Estimating pandemic effects in urban mass transportation systems: An approach based on visibility graphs and network similarity.

The COVID-19 pandemic has caused unprecedented disruptions to urban systems worldwide, but the extent and nature of these disruptions are not yet fully understood when it comes to transportation. In this work, we aim to explore how social distancing policies have affected passenger demand in urban mass transportation systems with the goal of supporting informed decisions in policy planning. We propose an approach based on complex networks and clustering time series with similar behavior, investigating possible changes in similarity patterns during pandemics and how they reflect into a regional scale.

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis.

This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems' safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data.

Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator.

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors' data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy.

Wavelet-Like Transform to Optimize the Order of an Autoregressive Neural Network Model to Predict the Dissolved Gas Concentration in Power Transformer Oil from Sensor Data.

Dissolved gas analysis (DGA) is one of the most important methods to analyze fault in power transformers. In general, DGA is applied in monitoring systems based upon an autoregressive model; the current value of a time series is regressed on past values of the same series, as well as present and past values of some exogenous variables. The main difficulty is to decide the order of the autoregressive model; this means determining the number of past values to be used.