transactions on Industrial Electronics.
[16] Shuhui Li,et al. Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks , 2021, IEEE Transactions on Circuits and Systems I: Regular Papers.
[17] Wang Hao,et al. Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching , 2021 .
[18] Maria Gabriella Xibilia,et al. RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process , 2021, Sensors.
[19] Yonghong Liu,et al. Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study , 2020, Comput. Ind. Eng..
[20] A. Al Bitar,et al. Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe , 2020, Water.
[21] Zheng O'Neill,et al. One for Many: Transfer Learning for Building HVAC Control , 2020, BuildSys@SenSys.
[22] Zhong Fan,et al. Anomaly Detection for IoT Time-Series Data: A Survey , 2020, IEEE Internet of Things Journal.
[23] Ramon Vilanova,et al. Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case , 2020, Sensors.
[24] George D. Montanez,et al. Limits of Transfer Learning , 2020, LOD.
[25] Antoni Morell,et al. A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection , 2020, Transportation Research Part C: Emerging Technologies.
[26] Zehong Cao,et al. Enhancing Transferability of Deep Reinforcement Learning-Based Variable Speed Limit Control Using Transfer Learning , 2020, IEEE Transactions on Intelligent Transportation Systems.
[27] Ramon Vilanova,et al. Design of Optimal PID Control with a Sensitivity Function for Resonance Phenomenon-involved Second-order Plus Dead-time System , 2020, J. Frankl. Inst..
[28] Hui Xiong,et al. A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.
[29] Ramon Vilanova,et al. Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design , 2019, Applied Sciences.
[30] Johannes L. Schönberger,et al. SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.
[31] Mohammad Hossein Kahaei,et al. Novel suboptimal approaches for hyperparameter tuning of deep neural network [under the shelf of optical communication] , 2019, Phys. Commun..
[32] Colin N. Jones,et al. Recurrent Neural Network based MPC for Process Industries , 2019, 2019 18th European Control Conference (ECC).
[33] Andre Leibsohn Martins,et al. Neural network based controllers for the oil well drilling process , 2019, Journal of Petroleum Science and Engineering.
[34] Ruqiang Yan,et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.
[35] Ramón Vilanova,et al. ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants , 2019, Sensors.
[36] Jaime G. Carbonell,et al. Characterizing and Avoiding Negative Transfer , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Costas J. Spanos,et al. Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting , 2018, Building and Environment.
[38] Chao Liu,et al. Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.
[39] Xiao Wang,et al. Soft sensor based on stacked auto-encoder deep neural network for air preheater rotor deformation prediction , 2018, Adv. Eng. Informatics.
[40] Junfei Qiao,et al. Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation , 2018, Neurocomputing.
[41] Juergen Jasperneite,et al. The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0 , 2017, IEEE Industrial Electronics Magazine.
[42] A. Thalla,et al. Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater , 2017, Applied Water Science.
[43] Stefania Tronci,et al. Predictive control of an activated sludge process for long term operation , 2016 .
[44] Ramon Vilanova,et al. Advanced decision control system for effluent violations removal in wastewater treatment plants , 2016 .
[45] Rui Araújo,et al. Review of soft sensor methods for regression applications , 2016 .
[46] Gaige Wang,et al. Self-adaptive extreme learning machine , 2016, Neural Computing and Applications.
[47] Jian Wang,et al. Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem , 2016 .
[48] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[49] Vijander Singh,et al. Development of soft sensor for neural network based control of distillation column. , 2013, ISA transactions.
[50] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[51] Bruce Ratner. The correlation coefficient: Its values range between +1/−1, or do they? , 2009 .
[52] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[53] Baokun Han,et al. Identification, classification, and quantification of three physical mechanisms in oil-in-water emulsions using AlexNet with transfer learning , 2021 .
[54] Arun Ross,et al. On automated source selection for transfer learning in convolutional neural networks , 2018, Pattern Recognit..
[55] Michael C. Hout,et al. Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.
[56] Phipps Arabie,et al. Chapter 3 – Multidimensional Scaling , 1998 .