Di Wang

Orcid: 0000-0001-7030-6521

Affiliations:
  • Shanghai Jiao Tong University, School of Mechanical Engineering, Department of Industrial Engineering and Management, Shanghai, China


According to our database1, Di Wang authored at least 16 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.
IEEE Trans Autom. Sci. Eng., April, 2024

An Integrated Deep Learning-Based Data Fusion and Degradation Modeling Method for Improving Prognostics.
IEEE Trans Autom. Sci. Eng., April, 2024

Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach.
ACM Trans. Sens. Networks, March, 2024

A Latent Variable-Based Multitask Learning Approach for Degradation Modeling of Machines With Dependency and Heterogeneity.
IEEE Trans. Instrum. Meas., 2024

2023
Distribution-Agnostic Few-Shot Industrial Fault Diagnosis via Adaptation-Aware Optimal Feature Transport.
IEEE Trans. Ind. Informatics, April, 2023

Spatial Rank-Based Augmentation for Nonparametric Online Monitoring and Adaptive Sampling of Big Data Streams.
Technometrics, April, 2023

A Control Chart for Monitoring Multivariate Spatiotemporal Correlated Data During Grain Storage.
Proceedings of the 19th IEEE International Conference on Automation Science and Engineering, 2023

An Entropy- and Attention-Based Feature Extraction and Selection Network for Multi-Target Coupling Scenarios.
Proceedings of the 19th IEEE International Conference on Automation Science and Engineering, 2023

2022
A Generic Indirect Deep Learning Approach for Multisensor Degradation Modeling.
IEEE Trans Autom. Sci. Eng., 2022

A Data Fusion-Based LSTM Network for Degradation Modeling Under Multiple Operational Conditions.
Proceedings of the 18th IEEE International Conference on Automation Science and Engineering, 2022

2020
Spatiotemporal Multitask Learning for 3-D Dynamic Field Modeling.
IEEE Trans Autom. Sci. Eng., 2020

Spatiotemporal Thermal Field Modeling Using Partial Differential Equations With Time-Varying Parameters.
IEEE Trans Autom. Sci. Eng., 2020

2019
Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks.
Sensors, 2019

Modeling of a three-dimensional dynamic thermal field under grid-based sensor networks in grain storage.
IISE Trans., 2019

2017
Modeling grain quality characteristics via dynamic models using sensing data.
Proceedings of the IEEE/SICE International Symposium on System Integration, 2017

2015
A prediction method for interior temperature of grain storage via dynamics models: A simulation study.
Proceedings of the IEEE International Conference on Automation Science and Engineering, 2015


  Loading...