Publications

5 Publications0 Citations

2025

Wafer Composite Defect Recognition Framework based on Residual Dynamic Perception Network with Asymmetric Multi-Label Loss

Journal
Jiale Liu, Huan Wang
ISA Transactions
Preview for Wafer Composite Defect Recognition Framework based on Residual Dynamic Perception Network with Asymmetric Multi-Label Loss

Wafer defect recognition with dynamic perception and asymmetric multi-label loss, state-of-the-art performance on accuracy, noise robustness, and mislabeling resilience.

Wafer Defect RecognitionSemiconductor ManufacturingMulti-Label LearningNoise Robustness
Code

Energy-Efficient Brain-Inspired Self-Attention-Spiking Neural Network Framework for Mix-Type Wafer Defect Recognition

Journal
Dandan Peng, Yitian Wang, Xinhe Zhou, Jiale Liu, Chenyu Liu, Te Han
IEEE Sensors Journal
Preview for Energy-Efficient Brain-Inspired Self-Attention-Spiking Neural Network Framework for Mix-Type Wafer Defect Recognition

A brain-inspired spiking neural network framework with self-attention mechanisms for energy-efficient wafer defect recognition in semiconductor manufacturing.

Spiking Neural NetworksWafer Defect RecognitionSemiconductor ManufacturingEnergy Efficiency

2024

An Integrated Framework of Fourier Transform and Transformer for Rotating Machinery Fault Diagnosis

Conference
Xiaopeng Liu, Jiale Liu, Bingxiang Sun, Weige Zhang
2024 IEEE International Conference on Prognostics and Health Management (ICPHM)
Preview for An Integrated Framework of Fourier Transform and Transformer for Rotating Machinery Fault Diagnosis

A hybrid deep learning framework combining Fourier transforms and Transformers for robust fault diagnosis in rotating machinery under noisy conditions.

Fourier TransformTransformerRotating MachineryFault Diagnosis

A Brain-Inspired Energy-Efficient Wide Spiking Residual Attention Framework for Intelligent Fault Diagnosis

Journal
Jiale Liu, Huan Wang
Reliability Engineering & System Safety
Preview for A Brain-Inspired Energy-Efficient Wide Spiking Residual Attention Framework for Intelligent Fault Diagnosis

A novel brain-inspired framework combining artificial and spiking neural networks for energy-efficient industrial fault diagnosis with enhanced noise robustness.

Spiking Neural NetworksFault DiagnosisEnergy EfficiencyIndustrial Reliability

2023

QGFORMER: Quantum-Classical Hybrid Transformer Architecture for Gravitational Wave Detection

Conference
Jiaxiang Hu, Jiale Liu
2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
Preview for QGFORMER: Quantum-Classical Hybrid Transformer Architecture for Gravitational Wave Detection

A quantum-classical hybrid transformer architecture leveraging quantum computing advantages for enhanced gravitational wave detection and astrophysical discovery.

Quantum ComputingTransformerGravitational WavesHybrid Architecture

Corresponding author

Underlined authors contributed equally to this work