Development of prescriptive AnalYtics baseD on aRtificial intElligence for iAMS

Risultati finali

Evaluation and Validation Criteria and related KPIs

D61 will include the Scenarios the criteria the metrics and related KPIs to evaluate the IAMS prototype and to validate the TRL 5 IAMS prototype

Mathematical Computational Modelling

This deliverable will describe the selected optimisation modelling approaches to deal with the joint planning of infrastructure maintenance and alternative services and the integration with data analytics outcomes dealing with uncertainty

IAMS Prototype Integration Guidelines, User Requirements and Scenarios

This documental deliverable reports on the following aspects the guidelines for the development of the components delivered by WP2WP3WP4 in order to pave the way for the integration and to allow for a holistic and cohesive final IAMS prototype the user requirements the scenarios for the integrated IAMS as collected from the DAYDREAMS IMs and IN2SMART2 Finally this document will provide a first description of the datasets available for each scenario this description will be refined and finalised in D52

Context-driven Dynamic HMI Assessment

D43 will report on the assessment as executed in T43 and will detail the applied qualitative and quantitative metrics as well as their results

Data and Process Management Definition and Integration Architecture Design

This documental deliverable reports on the formalisation of the aspect related to process and design in particular the processesworkflows specifying the interaction between usersstakeholders and the shared catalogue and on the description of the relevant modules the global architecture design of the integrated IAMS prototype specifying how the different technologies and components designed and developed within WP2WP3WP4 are orchestrated Moreover the report also describes the collected datasets related to the different scenarios and how the shared catalogue together with the blockchain technologies can support the tracking and monitoring of the IAMS adoption and use

Report on Way Forward for Industrial Exploitation

D62 will report the best practices and the lessons learnt collected during DAYDREAMS evaluation and validation regarding the use of contextdriven HMI in combination with the AI and Machine Learningbased Prescriptive analytics methodologies and AIBased MultiObjective Optimisation in the railway domain

Sensitivity and Robustness Analysis Report

D63 will synthesise the feedback from validation on the sensitivity and robustness of the AI and Machine Learningbased Prescriptive analytics methodologies and AIBased MultiObjective Optimisation developed in WP2 and WP3 within the framework of the IAMS prototype evaluation and validation

Report on Artificial Intelligence Modelling

This deliverable will provide a description of AI tools developed for MultiObjective Decision Optimisation covering the output of Task 32

Analysis of the context and state-of-the-art

It will report the context and the stateoftheart of humanmachine interfaces for IAMS as employed by the users It will further build upon the scenarios defined in WP5

Learning from Data and Human Behaviour

This deliverable will describe the prescriptive analytics scenarios defined in Task 21 The deliverable will include the data availability the domain knowledge review the analytics perspectives and the scenariospecific metrics to optimise

Dissemination, communication and exploitation activity report

Report summarising all communication dissemination and exploitation activities and their impact

Context-driven Dynamic HMI Design and Prototype

D42 will report on the work of T42 and how the developed TRL4 prototype assesses the scenarios defined in WP5

Pubblicazioni

Enhancing Reproducibility and Trustability in Intelligent Asset Management Systems for the Railway Domani

Autori: Mario Scrocca, Ilaria Baroni, Alessio Carenini, Irene Celino
Pubblicato in: 2023
Editore: PUBLISSO-Fachrepositorium
DOI: 10.4126/frl01-006444996

On applying artificial intelligence techniques to maximise passengers comfort and infrastructure reliability in urban railway systems

Autori: Elise Amiel, Markos Anastasopoulos, Guillaume Chevaleyre, Alice Consilvio
Pubblicato in: MT-ITS 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems Nice, France, June 14-16, 2023, 2023
Editore: MT-ITS

Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment

Autori: Garrone, A. and Minisi, S. and Oneto, L. and Dambra, C. and Borinato, M. and Sanetti, P. and Vignola, G. and Papa, F. and Mazzino, N. and Anguita, D.
Pubblicato in: International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production (SysInt), 2022
Editore: Springer, Cham
DOI: 10.1007/978-3-031-16281-7_8

DAYDREAMS - Development of Prescriptive Analytics based on Artificial Intelligence for Railways Intelligent Asset Management Systems

Autori: Gogos, S. and Oneto, L. and Anastasopoulos, M. and Anguita, D. and Baroni, I. and Canepa, R. and Petralli, S. and Dambra, C. and Jentner, W
Pubblicato in: Transport Research Arena Conference, 2022
Editore: online

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets

Autori: Minisi, S. and Garrone, A. and Oneto, L. and Canepa, R. and Dambra, C. and Anguita, D.
Pubblicato in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2022
Editore: https://i6doc.com/en/
DOI: 10.14428/esann/2022.es2022-59

Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data

Autori: Jentner, Wolfgang and Lindholz, Giuliana and Hauptmann, Hanna and El-Assady, Mennatallah and Ma, Kwan-Liu and Keim, Daniel
Pubblicato in: ACM Transactions on Interactive Intelligent Systems, Numero Volume 13 Numero, 2023, Pagina/e 1-49
Editore: ACM
DOI: 10.1145/3579031

Risultati finali

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