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Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains : A Survey
No full textArtificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension
Balancing privacy and performance in federated learning : A systematic literature review on methods and metrics
No full textFederated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning directly to the edge of the user’s device. Nevertheless, new privacy issues have been raised particularly during training and the exchange of parameters between servers and clients. 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.This research work has been partially supported by the EU ECSEL project DAIS, which received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 101007273. Also, this research work has been funded by the Knowledge Foundation within the framework of the INDTECH (Grant Number 20200132) and INDTECH + Research School project (Grant Number 20220132), participating companies and Mälardalen University</p
A multi-platform approach for the comprehensive analysis of per- and polyfluoroalkyl substances (PFAS) and fluorine mass balance in commercial ski wax products
No full textThe unique properties of per- and polyfluoroalkyl substances (PFAS) have led to their extensive use in consumer products, including ski wax. Based on the risks associated with PFAS, and to align with PFAS regulations, the international ski federation (FIS) implemented a ban on products containing “C8 fluorocarbons/perfluorooctanoate (PFOA)” at all FIS events from the 2021/2022 season, leading manufactures to shift their formulations towards short-chain PFAS chemistries. To date, most studies characterising PFAS in ski waxes have measured a suite of individual substances using targeted analytical approaches. However, the fraction of total fluorine (TF) in the wax accounted for by these substances remains unclear. In this study, we sought to address this question by applying a multi-platform, fluorine mass balance approach to a total of 10 commercially available ski wax products. Analysis of TF by combustion ion chromatography (CIC) revealed concentrations of 1040–51700 μg F g−1 for the different fluorinated waxes. In comparison, extractable organic fluorine (EOF) determined in methanol extracts by CIC (and later confirmed by inductively-coupled plasma-mass spectrometry and 19F- nuclear magnetic resonance spectroscopy) ranged from 92 to 3160 μg g−1, accounting for only 3–8.8 % of total fluorine (TF). Further characterisation of extracts by cyclic ion mobility-mass spectrometry (IMS) revealed 15 individual PFAS with perfluoroalkyl carboxylic acid concentrations up to 33 μg F g−1, and 3 products exceeding the regulatory limit for PFOA (0.025 μg g−1) by a factor of up to 100. The sum of all PFAS accounted for only 0.01–1.0 % of EOF, implying a high percentage of unidentified PFAS, thus, pyrolysis gas chromatography-mass spectrometry was used to provide evidence of the nature of the non-extractable fluorine present in the ski wax products. RGV thanks Faculty of Science Uni Graz and the NAWI visiting program, VM thanks Macaulay Development Trust, LS thanks the project “POPFREE Industry – Towards a PFAS-free and circular industry” with funding from VINNOVA (grant number 2021-04200). Funding by the Land Steiermark Zukunftsfonds (project grand 1109 “Frontier NMR”) is also gratefully acknowledge. The authors acknowledge the financial support by the University of Graz. </p
Physicochemical metamorphosis of re-aerosolized urban PM2.5
No full textThe toxicity of particulate matter (PM) is dependent on particle physical and chemical properties and is commonly studied using in vivo and in vitro approaches. PM to be used for in vivo and in vitro studies is often collected on filters and then extracted from the filter surface using a solvent. During extraction and further PM sample handling, particle properties change, but this is often neglected in toxicology studies, with possible implications for health effect assessment. To address the current lack of knowledge and investigate changes in particle properties further, ambient PM with diameter less than 2.5 μm (PM2.5) was collected on filters at an urban site and extracted using a standard methanol protocol. After extraction, the PM was dried, dispersed in water and subsequently nebulized. The resulting aerosol properties were then compared to those of the ambient PM2.5. The number size distribution for the nebulized aerosol resembled the ambient in terms of the main mode diameter, and >90 % of particle mass in the nebulized size distribution was still in the PM2.5 range. Black carbon made up a similar fraction of PM mass in nebulized as in ambient aerosol. The sulfate content in the nebulized aerosol seemed depleted and the chemical composition of the organic fraction was altered, but it remains unclear to what extent other non-refractory components were affected by the extraction process. Trace elements were not distributed equally across size fractions, neither in ambient nor nebulized PM. Change in chemical form was studied for zinc, copper and iron. The form did not appear to be different between the ambient and nebulized PM for iron and copper, but seemed altered for zinc. Although many of the studied properties were reasonably well preserved, it is clear that the PM2.5 collection and re-aerosolization process affects particles, and thus potentially also their health effects. Because of this, the effect of the particle collection and extraction process must be considered when evaluating cellular and physiological outcomes upon PM2.5 exposure. © 2024 The AuthorsThis work was supported by Formas (2019-00320), The Crafoord Foundation (20200673) and AFA (160226)</p
Systematic implementation of an innovation strategy using ISO 56002
No full textAn innovation strategy aims to provide guidance in terms of direction and prioritisation regarding innovation efforts. This study explores formulation and implementation of innovation strategy in the context of a case study of an organisation that explicitly deploys the guidance standard for innovation management systems ISO56002. Interviews were conducted and were analysed together with an abundance of company documentation, spanning seven years. The empirical results convey how intertwined the work on innovation strategy was with the formulation and implementation of the company’s innovation management system (based on ISO56002). The study addressed the call for more research on strategy implementation and showed the innovation strategy (part of the ISO 56002 Leadership element) influencing the other system elements within the innovation management system. Further, it is important to use a system of systems approach to integrate an Innovation Management System with other management systems. This may be achieved through ambidextrous leadership competences given that the management systems have with different purposes, properties and actions. Finally, as an innovation management system develops, it is important to adapt rather than over-optimise in order to for retain flexibility required to innovate.
Realization of the pascal based on argon using a Fabry–Perot refractometer
No full textBased on a recent experimental determination of the static polarizability and a first-principle calculation of the frequency-dependent dipole polarizability of argon, this work presents, by using a Fabry–Perot refractometer operated at 1550 nm, a realization of the SI unit of pressure, the pascal, for pressures up to 100 kPa, with an uncertainty of [(1.0 mPa)2 + (5.8 × 10−6 P)2 + (26 × 10−12P2)2]1/2. The work also presents a value of the molar polarizability of N2 at 1550 nm and 302.9146 K of 4.396572(26) × 10−6 m3/mol, which agrees well with previously determined ones. Vetenskapsrådet (621-2020-05105); VINNOVA (2022-02948);European Partnership on Metrology (MQB-Pascal 22IEM04), which is cofinanced from the European Union’s Horizon Europe Research and InnovationProgramme and by the participating states.</p
Safety on automated passenger ships : Exploration of evacuation scenarios for coastal vessels
No full textMany advancements are being made within the domain of autonomous shipping, motivating discussions of corresponding amendments to international safety regulations within the International Maritime Organization. Near-coastal passenger ferries are a form of sea traffic that has been the target of automation trials due to their short voyages and relatively protected waters of operation. This study investigated emergency evacuation from a range of such ships, covering both the current situation (focused on crew tasks, external rescue actors and interactions) and safety aspects that should be considered when automation brings about new work patterns, such as remote supervision and control. The study employed qualitative methods – interviews, field visits and a stakeholder workshop. Results give insight into ferry evacuation processes and challenges in their current form. In addition, results from the application of different automated evacuation scenarios suggest that more detailed studies are needed within the areas of remote operation situation awareness, remote operator and onboard personnel competencies, passenger safety information and communication, simple and robust evacuation equipment, technical means allowing assistance between autonomous and regular ships, and lastly, both procedures and interfaces for collaboration in a changing rescue network.
Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid
No full textIn smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key. This work was partially supported by the EU ECSEL project DAISwhich has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 101007273.</p
Using the ACE framework to enforce access and usage control with notifications of revoked access rights
No full textThe standard ACE framework provides authentication and authorization mechanisms similar to those of the standard OAuth 2.0 framework, but it is intended for use in Internet-of-Things environments. In particular, ACE relies on OAuth 2.0, CoAP, CBOR, and COSE as its core building blocks. In ACE, a non-constrained entity called Authorization Server issues Access Tokens to Clients according to some access control and policy evaluation mechanism. An Access Token is then consumed by a Resource Server, which verifies the Access Token and lets the Client accordingly access a protected resource it hosts. Access Tokens have a validity which is limited over time, but they can also be revoked by the Authorization Server before they expire. In this work, we propose the Usage Control framework as an underlying access control means for the ACE Authorization Server, and we assess its performance in terms of time required to issue and revoke Access Tokens. Moreover, we implement and evaluate a method relying on the Observe extension for CoAP, which allows to notify Clients and Resource Servers about revoked Access Tokens. Through results obtained in a real testbed, we show how this method reduces the duration of illegitimate access to protected resources following the revocation of an Access Token, as well as the time spent by Clients and Resource Servers to learn about their Access Tokens being revoked. This work has been partially supported by: the Sweden’sInnovation Agency VINNOVA within the EUREKA CELTIC-NEXTproject CYPRESS; the H2020 project SIFIS-Home (grant agreement952652); and the SSF project SEC4Factory (grant RIT17-0032).</p
Machining Effect On The Surface Integrity And SE Of Additively Manufactured And Heat-Treated Nitinol
No full textNitinol belongs to the class of smart materials that have attracted the attention of researchers in recent decades due to their new promising industrial applications. Because of the austenite/martensite phase transformation, nitinol offers unique properties: superelasticity and shape memory effect. The former ability can be exploited for sensing, actuating, and damping applications. On the other hand, additive manufacturing of nitinol has started kicking off unimaginable applications exploiting the complexity-for-free characteristics offered by the 3D printing processes. Although stand-alone research on additive manufacturing of nitinol is available, the impact of different manufacturing steps, such as machining and heat treatment, on its superelasticity is severely lacking. This work used a powder bed fusion process using a laser beam to manufacture a Ni50.4Ti49.6 austenitic alloy, which was subsequently heat-treated at different aging temperatures. Subsequently, turning operations were carried out at varying cutting speeds under cryogenic cooling conditions. An in-depth characterization of the surface integrity and SE alterations induced by manufacturing was conducted before and after machining. The outcome of the work provides the best combination of heat treatment and machining parameters that allow for maximum surface integrity and SE. .This work was finically supported by PNRR research activities of the consortium “iNEST (Interconnected North-Est Innovation Ecosystem)” funded by the European Union NextGenerationEU (Piano Nazionale di Ripresa e Resilienza (PNRR) – Missione 4 Componente 2, Investimento 1.5 – D.D. 1058 23/06/2022, ECS_00000043) and by the PRIN project “NEMESI - 4D manufacturing based on 3D printing and machining for Nitinol biomedical and sensing applications” funded by the Italian Ministry of University and Research (MUR).</p