Various LPF models are proposed, but some crucial questions are still remained: what is the performance bound (e. g., the error bound) of LPF models, how to know a branch is applicable for LPF models or not, and what is the best LPF model.
This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.
To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR).
While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing.
For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features.
Cryptography and Security Software Engineering
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort.
Graph convolution network (GCN) attracts intensive research interest with broad applications.
Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks.
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism.