Sensors and AI techniques for situational awareness in autonomous ships: A review
S Thombre, Z Zhao, H Ramm-Schmidt… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Autonomous ships are expected to improve the level of safety and efficiency in future
maritime navigation. Such vessels need perception for two purposes: to perform …
maritime navigation. Such vessels need perception for two purposes: to perform …
Data-driven model selections of second-order particle dynamics via integrating Gaussian processes with low-dimensional interacting structures
In this paper, we focus on the data-driven discovery of a general second-order particle-
based model that contains many state-of-the-art models for modeling the aggregation and …
based model that contains many state-of-the-art models for modeling the aggregation and …
Anomaly detection in streaming data with gaussian process based stochastic differential equations
This paper characterises streaming data as the evolution of a stochastic differential
equation, with the aim of extracting information that can be used to detect anomalies in the …
equation, with the aim of extracting information that can be used to detect anomalies in the …
Traversing time with multi-resolution Gaussian process state-space models
Gaussian Process state-space models capture complex temporal dependencies in a
principled manner by placing a Gaussian Process prior on the transition function. These …
principled manner by placing a Gaussian Process prior on the transition function. These …
Learning Collective Behaviors from Observation
We present a comprehensive examination of learning methodologies employed for the
structural identification of dynamical systems. These techniques are designed to elucidate …
structural identification of dynamical systems. These techniques are designed to elucidate …
[PDF][PDF] Variational inference for composite Gaussian process models
J Lindinger - 2023 - publishup.uni-potsdam.de
Most machine learning methods provide only point estimates when being queried to predict
on new data. This is problematic when the data is corrupted by noise, eg from imperfect …
on new data. This is problematic when the data is corrupted by noise, eg from imperfect …
Learning particle swarming models from data with Gaussian processes
Interacting particle or agent systems that exhibit diverse swarming behaviors are prevalent
in science and engineering. Develo** effective differential equation models to understand …
in science and engineering. Develo** effective differential equation models to understand …