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Lincke, A., Fagerström, C., Ekstedt, M., Löwe, W. & Backåberg, S. (2023). A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults. Health Informatics Journal, 29(4)
Open this publication in new window or tab >>A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults
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2023 (English)In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 29, no 4Article in journal (Refereed) Published
Abstract [en]

Background: Maintaining physical activity (PA) and functioning (mobility, balance) is essential for older adults’ well-being and quality of life. However, current methods (functional tests, self-reports) and available techniques (accelerometers, sensors, advanced movement analysis systems) for assessing physical activity and functioning have shown to be less reliable, time- and resource-consuming with limited routine usage in clinical practice. There is a need to simplify the assessment of physical activity and functioning among older adults both in health care and clinical studies. This work presents a study on using Skeleton Avatar Technology (SAT) for this assessment. SAT analyzes human movement videos using artificial intelligence (AI). The study compares handy SAT based on 2D camera technology (2D SAT) with previously studied 3D SAT for assessing physical activity and functioning in older adults. Objective: To explore whether 2D SAT yields accurate results in physical activity and functioning assessment in healthy older adults, statistically compared to the accuracy of 3D SAT. Method: The mobile pose estimation model provided by Tensorflow was used to extract 2D skeletons from the video recordings of functional test movements. Deep neural networks were used to predict the outcomes of functional tests (FT), expert-based movement quality assessment (EA), accelerometer-based assessments (AC), and self-assessments of PA (SA). To compare the accuracy with 3D SAT models, statistical analysis was used to test whether the difference in the predictions between 2D and 3D models is significant or not. Results: Overall, the accuracy of 2D SAT is lower than 3D SAT in predicting FTs and EA. 2D SAT was able to predict AC with 7% Mean Absolute Error (MAE), and self-assessed PA (SA) with 16% MAE. On average MAE was 4% higher for 2D than for 3D SAT. There was no significant difference found between the 2D and the 3D model for AC and for two FTs (30 seconds chair stand test, 30sCST and Timed up and go, TUG). A significant difference was found for the 2D- and 3D-model of another FT (4-stage balance test, 4SBT). Conclusion: Altogether, the results show that handy 2D SAT might be used for assessing physical activity in older adults without a significant loss of accuracy compared to time-consuming standard tests and to bulky 3D SAT-based assessments. However, the accuracy of 2D SAT in assessing physical functioning should be improved. Taken together, this study shows promising results to use 2D SAT for assessing physical activity in healthy older adults in future clinical studies and clinical practice.

Place, publisher, year, edition, pages
SAGE Open, 2023
Keywords
Physical activity, Skeleton avatar technology, machine learning, older adults, functioning mobility, balance
National Category
Sport and Fitness Sciences Computer Sciences
Research subject
Health and Caring Sciences, Health Informatics
Identifiers
urn:nbn:se:lnu:diva-125488 (URN)10.1177/14604582231214589 (DOI)001095930700001 ()2-s2.0-85176326223 (Scopus ID)
Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2024-01-18Bibliographically approved
Kumar, M., Cramsky, J., Löwe, W. & Danielsson, P.-O. (2023). A prediction model for exhaust gas regeneration(EGR) clogging using offline and online machinelearning. In: Karsten Berns, Klaus Dressler, Ralf Kalmar, Nicole Stephan, Roman Teutsch, Martin Thul (Ed.), Commercial Vehicle Technology 2022: Proceedings of 7th commercial vehicle technology symposium.. Paper presented at International Commercial Vehicle Technology Symposium (pp. 185-198). Wiesbaden: Springer
Open this publication in new window or tab >>A prediction model for exhaust gas regeneration(EGR) clogging using offline and online machinelearning
2023 (English)In: Commercial Vehicle Technology 2022: Proceedings of 7th commercial vehicle technology symposium. / [ed] Karsten Berns, Klaus Dressler, Ralf Kalmar, Nicole Stephan, Roman Teutsch, Martin Thul, Wiesbaden: Springer, 2023, p. 185-198Conference paper, Published paper (Refereed)
Abstract [en]

The exhaust gas regeneration (EGR) also called exhaust gasre-circulation system in an engine of construction machine (CM) oftengets clogged due to various ways of driving the machines. Currently, theredoes not exist any model that can predict clogging for maintenance planning.Hence, clogging is only recognized when it has occurred, and oftencauses the CM to drop out. Engines still operated despite clogging causesfrequent cold engine running, and excessive exhaustion of nitrogen, whichleads to loss of the engine's performance and reduces their lives.We propose an approach that builds on virtual key sensors. Virtual keysensors are usually used to replace real sensors. However, we proposeto compare the virtual and the real sensor outcomes. If di erences betweenthe estimated and the real value emerge, we assume changes ofthe systems because of, e.g., clogging or leakage in pipes. EGR pressureis identi ed as an important sensor to estimate clogging. A virtualsensor of EGR pressure is built from other real sensors based on a polynomialregression model [1]. The error between the real and the virtualEGR pressure sensors varies between 5-10% depending on the driver'sbehaviors. The model discriminates the ideal ways of working and abnormalities.Moreover, we suggest to adapt the weights of the regressionmodel to other engine types of the same engine family based on onlinestochastic gradient descent algorithm. Since the deployed regression andadaptation algorithms are computationally inexpensive, the approachcould be applied using existing CM micro controllers.

Place, publisher, year, edition, pages
Wiesbaden: Springer, 2023
Series
Proceedings, ISSN 2198-7432, E-ISSN 2198-7440 ; 7
Keywords
Polynomial regression, EGR, Predictive Model, Virtual sensor
National Category
Vehicle Engineering Control Engineering
Research subject
Computer and Information Sciences Computer Science, Media Technology; Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-119668 (URN)10.1007/978-3-658-40783-4_13 (DOI)9783658407827 (ISBN)9783658407834 (ISBN)
Conference
International Commercial Vehicle Technology Symposium
Funder
Knowledge Foundation
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-05-02Bibliographically approved
Lincke, A., Roth, J., Macedo, A. F., Bergman, P., Löwe, W. & Lagali, N. S. (2023). AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images. Translational Vision Science & Technology, 12(11), Article ID 29.
Open this publication in new window or tab >>AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images
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2023 (English)In: Translational Vision Science & Technology, E-ISSN 2164-2591, Vol. 12, no 11, article id 29Article in journal (Refereed) Published
Abstract [en]

Purpose: In vivo confocal microscopy (IVCM) of the cornea is a valuable tool for clinical assessment of the cornea but does not provide stand-alone diagnostic support. The aim of this work was to develop an artificial intelligence (AI)-based decision-support system (DSS) for automated diagnosis of Acanthamoeba keratitis (AK) using IVCM images.

Methods: The automated workflow for the AI-based DSS was defined and implemented using deep learning models, image processing techniques, rule-based decisions, and valuable input from domain experts. The models were evaluated with 5-fold-cross validation on a dataset of 85 patients (47,734 IVCM images from healthy, AK, and other disease cases) collected at a single eye clinic in Sweden. The developed DSS was validated on an additional 26 patients (21,236 images).

Results: Overall, the DSS uses as input raw unprocessed IVCM image data, successfully separates artefacts from true images (93% accuracy), then classifies the remaining images by their corneal layer (90% accuracy). The DSS subsequently predicts if the cornea is healthy or diseased (95% model accuracy). In disease cases, the DSS detects images with AK signs with 84% accuracy, and further localizes the regions of diagnostic value with 76.5% accuracy.

Conclusions: The proposed AI-based DSS can automatically and accurately preprocess IVCM images (separating artefacts and sorting images into corneal layers) which decreases screening time. The accuracy of AK detection using raw IVCM images must be further explored and improved.

Translational Relevance: The proposed automated DSS for experienced specialists assists in diagnosing AK using IVCM images.

Place, publisher, year, edition, pages
Association for research in vision and ophthalmology (ARVO), 2023
Keywords
image analysis, acanthamoeba keratitis, deep learning, in vivo confocal microscopy images
National Category
Computer Systems Medical Image Processing
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-125803 (URN)10.1167/tvst.12.11.29 (DOI)001125044500009 ()2-s2.0-85178009830 (Scopus ID)
Available from: 2023-11-28 Created: 2023-11-28 Last updated: 2024-01-16Bibliographically approved
Björneld, O., Carlsson, M. & Löwe, W. (2023). Case study: Feature engineering inspired by domain experts on real world medical data. Intelligence-Based Medicine, 8, Article ID 100110.
Open this publication in new window or tab >>Case study: Feature engineering inspired by domain experts on real world medical data
2023 (English)In: Intelligence-Based Medicine, ISSN 2666-5212, Vol. 8, article id 100110Article in journal (Refereed) Published
Abstract [en]

To perform data mining projects for knowledge discovery based on health data produced in a daily health care stored in electronic health records (EHR) can be time consuming. This study exemplifies that the involvement of a data scientist improves classification performances. We have performed a case study that comprises two real world medical research projects, comparing feature engineering and knowledge discovery based on classification performance. Project (P1) comprised 82,742 patients with the research question “Can we predict patient falls by use of EHR data” and the second project (P2) included 23,396 patients with the focus on “Negative side effects of antiepileptic drug consumption on bone structure”.

The results concluded three salient results. (i) It is valuable for medical researchers to involve a data scientist when medical research based on real world medical data is performed. The findings were justified with an analysis of classification metrics when iteratively engineered features were used. The features were generated from domain experts and computer scientists in collaboration with medical researchers. We gave this process the name domain knowledge-driven feature engineering (KDFE).

To evaluate the classification performance the metric area under the receiver operating characteristic curve (AUROC) was used. (ii) Domain experts are benefited in quantitative terms by KDFE. When KDFE was compared to baseline, the average classification performance measured by AUROC for the engineered features rose for P1 from 0.62 to 0.82 and for P2 from 0.61 to 0.89 (p-values << 0.001). (iii) The engineered features were represented in a systematic structure, which is the foundation of a theoretical model for automated KDFE (aKDFE).

To our knowledge, this is the first study that proves that via quantitative measures KDFE adds value to real-world. However, the method is not limited to the medical domain. Other areas with similar data properties should also benefit from KDFE.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Feature engineering, Medical registry research, Knowledge discovery in databases (KDD), Quantitative measures, Electronic health record (EHR), Domain knowledge
National Category
Information Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-125163 (URN)10.1016/j.ibmed.2023.100110 (DOI)2-s2.0-85173225229 (Scopus ID)
Available from: 2023-10-16 Created: 2023-10-16 Last updated: 2023-11-07Bibliographically approved
Kumar, M., Löwe, W., Cramsky, J. & Danielsson, P.-O. (2023). Driving pattern classification for wheel loaders in different materialhandling using machine learning. In: IEEE Transactions on Intelligent Transportation Systems: . Paper presented at IEEE ITSC-2023, Bilbao, Spain. IEEE
Open this publication in new window or tab >>Driving pattern classification for wheel loaders in different materialhandling using machine learning
2023 (English)In: IEEE Transactions on Intelligent Transportation Systems, IEEE, 2023Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The present paper discusses a new method ofclassifying kinds of material and driving stages and styles ofVolvo wheel loaders (WLO). This is achieved by indirectly monitoringrelevant, but usually latent variables, based on directlymonitored sensors of WLOs. The continuous classifications willsupport Volvo’s actual objectives such as, e.g., maximizingthe remaining useful life of components, fuel efficiency, andproductivity.To this end, a set of WLO machines was equipped with extrasensors and collected a limited dataset with richer information.Based on this limited dataset, different machine learning (ML)methods were tested to derive and to verify the classifications. Itshowed that support vector machines (SVM) produced the bestresults: the driving styles could be classified with a test accuracyof 77% (resp, 99.5%) in the loading (resp. unloading) drivingstage. Further the SVM model is also verified both theoreticallyto enhance the confidence in the model and experimentally witha set of additional test drivers.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE Transactions on Intelligent Transportation Systems, ISSN 1524-9050, E-ISSN 1524-9050
Keywords
Driving pattern classification, support vector machines (SVM), Wheel loader
National Category
Mechanical Engineering Control Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-125125 (URN)
Conference
IEEE ITSC-2023, Bilbao, Spain
Available from: 2023-10-11 Created: 2023-10-11 Last updated: 2024-01-10
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2023). Metrics As Scores: A Tool- and Analysis Suite and Interactive Application for Exploring Context-Dependent Distributions. Journal of Open Source Software, 8(88), Article ID 4913.
Open this publication in new window or tab >>Metrics As Scores: A Tool- and Analysis Suite and Interactive Application for Exploring Context-Dependent Distributions
2023 (English)In: Journal of Open Source Software, E-ISSN 2475-9066, Vol. 8, no 88, article id 4913Article in journal (Refereed) Published
Abstract [en]

Metrics As Scores can be thought of as an interactive, multiple analysis of variance (abbr. "ANOVA," Chambers et al., 2017). An ANOVA might be used to estimate the goodness-of-fit of a statistical model. Beyond ANOVA, which is used to analyze the differences among hypothesized group means for a single quantity (feature), Metrics As Scores seeks to answer the question of whether a sample of a certain feature is more or less common across groups. This approach to data visualization and -exploration has been used previously (e.g., Jiang etal., 2022). Beyond this, Metrics As Scores can determine what might constitute a good/bad, acceptable/alarming, or common/extreme value, and how distant the sample is from that value, for each group. This is expressed in terms of a percentile (a standardized scale of [0, 1]), which we call score. Considering all available features among the existing groups furthermore allows the user to assess how different the groups are from each other, or whether they are indistinguishable from one another. The name Metrics As Scores was derived from its initial application: examining differences of software metrics across application domains (Hönel et al., 2022). A software metric is an aggregation of one or more raw features according to some well-defined standard, method, or calculation. In software processes, such aggregations are often counts of events or certain properties (Florac & Carleton, 1999). However, without the aggregation that is done in a quality model, raw data (samples) and software metrics are rarely of great value to analysts and decision-makers. This is because quality models are conceived to establish a connection between software metrics and certain quality goals (Kaner & Bond, 2004). It is, therefore, difficult to answer the question "is my metric value good?". With Metrics As Scores we present an approach that, given some ideal value, can transform any sample into a score, given a sample of sufficiently many relevant values. While such ideal values for software metrics were previously attempted to be derived from, e.g., experience or surveys (Benlarbi et al., 2000), benchmarks (Alves et al., 2010), or by setting practical values (Grady, 1992), with Metrics As Scores we suggest deriving ideal values additionally in non-parametric, statistical ways. To do so, data first needs to be captured in a relevant context (group). A feature value might be good in one context, while it is less so in another. Therefore, we suggest generalizing and contextualizing the approach taken by Ulan et al. (2021), in which a score is defined to always have a range of [0, 1] and linear behavior. This means that scores can now also be compared and that a fixed increment in any score is equally valuable among scores. This is not the case for raw features, otherwise. Metrics As Scores consists of a tool- and analysis suite and an interactive application that allows researchers to explore and understand differences in scores across groups. The operationalization of features as scores lies in gathering values that are context-specific (group-typical), determining an ideal value non-parametrically or by user preference, and then transforming the observed values into distances. Metrics As Scores enables this procedure by unifying the way of obtaining probability densities/masses and conducting appropriate statistical tests. More than 120 different parametric distributions (approx. 20 of which are discrete) are fitted through a common interface. Those distributions are part of the scipy package for the Python programming language, which Metrics As Scores makes extensive use of (Virtanen et al., 2020). While fitting continuous distributions is straightforward using maximum likelihood estimation, many discrete distributions have integral parameters. For these, Metrics As Scores solves a mixed-variable global optimization problem using a genetic algorithm in pymoo (Blank& Deb, 2020). Additionally to that, empirical distributions (continuous and discrete) and smooth approximate kernel density estimates are available. Applicable statistical tests for assessing the goodness-of-fit are automatically performed. These tests are used to select some best-fitting random variable in the interactive web application. As an application written in Python, Metrics As Scores is made available as a package that is installable using the PythonPackage Index (PyPI): pip install metrics-as-scores. As such, the application can be used in a stand-alone manner and does not require additional packages, such as a web server or third-party libraries.

Place, publisher, year, edition, pages
Open Journals, 2023
Keywords
Metrics, Visualization, Conditional Distributions
National Category
Probability Theory and Statistics Software Engineering
Research subject
Statistics/Econometrics; Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-124881 (URN)10.21105/joss.04913 (DOI)
Available from: 2023-09-25 Created: 2023-09-25 Last updated: 2024-02-28Bibliographically approved
Hedenborg, M., Lundberg, J., Löwe, W. & Trapp, M. (2022). A Framework for Memory Efficient Context-Sensitive Program Analysis. Theory of Computing Systems, 66, 911-956
Open this publication in new window or tab >>A Framework for Memory Efficient Context-Sensitive Program Analysis
2022 (English)In: Theory of Computing Systems, ISSN 1432-4350, E-ISSN 1433-0490, Vol. 66, p. 911-956Article in journal (Refereed) Published
Abstract [en]

Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking.

In this paper we propose χ-terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. χ-terms are implemented as directed acyclic graphs without any redundant subgraphs. We introduce the k-approximation and the l-loop-approximation that limit the size of the context-sensitive information at the cost of analysis precision. We prove that every context-insensitive data-flow analysis has a corresponding k, l-approximated context-sensitive analysis, and that these analyses are sound and guaranteed to reach a fixed point.

We also present detailed algorithms outlining a compact, redundancy-free, and DAG-based implementation of χ-terms.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Static program analysis, Data-flow analysis, Context-sensitivity
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-115519 (URN)10.1007/s00224-022-10093-w (DOI)000826845200001 ()2-s2.0-85134543665 (Scopus ID)
Funder
Linnaeus University
Available from: 2022-07-18 Created: 2022-07-18 Last updated: 2023-04-11Bibliographically approved
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2022). Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?. In: Proceedings of the 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS): . Paper presented at 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), Guangzhou, China, 5-9 Dec. 2022 (pp. 333-343). IEEE
Open this publication in new window or tab >>Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?
2022 (English)In: Proceedings of the 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), IEEE, 2022, p. 333-343Conference paper, Published paper (Refereed)
Abstract [en]

Software quality models aggregate metrics to indicate quality. Most metrics reflect counts derived from events or attributes that cannot directly be associated with quality. Worse, what constitutes a desirable value for a metric may vary across contexts. We demonstrate an approach to transforming arbitrary metrics into absolute quality scores by leveraging metrics captured from similar contexts. In contrast to metrics, scores represent freestanding quality properties that are also comparable. We provide a web-based tool for obtaining contextualized scores for metrics as obtained from one’s software. Our results indicate that significant differences among various metrics and contexts exist. The suggested approach works with arbitrary contexts. Given sufficient contextual information, it allows for answering the question of whether a metric value is good/bad or common/extreme.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Conference on Software Quality, Reliability and Security (QRS), ISSN 2693-9185, E-ISSN 2693-9177
Keywords
Software quality, Metrics, Scores, Software Domains, Measurement, Aggregates, Software quality, Software reliability, Security, software metrics, absolute quality scores, arbitrary metrics, contextual operationalization, contextualized scores, quality properties, software quality models, Web-based tool
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer and Information Sciences Computer Science; Computer Science, Software Technology; Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-120165 (URN)10.1109/QRS57517.2022.00042 (DOI)2-s2.0-85151404427 (Scopus ID)9781665477048 (ISBN)
Conference
2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), Guangzhou, China, 5-9 Dec. 2022
Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-09-27Bibliographically approved
Picha, P., Hönel, S., Brada, P., Ericsson, M., Löwe, W., Wingkvist, A. & Danek, J. (2022). Process anti-pattern detection: a case study. In: Proceedings of the 27th European Conference on Pattern Languages of Programs, EuroPLop 2022, Irsee, Germany, July 6-10, 2022: . Paper presented at EuroPLop '22: Proceedings of the 27th European Conference on Pattern Languages of Programs, Irsee, Germany, July 6-10, 2022 (pp. 1-18). ACM Publications, Article ID 5.
Open this publication in new window or tab >>Process anti-pattern detection: a case study
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2022 (English)In: Proceedings of the 27th European Conference on Pattern Languages of Programs, EuroPLop 2022, Irsee, Germany, July 6-10, 2022, ACM Publications, 2022, p. 1-18, article id 5Conference paper, Published paper (Refereed)
Abstract [en]

Anti-patterns are harmful phenomena repeatedly occurring, e.g., in software development projects. Though widely recognized and well-known, their descriptions are traditionally not fit for automated detection. The detection is usually performed by manual audits, or on business process models. Both options are time-, effort- and expertise-heavy, prone to biases, and/or omissions. Meanwhile, collaborative software projects produce much data as a natural side product, capturing their status and day-to-day history. Long-term, our research aims at deriving models for the automated detection of process and project management anti-patterns, applicable to project data. Here, we present a general approach for studies investigating occurrences of these types of anti-patterns in projects and discuss the entire process of such studies in detail, starting from the anti-pattern descriptions in literature. We demonstrate and verify our approach with the Fire Drill anti-pattern detection as a case study, applying it to data from 15 student projects. The results of our study suggest that reliable detection of at least some process and project management anti-patterns in project data is possible, with 13 projects assessed accurately for Fire Drill presence by our automated detection when compared to the ground truth gathered from independent data. The overall approach can be similarly applied to detecting patterns and other phenomena with manifestations in Application Lifecycle Management data.

Place, publisher, year, edition, pages
ACM Publications, 2022
Keywords
Pattern detection, Project management anti-patterns, Software process anti-patterns, ALM tools, Fire Drill
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-120164 (URN)10.1145/3551902.3551965 (DOI)2-s2.0-85148442751 (Scopus ID)9781450395946 (ISBN)
Conference
EuroPLop '22: Proceedings of the 27th European Conference on Pattern Languages of Programs, Irsee, Germany, July 6-10, 2022
Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-09-27Bibliographically approved
Weyns, D., Andersson, J., Caporuscio, M., Flammini, F., Kerren, A. & Löwe, W. (2021). A Research Agenda for Smarter Cyber-Physical Systems. Journal of Integrated Design & Process Science, 25(2), 27-47
Open this publication in new window or tab >>A Research Agenda for Smarter Cyber-Physical Systems
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2021 (English)In: Journal of Integrated Design & Process Science, ISSN 1092-0617, E-ISSN 1875-8959, Vol. 25, no 2, p. 27-47Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IOS Press, 2021
Keywords
Smarter systems, trustworthiness, sustainability, cyber-physical systems, self-adaptation
National Category
Computer Sciences Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-106841 (URN)10.3233/JID210010 (DOI)000806149900002 ()2-s2.0-85122031282 (Scopus ID)2021 (Local ID)2021 (Archive number)2021 (OAI)
Available from: 2021-09-07 Created: 2021-09-07 Last updated: 2022-09-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7565-3714

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