Unknown uncertainties in the COVID-19 pandemic: Multi-dimensional identification and mathematical modelling for the analysis and estimation of the casualties
Insights about the dominant dynamics, coupled structures and the unknown uncertainties of
the pandemic diseases play an important role in determining the future characteristics of the …
the pandemic diseases play an important role in determining the future characteristics of the …
Bayesian noise modelling for state estimation of the spread of covid-19 in saudi arabia with extended kalman filters
The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions
using complex noisy data greatly affects the accuracy of pandemic trend and state …
using complex noisy data greatly affects the accuracy of pandemic trend and state …
Bayesian Filtering-Based Internet of Drone Things for Precision Drone Delivery: Challenges, Unravelling, and Future Research Scope
A Kumar, B Alankar, J Ahmed… - … Conference on Electrical …, 2024 - ieeexplore.ieee.org
Recent years have witnessed abundant growth in the Internet of Drone Things (IoDTs) in the
field of communication, supervising drones, and several severe environmental situations …
field of communication, supervising drones, and several severe environmental situations …
Unscented Kalman Filter-Based Bayesian Filtering of RC Circuit
R Sharma, R Bansal - 2022 IEEE 11th International Conference …, 2022 - ieeexplore.ieee.org
In this paper, we present output voltage estimation of second order resistor capacitor (RC)
low pass filter (LPF) circuit using the unscented Kalman filter (UKF). Firstly, with the use of …
low pass filter (LPF) circuit using the unscented Kalman filter (UKF). Firstly, with the use of …
Moving Horizon Estimation Based Stochastic Filtering of RC Circuit
This paper presents output voltage estimation of RC low pass circuit using the moving
horizon estimation (MHE) method. For this, we introduced a deterministic state-space model …
horizon estimation (MHE) method. For this, we introduced a deterministic state-space model …
A Deep Learning Method for Nonlinear Stochastic Filtering: Energy-Based Deep Splitting for Fast and Accurate Estimation of Filtering Densities
F Rydin - 2024 - odr.chalmers.se
In filtering the problem is to find the conditional distribution of a dynamically evolving state
given noisy measurements. Critically, designing accurate filters for nonlinear problems that …
given noisy measurements. Critically, designing accurate filters for nonlinear problems that …
Desarrollo de un modelo epidemiológico lagrangiano multiclase de tiempos de residencia y riesgos en ambientes, aplicado al brote de Covid-19 en la Región …
TA Catalán Muñoz - 2022 - repositorio.uchile.cl
La pandemia de COVID-19 ha sido un desafío sanitario a nivel mundial desde 2020,
causando más de 6 millones de muertes hacia marzo de 2022. Esta enfermedad se …
causando más de 6 millones de muertes hacia marzo de 2022. Esta enfermedad se …
Machine Learning Model for Predicting Number of COVID19 Cases in Countries with Low Number of Tests
S Hashim, S Farooq, E Syriopoulos, KL Cremer, A Vogt… - medRxiv, 2021 - medrxiv.org
The COVID-19 pandemic has presented a series of new challenges to governments and
health care systems. Testing is one important method for monitoring and therefore …
health care systems. Testing is one important method for monitoring and therefore …