Ting Yuan

Zhengyang Fan, Wanru Li, Kuo-chu Chang et al.

Accurately estimating the remaining useful life (RUL) for degradation systems is crucial in modern prognostic and health management (PHM). Convolutional Neural Networks (CNNs), initially developed for tasks like image and video recognition, have proven highly effectively in RUL prediction, demonstrating remarkable performance. However, with the emergence of the Vision Transformer (ViT), a Transformer model tailored for computer vision tasks such as image classification, and its demonstrated superiority over CNNs, there is a natural inclination to explore its potential in enhancing RUL prediction accuracy. Nonetheless, applying ViT directly to multivariate sensor data for RUL prediction poses challenges, primarily due to the ambiguous nature of spatial information in time series data. To address this issue, we introduce the PerFormer, a permutation-based vision transformer approach designed to permute multivariate time series data, mimicking spatial characteristics akin to image data, thereby making it suitable for ViT. To generate the desired permutation matrix, we introduce a novel permutation loss function aimed at guiding the convergence of any matrix towards a permutation matrix. Our experiments on NASA's C-MAPSS dataset demonstrate the PerFormer's superior performance in RUL prediction compared to state-of-the-art methods employing CNNs, Recurrent Neural Networks (RNNs), and various Transformer models. This underscores its effectiveness and potential in PHM applications.

Dominik Nuss, Stephan Reuter, Markus Thom et al.

Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.