139 citations · 192 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
A tractable ellipsoidal approximation for voltage regulation problems
Pan Li, Baihong Jin, Ruoxuan Xiong +3
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of o…
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Baihong Jin, Dan Li, Seshadhri Srinivasan +3
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning mo…
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
Baihong Jin, Yuxin Chen, Dan Li +2
It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM…
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Dan Li, Dacheng Chen, Lei Shi +3
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of mult…