4.7 Article

A Novel Data Augmentation Method Based on CoralGAN for Prediction of Part Surface Roughness

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Depth Image Denoising Using Nuclear Norm and Learning Graph Model

Chenggang Yan et al.

Summary: In this research, a group-based nuclear norm and learning graph (GNNLG) model is proposed for depth image denoising, taking advantage of patch similarity and low-rank property to enhance performance. Experimental results demonstrate that the proposed method outperforms other current state-of-the-art denoising methods in both subjective and objective evaluation.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Deep Multi-View Enhancement Hashing for Image Retrieval

Chenggang Yan et al.

Summary: This paper proposes a novel multi-view hashing learning method, integrating neural networks to enhance retrieval performance significantly. By effectively evaluating view stability and fusing multiple data, relationships between views are explored and advantages are preserved.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Engineering, Multidisciplinary

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Wei Zhang et al.

MEASUREMENT (2020)

Article Computer Science, Information Systems

3D Room Layout Estimation From a Single RGB Image

Chenggang Yan et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2020)

Article Computer Science, Interdisciplinary Applications

Generative adversarial networks for data augmentation in machine fault diagnosis

Siyu Shao et al.

COMPUTERS IN INDUSTRY (2019)

Article Chemistry, Multidisciplinary

Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

Wan-Ju Lin et al.

APPLIED SCIENCES-BASEL (2019)

Article Computer Science, Information Systems

Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Wentao Mao et al.

IEEE ACCESS (2019)

Article Computer Science, Artificial Intelligence

Surface roughness prediction in end milling process using intelligent systems

Abdel Badie Sharkawy et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2014)

Article Computer Science, Artificial Intelligence

SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

Enislay Ramentol et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

Learning Deep Architectures for AI

Yoshua Bengio

FOUNDATIONS AND TRENDS IN MACHINE LEARNING (2009)