| 51. |
- Pagliari, Lorenzo, et al.
(författare)
-
Performance modelling of intelligent transportation systems : Experience report
- 2021
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Ingår i: ICPE '21. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450383318 ; , s. 155-160
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Konferensbidrag (refereegranskat)abstract
- Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.
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| 52. |
- Pagliari, Lorenzo, et al.
(författare)
-
To what extent formal methods are applicable for performance analysis of smart cyber-physical systems?
- 2019
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Ingår i: ECSA '19: Proceedings of the 13th European Conference on Software Architecture. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450371421 ; , s. 139-144
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Konferensbidrag (refereegranskat)abstract
- The dynamic nature of complex Cyber-Physical Systems (CPS) introduces new research challenges since they need to smartly deal with changing situations in their environment. This triggers the usage of methodologies that keep track of changes and raise alarms whether extra-functional requirements (e.g., safety, reliability, performance) are violated. In this context, we investigate the usage of formal methods as support to provide a model-based performance evaluation of smart CPS. The main goal is to understand to what extent well-known performance models, specifically Queueing Networks, are suitable to represent these dynamic scenarios.
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| 53. |
- Saman Azari, Mehdi, et al.
(författare)
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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 12887-12910
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Forskningsöversikt (refereegranskat)abstract
- 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.
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| 54. |
- Saman Azari, Mehdi, et al.
(författare)
-
Data-Driven Fault Diagnosis of Once-through Benson Boilers
- 2019
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Ingår i: 2019 4th International Conference on System Reliability and Safety (ICSRS). - : IEEE Press. - 9781728147819 - 9781728147802 ; , s. 345-354
-
Konferensbidrag (refereegranskat)abstract
- Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario.
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| 55. |
- Singh, Prasannjeet, et al.
(författare)
-
Towards self-healing in the internet of things by log analytics and process mining
- 2020
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Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 4644-4651
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing. © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
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| 56. |
- Singh, Prasannjeet, et al.
(författare)
-
Using log analytics and process mining to enable self-healing in the Internet of Things
- 2022
-
Ingår i: Environment Systems and Decisions. - : Springer. - 2194-5403 .- 2194-5411. ; 42:2, s. 234-250
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT.
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| 57. |
- Stafford, Judith A., et al.
(författare)
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The Application of Dependence Analysis to Software Architecture Descriptions
- 2003
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Ingår i: Formal Methods for Software Architectures. - Berlin, Heidelberg : Springer. - 9783540200833 ; , s. 52-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- As the focus of software design shifts increasingly toward the architectural level, so too are its analysis techniques. Dependence analysis is one such technique that shows promise at this level. In this paper we briefly describe and illustrate the application of dependence analysis to architectural descriptions of software systems.
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| 58. |
- Weyns, Danny, et al.
(författare)
-
A Research Agenda for Smarter Cyber-Physical Systems
- 2021
-
Ingår i: Journal of Integrated Design & Process Science. - Amsterdam, Netherlands : IOS Press BV. - 1092-0617 .- 1875-8959. ; 25:2, s. 27-47
-
Tidskriftsartikel (refereegranskat)abstract
- With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.
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| 59. |
- Weyns, Danny, et al.
(författare)
-
BoConnect – Reliable Assistive Technologies to Empower Elderly People to Live Independently Longer
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- BoConnect was a multi-disciplinary collaborative research project between Linnaeus University and Växjö and Kalmarmunicipalities in Sweden. The project had a budget of 3 MSEK; it stated Jan. 2015 and ended Dec. 2016.The project studied reliable assistive technologies to support elderly people. In contrast to existing work in this areathatis often limited to small-scale technology-driven pilots that are poorly embedded in the social living context, the BoConnect project took a holistic perspective on assistive technologies and put user needs and reliability of the solutions in focus, both from a technological and organizational perspective. This report summarizes the main results of the project.
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| 60. |
- Weyns, Danny, et al.
(författare)
-
Design for Sustainability = Runtime Adaptation ∪ Evolution
- 2015
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Ingår i: Proceedings of the 2015 European Conference on Software Architecture Workshops;62. - New York, NY, USA : ACM Digital Library. - 9781450333931 ; , s. 1-7
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Continuous change changes everything; it introduces various uncertainties, which may harm the sustainability of software systems. We argue that integrating runtime adaptation and evolution is crucial for the sustainability of software sys- tems. Realising this integration calls for a radical change in the way software is developed and operated. Our posi- tion is that we need to Design for Sustainability. To that end, we present: (i) the AdEpS model (Adaptation and Evo- lution processes for Sustainability) to handle and mitigate uncertainties by means of integrating runtime adaptation and evolution, and (ii) a set of engineering principles to de- sign software systems that facilitate the application of the AdEpS model to build sustainable software.
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| 61. |
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