14 citations · 41 across the 37 of their papers we have counts for
9 papers · 1 filter
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
Tommaso Barbariol, Davide Masiero, Enrico Feltresi +1
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecastin…
Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito +1
The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from r…
Continual Learning Approaches for Anomaly Detection
Davide Dalle Pezze, Eugenia Anello, Chiara Masiero +1
Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task…
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito +1
Artificial Intelligence (AI) is one of the approaches that has been proposed to analyze the collected data (e.g., vibration signals) providing a diagnosis of the asset's operating…
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Davide Dalle Pezze, Denis Deronjic, Chiara Masiero +3
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classi…
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems
Elisa Marcelli, Tommaso Barbariol, Gian Antonio Susto
The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition o…
On the Properties of Adversarially-Trained CNNs
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts…
Learning to Rank from Relevance Judgments Distributions
Alberto Purpura, Gianmaria Silvello, Gian Antonio Susto
Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield…
Algorithmic Fairness Datasets: the Story so Far
Alessandro Fabris, Stefano Messina, Gianmaria Silvello +1
Data-driven algorithms are studied in diverse domains to support critical decisions, directly impacting people's well-being. As a result, a growing community of researchers has bee…