What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mechanical engineering
His primary areas of investigation include Fault, State-space representation, Algorithm, Process and Quality.
His Fault research includes themes of Feature, Principal component analysis, Data mining and Pattern recognition.
His State-space representation study integrates concerns from other disciplines, such as Mechanical engineering, Interchangeability, Machining and Control theory.
The various areas that Jianjun Shi examines in his Algorithm study include Fixture, Computer Aided Design, Fault model and State space.
Jianjun Shi combines subjects such as State vector and Engineering drawing with his study of State space.
He has researched Process in several fields, including Statistical process control, Control engineering, Relation, Geometric dimensioning and tolerancing and Product design.
His most cited work include:
- State Space Modeling of Sheet Metal Assembly for Dimensional Control (338 citations)
- State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors (214 citations)
- Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition (204 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Quality, Algorithm, Data mining, Fault and Reliability engineering.
His Quality study incorporates themes from Data science, Control and Process.
In his study, which falls under the umbrella issue of Algorithm, State space, Control theory and Mechanical engineering is strongly linked to State-space representation.
His Data mining research incorporates themes from Sampling, Statistical process control and Multivariate statistics.
He combines subjects such as Fixture, Engineering drawing, Signal processing, Feature extraction and Pattern recognition with his study of Fault.
Jianjun Shi focuses mostly in the field of Reliability engineering, narrowing it down to matters related to Machining and, in some cases, Machine tool.
He most often published in these fields:
- Quality (16.67%)
- Algorithm (14.88%)
- Data mining (14.88%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (14.88%)
- Data mining (14.88%)
- Composite number (5.36%)
In recent papers he was focusing on the following fields of study:
Algorithm, Data mining, Composite number, Fuselage and Structural engineering are his primary areas of study.
His study in the fields of Estimation theory under the domain of Algorithm overlaps with other disciplines such as Data modeling.
His research in Data mining intersects with topics in Sampling, Statistical process control and Sensor fusion.
Jianjun Shi works mostly in the field of Statistical process control, limiting it down to topics relating to Multi channel and, in certain cases, Artificial intelligence.
His study on Composite number also encompasses disciplines like
- Control engineering which connect with Computer experiment and Engineering design process,
- Surrogate model and related Dimension, Variation and Reduction.
His Actuator course of study focuses on Control system and Mechanical engineering.
Between 2017 and 2021, his most popular works were:
- Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition (25 citations)
- Surrogate Model-Based Control Considering Uncertainties for Composite Fuselage Assembly (19 citations)
- Feasibility analysis of composite fuselage shape control via finite element analysis (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Statistical process control, Composite number, Data mining and Anomaly detection.
His work deals with themes such as Multivariate statistics and Pattern recognition, which intersect with Artificial intelligence.
His studies in Statistical process control integrate themes in fields like Algorithm, Decomposition method, Noise, Wavelet and Principal component analysis.
The Composite number study combines topics in areas such as Structural engineering, Finite element method, Fuselage, Actuator and Surrogate model.
His Actuator research is multidisciplinary, incorporating perspectives in Fixture, Mechanical engineering, Control system, Stress and Process variation.
The concepts of his Data mining study are interwoven with issues in Advanced manufacturing, Key, Measure and Sensor fusion.
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