100 citations · 192 across the 11 of their papers we have counts for
11 papers
Data-driven sparse polynomial chaos expansion for models with dependent inputs
Zhanlin Liu, Youngjun Choe
Polynomial chaos expansions (PCEs) have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs. PCEs for model…
Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features
Quoc Dung Cao, Youngjun Choe
Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goa…
Splitting Gaussian Process Regression for Streaming Data
Nick Terry, Youngjun Choe
Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer f…
Infrastructure Recovery Curve Estimation Using Gaussian Process Regression on Expert Elicited Data
Quoc D. Cao, Scott B. Miles, Youngjun Choe
Infrastructure recovery time estimation is critical to disaster management and planning. Inspired by recent resilience planning initiatives, we consider a situation where experts a…
Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
Zhanlin Liu, Ashis G. Banerjee, Youngjun Choe
Sensitivity analysis (SA) is an important aspect of process automation. It often aims to identify the process inputs that influence the process output's variance significantly. Exi…
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Sean Andrew Chen, Andrew Escay, Christopher Haberland +3
Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-…