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Found 10 papers, 1 papers with code
Risk-aware Flexible Resource Utilization in an Unbalanced Three-Phase Distribution Network using SDP-based Distributionally Robust Optimal Power Flow
This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network.
Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing
The resulting dynamic feature-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model.
ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction
However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i. e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware.
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation
Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C).
Predict stock prices with ARIMA and LSTM
For LSTM model, the data from 01 January 2010 to 31 December 2015 are selected as the training set, the data from 01 January 2016 to 31 December 2017 as the validation set and the data from 01 January 2018 to 31 December 2018 as the test set.
QoE-Aware Resource Allocation for Semantic Communication Networks
Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed.
Resource allocation for text semantic communications
Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime.
Event-driven Two-stage Solution to Non-intrusive Load Monitoring
Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances.
Non-Intrusive Load Monitoring
Vocal Bursts Valence Prediction
Adaptive Event Detection for Representative Load Signature Extraction
Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances.
Deep Shape from Polarization
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning.
