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Data-driven prediction of battery cycle life before capacity degradation

Nature energy, 2019-05, Vol.4 (5), p.383-391

2019

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Fault Detection in Industrial Systems Using Maximized Divergence Analysis Approach

IEEE access, 2022, Vol.10, p.60674-60681

2022

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A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

Nature communications, 2024-01, Vol.15 (1), p.280-280, Article 280

2024

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Realistic fault detection of li-ion battery via dynamical deep learning

Nature communications, 2023-09, Vol.14 (1), p.5940-5940, Article 5940

2023

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Latent variable modeling approach for fault detection and identification of process correlations

Transactions of the Institute of Measurement and Control, 2019-04, Vol.41 (6), p.1740-1749

2019

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Multi-objective optimization for fast charging design of lithium-ion batteries using constrained Bayesian optimization

Journal of power sources, 2023-11, Vol.584, p.233602, Article 233602

2023

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Dynamic Bhattacharyya Bound-Based Approach for Fault Classification in Industrial Processes

IEEE transactions on industrial informatics, 2022-01, Vol.18 (1), p.397-404

2022

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Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Chemical engineering science, 2022-04, Vol.252, p.117510, Article 117510

2022

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Simultaneous Identification of Bidirectional Path Models Based on Process Data

IEEE transactions on automation science and engineering, 2015-04, Vol.12 (2), p.666-679

2015

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Adaptive boosting with fairness-aware reweighting technique for fair classification

Expert systems with applications, 2024-09, Vol.250, Article 123916

2024

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Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

Information sciences, 2023-11, Vol.647, p.119521, Article 119521

2023

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Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback-Leibler Divergence

IEEE transactions on control systems technology, 2021-03, Vol.29 (2), p.780-785

2021

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State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Journal of process control, 2022-04, Vol.112, p.1-11

2022

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Constrained Bayesian Optimization for Minimum-Time Charging of Lithium-Ion Batteries

IEEE control systems letters, 2022, Vol.6, p.1682-1687

2022

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Perspective on Automation of Statistical Modeling Process for Battery Lifetime Prediction

2019 Chinese Control Conference (CCC), 2019, p.6537-6540

Jiang, Benben

2019

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State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Journal of process control, 2022-04, Vol.112, p.1-11

2022

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Matrix factorization based instrumental variable approach for simultaneous identification of Bi-directional path models

ISA transactions, 2018-08, Vol.79, p.73-82

2018

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Maximized Mutual Information Analysis Based on Stochastic Representation for Process Monitoring

IEEE transactions on industrial informatics, 2019-03, Vol.15 (3), p.1579-1587

2019

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Adaptive Model-Based Reinforcement Learning for Fast Charging Optimization of Lithium-Ion Batteries

IEEE transactions on industrial informatics, 2024-01, Vol.20 (1), p.1-10

2024

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Fault detection of process correlation structure using canonical variate analysis-based correlation features

Journal of process control, 2017-10, Vol.58, p.131-138

2017

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Dynamic Minimax Probability Machine-Based Approach for Fault Diagnosis Using Pairwise Discriminate Analysis

IEEE transactions on control systems technology, 2019-03, Vol.27 (2), p.806-813

2019

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Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

Energy & environmental science, 2022-10, Vol.15 (1), p.483-494

2022

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Canonical variate analysis-based contributions for fault identification

Journal of process control, 2015-02, Vol.26, p.17-25

2015

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Fast charging design for Lithium-ion batteries via Bayesian optimization

Applied energy, 2022-02, Vol.307, p.118244, Article 118244

2022

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A combined canonical variate analysis and Fisher discriminant analysis (CVA–FDA) approach for fault diagnosis

Computers & chemical engineering, 2015-06, Vol.77, p.1-9

2015

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Data-driven prediction of battery cycle life before capacity degradation

Fault Detection in Industrial Systems Using Maximized Divergence Analysis Approach

A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

Realistic fault detection of li-ion battery via dynamical deep learning

Latent variable modeling approach for fault detection and identification of process correlations

Multi-objective optimization for fast charging design of lithium-ion batteries using constrained Bayesian optimization

Dynamic Bhattacharyya Bound-Based Approach for Fault Classification in Industrial Processes

Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Simultaneous Identification of Bidirectional Path Models Based on Process Data

Adaptive boosting with fairness-aware reweighting technique for fair classification

Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback-Leibler Divergence

State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Constrained Bayesian Optimization for Minimum-Time Charging of Lithium-Ion Batteries

Perspective on Automation of Statistical Modeling Process for Battery Lifetime Prediction

State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Matrix factorization based instrumental variable approach for simultaneous identification of Bi-directional path models

Maximized Mutual Information Analysis Based on Stochastic Representation for Process Monitoring

Adaptive Model-Based Reinforcement Learning for Fast Charging Optimization of Lithium-Ion Batteries

Fault detection of process correlation structure using canonical variate analysis-based correlation features

Dynamic Minimax Probability Machine-Based Approach for Fault Diagnosis Using Pairwise Discriminate Analysis

Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

Canonical variate analysis-based contributions for fault identification

Fast charging design for Lithium-ion batteries via Bayesian optimization

A combined canonical variate analysis and Fisher discriminant analysis (CVA–FDA) approach for fault diagnosis

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