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Found 9 papers, 1 papers with code
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning
In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes.
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning.
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients.
Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home
To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase. We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model.
Predicting infections in the Covid-19 pandemic -- lessons learned
Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions.
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning
A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns.
Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning
Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities.
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior.
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model.
