Autonomous underwater vehicle fault diagnosis dataset.

The dataset contains 1225 data samples for 5 (labels). We divided the dataset into the training set and the test set through random stratified sampling. The test set accounted for of the total dataset.

Probabilistic learning vector quantization on manifold of symmetric positive definite matrices.

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data.

NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation.

Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains.