Picture for Peng Peng

Peng Peng

Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework

Jun 27, 2023
Figure 1 for Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework
Figure 2 for Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework
Figure 3 for Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework
Figure 4 for Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework
Viaarxiv icon

Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis

Jun 26, 2023
Figure 1 for Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Figure 2 for Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Figure 3 for Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Figure 4 for Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Viaarxiv icon

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

Feb 17, 2023
Figure 1 for SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Figure 2 for SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Figure 3 for SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Figure 4 for SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Viaarxiv icon

An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis

Feb 13, 2023
Figure 1 for An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Figure 2 for An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Figure 3 for An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Figure 4 for An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Viaarxiv icon

SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data

Feb 12, 2023
Figure 1 for SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Figure 2 for SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Figure 3 for SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Figure 4 for SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Viaarxiv icon

UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering

Dec 23, 2022
Figure 1 for UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Figure 2 for UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Figure 3 for UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Figure 4 for UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Viaarxiv icon

Supervised Contrastive Learning with TPE-based Bayesian Optimization of Tabular Data for Imbalanced Learning

Oct 19, 2022
Figure 1 for Supervised Contrastive Learning with TPE-based Bayesian Optimization of Tabular Data for Imbalanced Learning
Figure 2 for Supervised Contrastive Learning with TPE-based Bayesian Optimization of Tabular Data for Imbalanced Learning
Figure 3 for Supervised Contrastive Learning with TPE-based Bayesian Optimization of Tabular Data for Imbalanced Learning
Figure 4 for Supervised Contrastive Learning with TPE-based Bayesian Optimization of Tabular Data for Imbalanced Learning
Viaarxiv icon

Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games

Jun 22, 2022
Figure 1 for Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
Figure 2 for Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
Figure 3 for Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
Figure 4 for Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
Viaarxiv icon

Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment

Add code
Feb 27, 2022
Figure 1 for Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment
Figure 2 for Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment
Figure 3 for Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment
Figure 4 for Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment
Viaarxiv icon

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Add code
Feb 13, 2022
Figure 1 for Continuous Occupancy Mapping in Dynamic Environments Using Particles
Figure 2 for Continuous Occupancy Mapping in Dynamic Environments Using Particles
Figure 3 for Continuous Occupancy Mapping in Dynamic Environments Using Particles
Figure 4 for Continuous Occupancy Mapping in Dynamic Environments Using Particles
Viaarxiv icon