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Found 12 papers, 2 papers with code
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling
Various approaches of active learning have been proposed in the literature, however, they either focus on exploration or exploitation in the design space.
Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data
Point cloud data are widely used in manufacturing applications for process inspection, modeling, monitoring and optimization.
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate.
Deep Learning based Covert Attack Identification for Industrial Control Systems
This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.
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.
AKM$^2$D : An Adaptive Framework for Online Sensing and Anomaly Quantification
In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification.
Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems
The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach.
Dataset: Rare Event Classification in Multivariate Time Series
The dataset comes from a multivariate time series process.
Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization
Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization.
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity
In this paper, we develop a new sequential regression modeling approach for data streams.
Sequence Graph Transform (SGT): A Feature Extraction Function for Sequence Data Mining (Extended Version)
However, sequence mining is particularly challenging because of difficulty in finding (dis)similarity/distance between sequences.
The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling
CSI functions by clustering patients into groups based on similarity of trajectory using a novel Semi-Markov model (SMM)-based clustering scheme proposed in this paper, as opposed to clustering by admit type or condition as in previous literature.
