235 citations · 531 across the 38 of their papers we have counts for
10 papers · 1 filter
ODRE Workshop: Probabilistic Dynamic Hard Real-Time Scheduling in HPC
Florian Hofer, Martin A. Sehr, Alberto Sangiovanni-Vincentelli +1
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufact…
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…
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao +3
We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is tr…
Industrial Control via Application Containers: Migrating from Bare-Metal to IAAS
Florian Hofer, Martin A. Sehr, Antonio Iannopollo +3
We explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or cloud computing platforms using off the shelf t…
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 Formalization of Robustness for Deep Neural Networks
Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli +1
Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural netwo…
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…
A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics
Shromona Ghosh, Somil Bansal, Alberto Sangiovanni-Vincentelli +2
We consider the problem of extracting safe environments and controllers for reach-avoid objectives for systems with known state and control spaces, but unknown dynamics. In a given…
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…