World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
7641
World Ranking
6905
National Ranking
926

Overview

Xuefeng Yan is affiliated with East China University of Science and Technology in China. Their research primarily spans the fields of Engineering and Computer Science, with notable contributions in Control and Systems Engineering, Mechanical Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, and Molecular Biology.

Their work covers a diverse range of topics including:

  • Fault Detection and Control Systems
  • Mineral Processing and Grinding
  • Advanced Control Systems Optimization
  • Spectroscopy and Chemometric Analyses
  • Machine Fault Diagnosis Techniques
  • Anomaly Detection Techniques and Applications
  • 3D Surveying and Cultural Heritage

Frequent collaborators in their research include Qingchao Jiang, Mingqiang Wei, Zhichao Li, Li Tian, and Jianbo Yu. Yan has published extensively across various venues, highlighting repeated appearances in:

  • arXiv (Cornell University)
  • Applied Soft Computing
  • Soft Computing
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Information Sciences

Notable papers by Xuefeng Yan include:

  • "Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application," 2020, IEEE Transactions on Industrial Informatics
  • "Local-Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "Distributed-ensemble stacked autoencoder model for non-linear process monitoring," 2020, Information Sciences
  • "A new deep model based on the stacked autoencoder with intensified iterative learning style for industrial fault detection," 2021, Process Safety and Environmental Protection
  • "Data-Driven Communication Efficient Distributed Monitoring for Multiunit Industrial Plant-Wide Processes," 2021, IEEE Transactions on Automation Science and Engineering

Best Publications

  • Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies

    Qinqin Fan;Xuefeng Yan

  • Parallel PCA–KPCA for nonlinear process monitoring

    Qingchao Jiang;Xuefeng Yan

  • Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application

    Huaikuan Yi;Qingchao Jiang;Xuefeng Yan;Bei Wang

  • Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference

    Jian Huang;Xuefeng Yan

  • Plant-wide process monitoring based on mutual information-multiblock principal component analysis.

    Qingchao Jiang;Xuefeng Yan

  • Optimizing the echo state network with a binary particle swarm optimization algorithm

    Heshan Wang;Xuefeng Yan

  • Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis

    Qingchao Jiang;Xuefeng Yan;Weixiang Zhao

  • Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares

    Qingchao Jiang;Xuefeng Yan;Hui Yi;Furong Gao

  • Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring

    Qingchao Jiang;Xuefeng Yan

  • Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis

    Qingchao Jiang;Steven X. Ding;Yang Wang;Xuefeng Yan

  • Self-adaptive differential evolution algorithm with discrete mutation control parameters

    Unknown

  • Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model

    Unknown

  • Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA

    Qingchao Jiang;Xuefeng Yan

  • Cycle-SNSPGAN: Towards Real-World Image Dehazing via Cycle Spectral Normalized Soft Likelihood Estimation Patch GAN

    Unknown

  • Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference

    Qingchao Jiang;Xuefeng Yan

  • GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

    Qingchao Jiang;Biao Huang;Xuefeng Yan

  • An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding

    Chudong Tong;Chudong Tong;Ahmet Palazoglu;Xuefeng Yan

  • Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data

    Qingchao Jiang;Shifu Yan;Hui Cheng;Xuefeng Yan

  • Distributed Statistical Process Monitoring Based on Four-Subspace Construction and Bayesian Inference

    Chudong Tong;Yu Song;Xuefeng Yan

  • Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA

    Junping Huang;Xuefeng Yan

  • Related and independent variable fault detection based on KPCA and SVDD

    Jian Huang;Xuefeng Yan

Frequent Co-Authors

Biao Huang
Biao Huang University of Alberta
Furong Gao
Furong Gao Hong Kong University of Science and Technology
Ahmet Palazoglu
Ahmet Palazoglu University of California, Davis
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Neil D. Lawrence
Neil D. Lawrence University of Cambridge
Nael H. El-Farra
Nael H. El-Farra University of California, Davis
Manabu Kano
Manabu Kano Kyoto University
Yaochu Jin
Yaochu Jin Westlake University
Yu Xue
Yu Xue Nanjing University of Information Science and Technology

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