Liang Chen

Orcid: 0000-0002-6598-1036

Affiliations:
  • Soochow University, Suzhou, China


According to our database1, Liang Chen authored at least 12 papers between 2018 and 2025.

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

Timeline

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Bibliography

2025
I2KEN: Intra-Domain and Inter-Domain Knowledge Enhancement Network for Lifelong Loop Closure Detection.
IEEE Robotics Autom. Lett., September, 2025

2024
Metric Learning-Based Few-Shot Adversarial Domain Adaptation: A Cross-Machine Diagnosis Method for Ball Screws of Industrial Robots.
IEEE Trans. Instrum. Meas., 2024

Fault diagnosis for ball screws in industrial robots under variable and inaccessible working conditions with non-vibration signals.
Adv. Eng. Informatics, 2024

2023
Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions.
Reliab. Eng. Syst. Saf., June, 2023

2022
A Lifelong Learning Method for Gearbox Diagnosis With Incremental Fault Types.
IEEE Trans. Instrum. Meas., 2022

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions.
IEEE Trans. Ind. Informatics, 2022

2021
Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning.
Sensors, 2021

2020
Improved Deep Distance Learning for Visual Loop Closure Detection in Smart City.
Peer-to-Peer Netw. Appl., 2020

A novel vSLAM framework with unsupervised semantic segmentation based on adversarial transfer learning.
Appl. Soft Comput., 2020

An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis.
IEEE Access, 2020

Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions.
Proceedings of the 18th IEEE International Conference on Industrial Informatics, 2020

2018
An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis.
IEEE Access, 2018


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