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Found 11 papers, 2 papers with code
HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
As part of this paper, we present a search algorithm to find the TTs and upgrade our previously developed DRL agent CurriculumAgent (CAgent) to a novel topology agent.
Exploring the Dynamics of Data Transmission in 5G Networks: A Conceptual Analysis
A series of experiments were conducted to evaluate the key aspects of the data transmission.
BirdSet: A Multi-Task Benchmark for Classification in Computational Avian Bioacoustics
Data fragmentation and opacity across studies complicate a comprehensive evaluation of model performance.
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers
We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL).
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach.
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation
Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account.
Towards Organic Distribution Systems -- The Vision of Self-Configuring, Self-Organising, Self-Healing, and Self-Optimising Power Distribution Management
On the grid level, distribution management systems (DMS) try to keep the power system in the normal operational state.
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes
Furthermore, they have to be taken into consideration when self-improving the own configuration decisions based on a feedback loop concept, e. g., known from the SASO domain or the Autonomic and Organic Computing initiatives.
Self-Adaptation of Activity Recognition Systems to New Sensors
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account.
Organic Computing in the Spotlight
Organic Computing is an initiative in the field of systems engineering that proposed to make use of concepts such as self-adaptation and self-organisation to increase the robustness of technical systems.
