139 citations · 192 across the 14 of their papers we have counts for
14 papers
Predicting Electricity Infrastructure Induced Wildfire Risk in California
Mengqi Yao, Meghana Bharadwaj, Zheng Zhang +2
This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down…
Class-wise Thresholding for Robust Out-of-Distribution Detection
Matteo Guarrera, Baihong Jin, Tung-Wei Lin +3
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of sev…
Super-Resolution Reconstruction of Interval Energy Data
Jieyi Lu, Baihong Jin
High-resolution data are desired in many data-driven applications; however, in many cases only data whose resolution is lower than expected are available due to various reasons. It…
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…
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…
Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints
Pan Li, Baihong Jin, Dai Wang +1
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources int…