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Engineering and Technology

D-Index
56
Citations
10756
World Ranking
2857
National Ranking
869

Overview

Jianjun Shi is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the fields of Engineering and Computer Science, with significant contributions to Industrial and Manufacturing Engineering, Control and Systems Engineering, and Computer Vision and Pattern Recognition among other subfields.

Their work covers diverse topics including:

  • Industrial Vision Systems and Defect Detection
  • Manufacturing Process and Optimization
  • Advanced Statistical Process Monitoring
  • Tensor Decomposition and Applications
  • Fault Detection and Control Systems
  • Sparse and Compressive Sensing Techniques
  • Control Systems and Identification

Jianjun Shi has published extensively in several venues. The most frequent publication outlets include:

  • IISE Transactions
  • arXiv (Cornell University)
  • IEEE Transactions on Automation Science and Engineering
  • Journal of Manufacturing Science and Engineering
  • Technometrics

Among recent papers authored or co-authored by Jianjun Shi and colleagues are:

  • "A Deep Learning Based Data Fusion Method for Degradation Modeling and Prognostics," 2020, IEEE Transactions on Reliability
  • "Active Learning for Gaussian Process Considering Uncertainties With Application to Shape Control of Composite Fuselage," 2020, IEEE Transactions on Automation Science and Engineering
  • "A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN," 2021, Frontiers in Physics
  • "In-process quality improvement: Concepts, methodologies, and applications," 2022, IISE Transactions
  • "Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning," 2020, Technometrics

The scientist has collaborated frequently with colleagues including Shancong Mou, Michael Biehler, Andi Wang, Zhen Zhong, and Jeffrey H. Hunt. These collaborations have resulted in multiple co-authored publications, reflecting ongoing research partnerships.

Best Publications

  • State Space Modeling of Sheet Metal Assembly for Dimensional Control

    Jionghua Jin;Jianjun Shi

  • Active Balancing and Vibration Control of Rotating Machinery: A Survey

    Shiyu Zhou;Jianjun Shi

  • State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors

    Shiyu Zhou;Qiang Huang;Jianjun Shi

  • A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

    Kaibo Liu;N. Z. Gebraeel;Jianjun Shi

  • Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes

    Jianjun Shi

  • Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition

    Darek Ceglarek;J. Shi

  • Quality control and improvement for multistage systems: A survey

    Jianjun Shi;Shiyu Zhou

  • Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach

    Yu Ding;Dariusz Ceglarek;Jianjun Shi

  • Dimensional variation reduction for automotive body assembly

    Darek Ceglarek;J. Shi

  • Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

    Jionghua Jin;Jianjun Shi

  • Feature-preserving data compression of stamping tonnage information using wavelets

    Jionghua Jin;Jianjun Shi

  • A survey on statistical methods for health care fraud detection

    Jing Li;Kuei Ying Huang;Jionghua Jin;Jianjun Shi

  • Diagnosability Analysis of Multi-Station Manufacturing Processes

    Yu Ding;Jianjun Shi;Dariusz Ceglarek

  • MODELING AND DIAGNOSIS OF MULTISTAGE MANUFACTURING PROCESSES: PART I - STATE SPACE MODEL

    Yu Ding;Dariusz Ceglarek;Jianjun Shi

  • Diagnosis of Multiple Fixture Faults in Panel Assembly

    D. W. Apley;J. Shi

  • The GLRT for statistical process control of autocorrelated processes

    Daniel W. Apley;Jianjun Shi

  • A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

    Darek Ceglarek;J. Shi;S. M. Wu

  • Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models

    Shiyu Zhou;Yu Ding;Yong Chen;Jianjun Shi

  • Image-Based Process Monitoring Using Low-Rank Tensor Decomposition

    Hao Yan;Kamran Paynabar;Jianjun Shi

  • A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes

    Daniel W Apley;Jianjun Shi

Frequent Co-Authors

Darek Ceglarek
Darek Ceglarek University of Warwick
Shiyu Zhou
Shiyu Zhou University of Wisconsin–Madison
Yu Ding
Yu Ding Georgia Institute of Technology
S. Jack Hu
S. Jack Hu University of California, Riverside
Chuck Zhang
Chuck Zhang Georgia Institute of Technology
Ben Wang
Ben Wang Georgia Institute of Technology
Massimo Ruzzene
Massimo Ruzzene University of Colorado Boulder
Fugee Tsung
Fugee Tsung Hong Kong University of Science and Technology
Linkan Bian
Linkan Bian Mississippi State University
Elsayed A. Elsayed
Elsayed A. Elsayed Rutgers, The State University of New Jersey

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