Article-Journal

Artificial Intelligence for the Computer-Aided Detection of Periapical Lesions in Cone-Beam Computed Tomographic Images
Introduction The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed …
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Adaptive Change Point Monitoring for High-Dimensional Data
In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by …
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Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach
Trajectory prediction is an essential component of the next generation national air transportation system. Reliable trajectory …
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Deep Multistage Multi-Task Learning for Quality Prediction and Diagnostics of Multistage Manufacturing Systems
In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, …
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Deep Multistage Multi-Task Learning for Quality Prediction and Diagnostics of Multistage Manufacturing Systems
Image Decomposition-Based Sparse Extreme Pixel-Level Feature Detection Model with Application to Medical Images
Pixel-level feature detection from images is an essential but challenging task encountered in domains such as detecting defects in …
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Real-Time Detection of Clustered Events in Video-Imaging Data with Applications to Additive Manufacturing
The use of video-imaging data for in-line process monitoring applications has become more and more popular in the industry. In this …
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Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders
Variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results …
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Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders
AKM2D: An Adaptive Framework for Online Sensing and Anomaly Quantification
In point-based sensing systems such as coordinate measuring machines and laser ultrasonics where complete sensing is impractical due to …
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Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images
Introduction The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed …
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Multi-Sensor Prognostics Modeling for Applications with Highly Incomplete Signals
Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To …
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