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Flammini, Francesco, Senior LecturerORCID iD iconorcid.org/0000-0002-2833-7196
Publications (10 of 137) Show all publications
Pappaterra, M. J., Pappaterra, M. L. & Flammini, F. (2024). A study on the application of convolutional neural networks for the maintenance of railway tracks. Discover Artificial Intelligence, 4(1), Article ID 30.
Open this publication in new window or tab >>A study on the application of convolutional neural networks for the maintenance of railway tracks
2024 (English)In: Discover Artificial Intelligence, ISSN 2731-0809, Vol. 4, no 1, article id 30Article in journal (Refereed) Published
Abstract [en]

This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Artificial Intelligence, Convolutional Neural Networks, Datasets, Literature review, Maintenance, Railways, Convolution, Railroad tracks, Railroads, Convolutional neural network, Dataset, Literature reviews, Railway, Railway maintenance, Railway track, State of the art, Subdomain, Track components, Track maintenance
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-66617 (URN)10.1007/s44163-024-00127-2 (DOI)2-s2.0-85191945832 (Scopus ID)
Available from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-05-15Bibliographically approved
Mohammadi, S., Balador, A., Sinaei, S. & Flammini, F. (2024). Balancing Privacy and Performance in Federated Learning: a Systematic Literature Review on Methods and Metrics. Journal of Parallel and Distributed Computing, 192
Open this publication in new window or tab >>Balancing Privacy and Performance in Federated Learning: a Systematic Literature Review on Methods and Metrics
2024 (English)In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 192Article in journal (Refereed) Submitted
Abstract [en]

Federated Learning (FL) has emerged as a novel paradigm in the area of Artificial Intelligence (AI), emphasizing decentralized data utilization and bringing learning to the edge or directly on-device. While this approach eliminates the need for data centralization, ensuring enhanced privacy and protection of sensitive information, it is not without challenges. Particularly during the training phase and the exchange of model update parameters between servers and clients, new privacy challenges have arisen. While several privacy-preserving FL solutions have been developed to mitigate potential breaches in FL architectures, their integration poses its own set of challenges. Incorporating these privacy-preserving mechanisms into FL at the edge computing level can increase both communication and computational overheads, which may, in turn, compromise data utility and learning performance metrics. This paper provides a systematic literature review on essential methods and metrics to support the most appropriate trade-offs between FL privacy and other performance-related application requirements such as accuracy, loss, convergence time, utility, communication, and computation overhead. We aim to provide an extensive overview of recent privacy-preserving mechanisms in FL used across various applications, placing a particular focus on quantitative privacy assessment approaches in FL and the necessity of achieving a balance between privacy and the other requirements of real-world FL applications. This review collects, classifies, and discusses relevant papers in a structured manner, emphasizing challenges, open issues, and promising research directions. 

Place, publisher, year, edition, pages
Academic Press Inc., 2024
Keywords
Cybersecurity, Distributed artificial intelligence, Federated learning, Performance evaluation, Trustworthiness
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-64363 (URN)10.1016/j.jpdc.2024.104918 (DOI)001246744100001 ()2-s2.0-85194089881 (Scopus ID)
Available from: 2023-09-26 Created: 2023-09-26 Last updated: 2024-07-03Bibliographically approved
Dirnfeld, R., De Donato, L., Somma, A., Azari, M. S., Marrone, S., Flammini, F. & Vittorini, V. (2024). Integrating AI and DTs: challenges and opportunities in railway maintenance application and beyond. Simulation (San Diego, Calif.)
Open this publication in new window or tab >>Integrating AI and DTs: challenges and opportunities in railway maintenance application and beyond
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2024 (English)In: Simulation (San Diego, Calif.), ISSN 0037-5497, E-ISSN 1741-3133Article in journal (Refereed) Epub ahead of print
Abstract [en]

In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber-physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD, 2024
Keywords
Digital twin, railway, artificial intelligence, machine learning, cyber-physical system, Internet of things
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66178 (URN)10.1177/00375497241229756 (DOI)001163924700001 ()2-s2.0-85185910530 (Scopus ID)
Available from: 2024-03-06 Created: 2024-03-06 Last updated: 2024-03-06Bibliographically approved
Zahid, M., Bucaioni, A. & Flammini, F. (2024). Model-based Trustworthiness Evaluation of Autonomous Cyber-Physical Production Systems: A Systematic Mapping Study. ACM Computing Surveys, 56(6), Article ID 157.
Open this publication in new window or tab >>Model-based Trustworthiness Evaluation of Autonomous Cyber-Physical Production Systems: A Systematic Mapping Study
2024 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, no 6, article id 157Article in journal (Refereed) Published
Abstract [en]

The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) have enabled increased autonomy and closer human-robot cooperation in the production and manufacturing industry, thus leading to Autonomous Cyber-Physical Production Systems (ACPPS) and paving the way to the fifth industrial revolution (i.e., Industry 5.0). ACPPS are increasingly critical due to the possible consequences of their malfunctions on human co-workers, and therefore, evaluating their trustworthiness is essential. This article reviews research trends, relevant attributes, modeling languages, and tools related to the model-based trustworthiness evaluation of ACPPS. As in many other engineering disciplines and domains, model-based approaches, including stochastic and formal analysis tools, are essential to master the increasing complexity and criticality of ACPPS and to prove relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2024
Keywords
Autonomous cyber-physical production systems, cyber-physical manufacturing systems, industry 4.0, industry 5.0, automation, trustworthiness, models, mapping study
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-66410 (URN)10.1145/3640314 (DOI)001208566200023 ()2-s2.0-85188966114 (Scopus ID)
Available from: 2024-04-10 Created: 2024-04-10 Last updated: 2024-05-20Bibliographically approved
D’Aniello, G., Gaeta, M., Flammini, F. & Fortino, G. (2024). Situation Awareness in the Cloud-Edge Continuum. In: Lecture Notes on Data Engineering and Communications Technologies: (pp. 307-316). Springer Science and Business Media Deutschland GmbH, 203
Open this publication in new window or tab >>Situation Awareness in the Cloud-Edge Continuum
2024 (English)In: Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH , 2024, Vol. 203, p. 307-316Chapter in book (Other academic)
Abstract [en]

Edge Computing is becoming a promising computing paradigm that addresses the limitations of cloud computing concerning latency, autonomy, and costs. To facilitate more intelligent applications made possible by the Edge Computing paradigm, it is essential to integrate intelligence and adaptability into devices located at the network’s edge. The paper explores the potential integration of Situation Awareness (SA) capabilities into the Cloud-Edge continuum. This integration aims to empower smarter applications while effectively managing challenges related to low latency, high autonomy, and cost-effective solutions. Within our illustrative example in healthcare, we showcase how the proposed SA cloud-edge continuum architecture enables efficient data processing and decision-making.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Cost effectiveness, Data handling, Decision making, Cloud-computing, Computing paradigm, Cost-effective solutions, Decisions makings, Edge computing, Intelligent applications, Low latency, Situation awareness, Smart applications
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-66557 (URN)10.1007/978-3-031-57931-8_30 (DOI)2-s2.0-85191317442 (Scopus ID)
Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-14Bibliographically approved
Flammini, F., Alcaraz, C., Bellini, E., Marrone, S., Lopez, J. & Bondavalli, A. (2024). Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives. IEEE Transactions on Emerging Topics in Computing, 12(2), 601-614
Open this publication in new window or tab >>Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives
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2024 (English)In: IEEE Transactions on Emerging Topics in Computing, E-ISSN 2168-6750, Vol. 12, no 2, p. 601-614Article in journal (Refereed) Published
Abstract [en]

The class of Trustworthy Autonomous Systems (TAS) includes cyber-physical systems leveraging on self-x technologies that make them capable to learn, adapt to changes, and reason under uncertainties in possibly critical applications and evolving environments. In the last decade, there has been a growing interest in enabling artificial intelligence technologies, such as advanced machine learning, new threats, such as adversarial attacks, and certification challenges, due to the lack of sufficient explainability. However, in order to be trustworthy, those systems also need to be dependable, secure, and resilient according to well-established taxonomies, methodologies, and tools. Therefore, several aspects need to be addressed for TAS, ranging from proper taxonomic classification to the identification of research opportunities and challenges. Given such a context, in this paper address relevant taxonomies and research perspectives in the field of TAS. We start from basic definitions and move towards future perspectives, regulations, and emerging technologies supporting development and operation of TAS.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024
Keywords
Taxonomy, Resilience, Computer security, Unified modeling language, Analytical models, Safety, Trustworthy autonomous systems, dependability, cyber-resilience, cybersecurity, artificial intelligence, intelligent systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-67894 (URN)10.1109/TETC.2022.3227113 (DOI)001247143600022 ()
Available from: 2024-06-26 Created: 2024-06-26 Last updated: 2024-06-26Bibliographically approved
Faramondi, L., Flammini, F., Guarino, S. & Setola, R. (2023). A hybrid behavior- and Bayesian network-based framework for cyber–physical anomaly detection. Computers & electrical engineering, 112, Article ID 108988.
Open this publication in new window or tab >>A hybrid behavior- and Bayesian network-based framework for cyber–physical anomaly detection
2023 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 112, article id 108988Article in journal (Refereed) Published
Abstract [en]

In recent years, the increasing Internet connectivity and heterogeneity of industrial protocols have been raising the number and nature of cyber-attacks against Industrial Control Systems (ICS). Such cyber-attacks may lead to cyber anomalies and further to the failure of physical components, thus leading to cyber–physical attacks. In this paper, we present a novel unsupervised cyber–physical anomaly detection framework based on a hybrid “multi-formalism” approach that combines the outcomes of multiple unsupervised behavior-based anomaly detectors through a Bayesian network-based probabilistic modeling of the ICS. More precisely, the framework consists of two behavior-based anomaly detection modules that monitor separately and simultaneously the behavior of cyber and physical data acquired from the ICS. The outputs of such modules are filtered and combined through a Bayesian network-based modeling in order to improve the trustworthiness of the detected anomalies and to provide the detection probability of cyber, physical, and cyber–physical anomalies, taking into account possible cascading effects over the cyber–physical process. The outcomes achieved through the implementation of our framework on the hardware-in-the-loop Water Distribution Testbed (WDT) dataset show very high detection performance with a strong ability to reject false positive events and to isolate and localize the anomalies over the cyber–physical process.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-64624 (URN)10.1016/j.compeleceng.2023.108988 (DOI)001098266200001 ()2-s2.0-85174801411 (Scopus ID)
Available from: 2023-10-27 Created: 2023-10-27 Last updated: 2023-12-07Bibliographically approved
Pappaterra, M. J. & Flammini, F. (2023). A Review of Intelligent Infrastructure Surveillance to Support Safe Autonomy in Smart-Railways. In: IEEE Conf Intell Transport Syst Proc ITSC: . Paper presented at IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 5603-5610). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A Review of Intelligent Infrastructure Surveillance to Support Safe Autonomy in Smart-Railways
2023 (English)In: IEEE Conf Intell Transport Syst Proc ITSC, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 5603-5610Conference paper, Published paper (Refereed)
Abstract [en]

Since railways are considered among the most critical transportation infrastructures, significant research exists on the use of modern technology for their safety and security. In order to enable full autonomy of railway operations, it is not only important to guarantee safety from the control system perspective, but also to ensure that external threats of both intentional and natural origin are detected automatically. This literature review highlights the potential of Artificial Intelligence (AI) in physical infrastructure security and surveillance of autonomous railway operations. We survey AI applications to identify unauthorized access to restricted areas, as well as intelligent monitoring against natural disasters. We also discuss the use of AI to improve situational awareness, in order to analyze data from various sources to prevent incidents, provide real-time information to decision-makers, and support emergency response. Finally, we highlight the challenges to overcome, including data quality, privacy concerns, regulations, and other technical limitations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Disaster prevention, Disasters, Railroads, Area monitoring, Infrastructure security, Intelligent infrastructures, Literature reviews, Modern technologies, Railway operations, Safety and securities, Security and surveillances, Transportation infrastructures, Unauthorized access, Decision making
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66242 (URN)10.1109/ITSC57777.2023.10422317 (DOI)001178996705098 ()2-s2.0-85186497675 (Scopus ID)9798350399462 (ISBN)
Conference
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Available from: 2024-03-19 Created: 2024-03-19 Last updated: 2024-06-19Bibliographically approved
Azari, M. S., Flammini, F., Santini, S. & Caporuscio, M. (2023). A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0. IEEE Access, 11, 12887-12910
Open this publication in new window or tab >>A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 12887-12910Article, review/survey (Refereed) Published
Abstract [en]

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023
Keywords
Maintenance engineering, Predictive maintenance, Prognostics and health management, Transfer learning, Artificial intelligence, Fault diagnosis, domain adaptation, fault detection, fault prognosis
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-62029 (URN)10.1109/ACCESS.2023.3239784 (DOI)000933765200001 ()2-s2.0-85147287812 (Scopus ID)
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-03-08Bibliographically approved
De Donato, L., Tang, R., Bešinović, N., Flammini, F., M.P Goverde, R., Lin, Z., . . . Vittorini, V. (2023). Artificial intelligence in railways: current applications, challenges, and ongoing research. In: Hussein Dia (Ed.), Handbook on Artificial Intelligence and Transport: (pp. 249-283). Edward Elgar Publishing
Open this publication in new window or tab >>Artificial intelligence in railways: current applications, challenges, and ongoing research
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2023 (English)In: Handbook on Artificial Intelligence and Transport / [ed] Hussein Dia, Edward Elgar Publishing, 2023, p. 249-283Chapter in book (Refereed)
Abstract [en]

This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.

Place, publisher, year, edition, pages
Edward Elgar Publishing, 2023
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-64588 (URN)10.4337/9781803929545.00017 (DOI)2-s2.0-85178989533 (Scopus ID)9781803929538 (ISBN)9781803929545 (ISBN)
Available from: 2023-10-26 Created: 2023-10-26 Last updated: 2023-12-20Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2833-7196

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