Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks

publishedVersio

A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

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Implementation of an underwater acoustic modem with network capability

This paper introduces the underwater acoustic modem as implemented within the UAN – Underwater Acoustic Network project. The low power modem has implemented turbo equalization algorithms in addition to variable spread rate direct sequence spread spectrum signaling. The network layer implemented on the modem support automatic network discovery, multi hop routing and support for mobile nodes, and when expanded with a single board computer via serial line it supports IP connectivity end-to-end. Experimental results from sea trials are presented

Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles

Author's accepted manuscriptProviding full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function.acceptedVersio

Generative Adversarial Immitation Learning for Steering an Unmanned Surface Vehicle

The task of obstacle avoidance using maritime vessels, such as Unmanned Surface Vehicles (USV), has traditionally been solved using specialized modules that are designed and optimized separately. However, this approach requires a deep insight into the environment, the vessel, and their complex dynamics. We propose an alternative method using Imitation Learning (IL) through Deep Reinforcement Learning (RL) and Deep Inverse Reinforcement Learning (IRL) and present a system that learns an end-to-end steering model capable of mapping radar-like images directly to steering actions in an obstacle avoidance scenario. The USV used in the work is equipped with a Radar sensor and we studied the problem of generating a single action parameter, heading. We apply an IL algorithm known as generative adversarial imitation learning (GAIL) to develop an end-to-end steering model for a scenario where avoidance of an obstacle is the goal. The performance of the system was studied for different design choices and compared to that of a system that is based on pure RL. The IL system produces results that indicate it is able to grasp the concept of the task and that in many ways are on par with the RL system. We deem this to be promising for future use in tasks that are not as easily described by a reward function

Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

publishedVersio

Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures

publishedVersio

Implementation of an underwater acoustic modem with network capability

This paper introduces the underwater acoustic modem as implemented within the UAN – Underwater Acoustic Network project. The low power modem has implemented turbo equalization algorithms in addition to variable spread rate direct sequence spread spectrum signaling. The network layer implemented on the modem support automatic network discovery, multi hop routing and support for mobile nodes, and when expanded with a single board computer via serial line it supports IP connectivity end-to-end. Experimental results from sea trials are presented

Implementation of an underwater acoustic modem with network capability

This paper introduces the underwater acoustic modem as implemented within the UAN – Underwater Acoustic Network project. The low power modem has implemented turbo equalization algorithms in addition to variable spread rate direct sequence spread spectrum signaling. The network layer implemented on the modem support automatic network discovery, multi hop routing and support for mobile nodes, and when expanded with a single board computer via serial line it supports IP connectivity end-to-end. Experimental results from sea trials are presented