7 citations · 7 across the 7 of their papers we have counts for
7 papers
From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
Olga Fink, Ismail Nejjar, Vinay Sharma +13
Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and op…
TARD: Test-time Domain Adaptation for Robust Fault Detection under Evolving Operating Conditions
Han Sun, Olga Fink
Fault detection is essential in complex industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. With the growin…
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation
Hankang Sun, Guiming Li, Su Yang +1
Domain adaptation is challenging for time series classification due to the highly dynamic nature. This study tackles the most difficult subtask when both target labels and source d…
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Han Sun, Rui Gong, Ismail Nejjar +1
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignore…
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions
Han Sun, Kevin Ammann, Stylianos Giannoulakis +1
Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have…
Unseen Visual Anomaly Generation
Han Sun, Yunkang Cao, Hao Dong +1
Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples…
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Hao Dong, Ismail Nejjar, Han Sun +2
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to…