no code implementations • 10 Jan 2024 • Yiran Huang, Haibin Zhao, Yexu Zhou, Till Riedel, Michael Beigl
In recent years, deep learning has emerged as a potent tool across a multitude of domains, leading to a surge in research pertaining to its application in the wearable human activity recognition (WHAR) domain.
no code implementations • 15 Jul 2023 • Yiran Huang, Yexu Zhou, Till Riedel, Likun Fang, Michael Beigl
Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering.
no code implementations • 15 Nov 2022 • Yiran Huang, Yexu Zhou, Michael Hefenbrock, Till Riedel, Likun Fang, Michael Beigl
In this work, we propose a pixel-wise decision-based attack algorithm that finds a distribution of adversarial perturbation through a reinforcement learning algorithm.
no code implementations • 4 Jan 2022 • Yiran Huang, Nicole Schaal, Michael Hefenbrock, Yexu Zhou, Till Riedel, Likun Fang, Michael Beigl
Our method leverages Monte Carlo tree search and models the process of generating explanations as two games.
no code implementations • 10 Aug 2020 • Yexu Zhou, Yuting Gao, Yiran Huang, Michael Hefenbrock, Till Riedel, Michael Beigl
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series.