7 citations · 24 across the 8 of their papers we have counts for
6 papers · 1 filter
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
Yingshui Tan, Baihong Jin, Qiushi Cui +2
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. Th…
Using Ensemble Classifiers to Detect Incipient Anomalies
Baihong Jin, Yingshui Tan, Albert Liu +3
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. Th…
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Yingshui Tan, Baihong Jin, Xiangyu Yue +2
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learne…
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Baihong Jin, Yingshui Tan, Yuxin Chen +2
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operatin…
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
Baihong Jin, Yingshui Tan, Yuxin Chen +1
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently ap…
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
Baihong Jin, Yingshui Tan, Alexander Nettekoven +4
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial…