Bin Li

Orcid: 0000-0002-8722-8934

Affiliations:

Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, Wuhan, China
Huazhong University of Science and Technology, School of Mechanical Science and Engineering, Wuhan, China (PhD 2006)

According to our database¹, Bin Li authored at least 33 papers between 2008 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

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Other

Bibliography

2024

A novel deep learning motivated data augmentation system based on defect segmentation requirements.

[BibT_eX]

[DOI]

J. Intell. Manuf., February, 2024

A-Net: An A-Shape Lightweight Neural Network for Real-Time Surface Defect Segmentation.

[BibT_eX]

[DOI]

IEEE Trans. Instrum. Meas., 2024

A drift detection method for industrial images based on a defect segmentation model.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2024

Feature matching driven background generalization neural networks for surface defect segmentation.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2024

Learning trustworthy model from noisy labels based on rough set for surface defect detection.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2024

NAS-ASDet: An adaptive design method for surface defect detection network using neural architecture search.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, 2024

2023

A generalized well neural network for surface defect segmentation in Optical Communication Devices via Template-Testing comparison.

[BibT_eX]

[DOI]

Comput. Ind., October, 2023

A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, August, 2023

A transformed-feature-space data augmentation method for defect segmentation.

[BibT_eX]

[DOI]

Comput. Ind., May, 2023

Selecting informative data for defect segmentation from imbalanced datasets via active learning.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, April, 2023

NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection.

[BibT_eX]

[DOI]

CoRR, 2023

Background-Adaptive Surface Defect Detection Neural Networks via Positive Samples.

[BibT_eX]

[DOI]

Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023

2022

Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images.

[BibT_eX]

[DOI]

IEEE Trans. Ind. Informatics, 2022

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection.

[BibT_eX]

[DOI]

Symmetry, 2022

Defect attention template generation cycleGAN for weakly supervised surface defect segmentation.

[BibT_eX]

[DOI]

Pattern Recognit., 2022

Clear Memory-Augmented Auto-Encoder for Surface Defect Detection.

[BibT_eX]

[DOI]

CoRR, 2022

2020

Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion.

[BibT_eX]

[DOI]

IEEE Trans. Ind. Informatics, 2020

Defect Image Sample Generation With GAN for Improving Defect Recognition.

[BibT_eX]

[DOI]

IEEE Trans Autom. Sci. Eng., 2020

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition.

[BibT_eX]

[DOI]

IEEE Access, 2020

2019

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing.

[BibT_eX]

[DOI]

IEEE Trans. Ind. Electron., 2019

Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification.

[BibT_eX]

[DOI]

IEEE Trans. Ind. Electron., 2019

Design of deep learning accelerated algorithm for online recognition of industrial products defects.

[BibT_eX]

[DOI]

Yufeng Shu

Yu Huang

Bin Li

Neural Comput. Appl., 2019

Automated defect inspection of LED chip using deep convolutional neural network.

[BibT_eX]

[DOI]

J. Intell. Manuf., 2019

DefectGAN: Weakly-Supervised Defect Detection using Generative Adversarial Network.

[BibT_eX]

[DOI]

Proceedings of the 15th IEEE International Conference on Automation Science and Engineering, 2019

2016

Influence of information overload on operator's user experience of human-machine interface in LED manufacturing systems.

[BibT_eX]

[DOI]

Cogn. Technol. Work., 2016

EID vs UCD: A Comparative Study on User Interface Design in Complex Electronics Manufacturing Systems.

[BibT_eX]

[DOI]

Proceedings of the Engineering Psychology and Cognitive Ergonomics, 2016

2015

Skeuomorphism and Flat Design: Evaluating Users' Emotion Experience in Car Navigation Interface Design.

[BibT_eX]

[DOI]

Proceedings of the Design, User Experience, and Usability: Design Discourse, 2015

2014

Experimental Study on Cutter Deflection in Multi-axis NC Machining.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Robotics and Applications - 7th International Conference, 2014

2013

A Complete Methodology for Estimating Dynamics of the Heavy Machine Tool Structure.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Robotics and Applications - 6th International Conference, 2013

2008

Adaptive Notch Filter Control for the Torsion Vibration in Lead-Screw Feed Drive System Based on Neural Network.

[BibT_eX]

[DOI]

Yong Zhou

Fangyu Peng

Bin Li

Proceedings of the Intelligent Robotics and Applications, First International Conference, 2008

A Method of General Stiffness Modeling for Multi-axis Machine Tool.

[BibT_eX]

[DOI]

Rong Yan

Fangyu Peng

Bin Li

Proceedings of the Intelligent Robotics and Applications, First International Conference, 2008

Look-Ahead Scheme for High Speed Consecutive Micro Line Interpolation Based on Dynamics of Machine Tool.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Robotics and Applications, First International Conference, 2008

Bin Li

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