logo-polimi
Loading...
Manifesto
Struttura Corso di Studi
Cerca/Visualizza Manifesto
Regolamento didattico
Indicatori corsi di studio
Internazionalizzazione
Orario Personalizzato
Il tuo orario personalizzato è disabilitato
Abilita
Ricerche
Cerca Docenti
Attività docente
Cerca Insegnamenti
Cerca insegnamenti degli Ordinamenti precedenti al D.M.509
Erogati in lingua Inglese
Le informazioni sulla didattica, sulla ricerca e sui compiti istituzionali riportate in questa pagina sono certificate dall'Ateneo; ulteriori informazioni, redatte a cura del docente, sono disponibili sulla pagina web personale e nel curriculum vitae indicati nella scheda.
Informazioni
DocenteBaraldi Piero
QualificaProfessore ordinario a tempo pieno
Dipartimento d'afferenzaDipartimento di Energia
Settore Scientifico DisciplinareIIND-07/D - Impianti Nucleari
Curriculum VitaeScarica il CV (772.8Kb - 13/04/2022)
OrcIDhttps://orcid.org/0000-0003-4232-4161

Contatti
Orario di ricevimento
DipartimentoPianoUfficioGiornoOrarioTelefonoFaxNote
energia------LunedìDalle 15:00
Alle 17:00
6345------
E-mailpiero.baraldi@polimi.it
Pagina web redatta a cura del docentewww.lasar.polimi.it

Fonte dati: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2026
Nessun prodotto attualmente registrato nell'anno 2026


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2025
Nessun prodotto attualmente registrato nell'anno 2025


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2024 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Contributo in Atti di convegno
Estimation of Real-Time Bottomhole Parameters in CO2 Injection Wells During Operations by Means of an Ensemble of Neural Networks (Mostra >>)
Mean Variance Estimation Neural Network Particle Filter for Predicting Battery Remaining Useful Life (Mostra >>)
Monografie o trattati scientifici
An Artificial Intelligence-Based Framework for Burn-in Reduction in the Semiconductor Manufacturing Industry (Mostra >>)
Articoli su riviste
A novel methodology based on long short-term memory stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing systems (Mostra >>)
Combining natural language processing and bayesian networks for the probabilistic estimation of the severity of process safety events in hydrocarbon production assets (Mostra >>)
Editoral on special issue “Text mining applied to risk analysis, maintenance and safety” (Mostra >>)
Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant (Mostra >>)
Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework (Mostra >>)
Physics-Informed deep Autoencoder for fault detection in New-Design systems (Mostra >>)
Sensitivity analysis by differential importance measure for unsupervised fault diagnostics (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2023 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Contributo in Atti di convegno
A Method based on Natural Language Processing for Periodically Estimating Variations of Performance of Safety Barriers in Hydrocarbon Production Assets (Mostra >>)
Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection (Mostra >>)
Optimization Method for an Improved Training of Physics Informed Neural Networks (Mostra >>)
Prediction of the Number of Defectives in a Production Batch of Semiconductor Devices (Mostra >>)
Articoli su riviste
Deep Multiadversarial Conditional Domain Adaptation Networks for Fault Diagnostics of Industrial Equipment (Mostra >>)
Guest Editorial: Special Issue of ESREL2020 PSAM15 (Mostra >>)
Maintenance optimization in industry 4.0 (Mostra >>)
Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning (Mostra >>)
The Aramis Data Challenge to prognostics and health management methods for application in evolving environments (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2022 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Contributi su volumi (Capitolo o Saggio)
Optimal Management of the Flow of Parts for Gas Turbines Maintenance by Reinforcement Learning and Artificial Neural Networks (Mostra >>)
Contributo in Atti di convegno
A Taxonomy for Modelling Reports of Process Safety Events in the Oil and Gas Industry (Mostra >>)
An Unsupervised Method for Anomaly Detection in Multi-Stage Production Systems Based on LSTM Autoencoders (Mostra >>)
Estimation of the Case Temperature of Insulated Gate Bipolar Temperatures in Induction Cooktops by Deep Neural Network (Mostra >>)
Monitoring Degradation of Insulated Gate Bipolar Transistors in Induction Cooktops by Artificial Neural Networks (Mostra >>)
Prediction of the Remaining Useful Life of MOSFETs Used in Automotive Inverters by an Ensemble of Neural Networks (Mostra >>)
Wrapper Selection of Features for Fault Diagnostics of Truss Structures (Mostra >>)
Articoli su riviste
A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data (Mostra >>)
A Novel Metric to Evaluate the Association Rules for Identification of Functional Dependencies in Complex Technical Infrastructures (Mostra >>)
A dynamic event tree for a blowout accident in an oil deep-water well equipped with a managed pressure drilling condition monitoring and operation system (Mostra >>)
A framework based on Natural Language Processing and Machine Learning for the classification of the severity of road accidents from reports (Mostra >>)
A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks (Mostra >>)
A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data (Mostra >>)
A two-stage estimation method based on Conceptors-aided unsupervised clustering and convolutional neural network classification for the estimation of the degradation level of industrial equipment (Mostra >>)
Generative Adversarial Networks With AdaBoost Ensemble Learning for Anomaly Detection in High-Speed Train Automatic Doors (Mostra >>)
Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning (Mostra >>)
manifesti v. 3.13.1 / 3.13.1
Area Servizi ICT
23/01/2026