Analysis and Prediction for Confirmed COVID-19 Cases in Czech Republic with Uncertain Logistic Growth Model

This paper presents an uncertain logistic growth model to analyse and predict the evolution of the cumulative number of COVID-19 infection in Czech Republic. Some fundamental knowledge about the uncertain regression analysis are reviewed firstly. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID-19 cases in Czech Republic, by considering the disturbance term as random variables, because that the normality test and the identical distribution test of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. In this case, the uncertain logistic growth model is applied by characterizing the disturbance term as uncertain variables. Then parameter estimation, residual analysis, the forecast value and confidence interval are studied. Additionally, the uncertain hypothesis test is proposed to evaluate the appropriateness of the fitted logistic growth model and estimated disturbance term. The analysis and prediction for the cumulative number of COVID-19 infection in Czech Republic can propose theoretical support for the disease control and prevention. Due to the symmetry and similarity of epidemic transmission, other regions of COVID-19 infections, or other diseases can be disposed in a similar theory and method.

Operational Based Stochastic Cluster Regression-Based Modeling for Predicting Condition Rating of Highway Tunnels

The aim of this research and proposed contribution are investigating and modeling the impact of explanatory variables and non-periodic maintenance effect on highway tunnels condition. The stochastic regression analysis has been conducted to come out with a realistic tunnels condition through the Monte Carlo simulation methodology. The research methodology consists of three phases: cluster analysis, regression modeling, and stochastic analysis. A data set of 473 highway tunnels along 41 American states from the National Tunnels Inventory (NTI) has been used. Nine models have been developed with a high coefficient of determination (R2=90.8%). The obtained results and the models could push the wheel towards developing tunnel deterioration models from a management perspective, not only from the structural view. The developed models help highway authorities to prioritize the maintenance and objectively make informed investment decisions based on the historical data.

Stochastic Algorithm for Optimal Placement of STATCOM in a DG Interconnection System

The advanced static synchronous compensator or Ad-STATCOM is a reactive voltage control device which when connected to the bus provides reactive power support by maintaining proper voltage level at each bus terminal. A new control scheme for optimizing the STATCOM based on Stochastic Machine Learning Algorithm is presented in this paper. The design of STATCOM controller for controlling reactive power does not depend on the Power System Parameters. All the design parameters were updated in this paper through a constant stochastic regression optimization. The design parameters only depend on the local values which are collected from history sheet or previous such case. A MATLAB Simulink model was designed and the model shows effectiveness on maintaining voltage level and thereby avoiding the voltage collapse.