no code implementations • 7 Jun 2023 • Xihaier Luo, Ahsan Kareem, Shinjae Yoo
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design.
no code implementations • 25 Jan 2022 • Xihaier Luo, Ahsan Kareem, Liting Yu, Shinjae Yoo
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems.
no code implementations • 10 Jan 2021 • Monica Arul, Ahsan Kareem
In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of shape of their time series.
no code implementations • 31 Aug 2020 • Monica Arul, Ahsan Kareem
The shapelet transform is a unique time series representation that is solely based on the shape of the time series data.
no code implementations • 22 Apr 2020 • Monica Arul, Ahsan Kareem
In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation.
no code implementations • 20 Nov 2019 • Monica Arul, Ahsan Kareem
This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes.
no code implementations • 8 Jul 2019 • Xihaier Luo, Ahsan Kareem
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion.