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Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance. In: Proceedings of the 15th European Wave and Tidal Energy Conference: . Paper presented at The 15th European Wave and Tidal Energy Conference.
Open this publication in new window or tab >>A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance
2023 (English)In: Proceedings of the 15th European Wave and Tidal Energy Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.

Keywords
Linear potential flow, machine learning, recurrent neural network, floating bodies, wave energy
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72107 (URN)10.36688/ewtec-2023-321 (DOI)
Conference
The 15th European Wave and Tidal Energy Conference
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). Estimation of nonlinear forces acting on floating bodies using machine learning. In: J. W. Ringsberg, C. Guedes Soares (Ed.), Advances in the Analysis and Design of Marine Structures: (pp. 63-72). CRC Press
Open this publication in new window or tab >>Estimation of nonlinear forces acting on floating bodies using machine learning
2023 (English)In: Advances in the Analysis and Design of Marine Structures / [ed] J. W. Ringsberg, C. Guedes Soares, CRC Press, 2023, p. 63-72Chapter in book (Other academic)
Abstract [en]

Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.

Place, publisher, year, edition, pages
CRC Press, 2023
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72114 (URN)10.1201/9781003399759 (DOI)9781003399759 (ISBN)
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems. In: The Proceedings of the 33rd International Ocean and Polar Engineering Conference: . Paper presented at The 33rd International Ocean and Polar Engineering Conference. , 33, Article ID 307.
Open this publication in new window or tab >>Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems
2023 (English)In: The Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023, Vol. 33, article id 307Conference paper, Published paper (Refereed)
Abstract [en]

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.

Keywords
Wave-body interaction; hierarchical modelling; linear potential flow; hybrid modeling; machine learning; recurrent neural net- work.
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72110 (URN)
Conference
The 33rd International Ocean and Polar Engineering Conference
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Kans, M., Ingwald, A., Strömberg, A.-B., Patriksson, M., Ekman, J., Holst, A. & Rudström, Å. (2022). Data Driven Maintenance: A Promising Way of Action for Future Industrial Services Management. In: Lecture Notes in Mechanical Engineering: . Paper presented at International Congress and Workshop on Industrial AI, IAI 2021, Virtual, Online, 6 October 2021 through 7 October 2021 (pp. 212-223). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Data Driven Maintenance: A Promising Way of Action for Future Industrial Services Management
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2022 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, p. 212-223Conference paper, Published paper (Refereed)
Abstract [en]

Maintenance and services of products as well as processes are pivotal for achieving high availability and avoiding catastrophic and costly failures. At the same time, maintenance is routinely performed more frequently than necessary, replacing possibly functional components, which has negative economic impact on the maintenance. New processes and products need to fulfil increased environmental demands, while customers put increasing demands on customization and coordination. Hence, improved maintenance processes possess very high potentials, economically as well as environmentally. The shifting demands on product development and production processes have led to the emergency of new digital solutions as well as new business models, such as integrated product-service offerings. Still, the general maintenance problem of how to perform the right service at the right time, taking available information and given limitations is valid. The project Future Industrial Services Management (FUSE) project was a step in a long-term effort for catalysing the evolution of maintenance and production in the current digital era. In this paper, several aspects of the general maintenance problem are discussed from a data driven perspective, spanning from technology solutions and organizational requirements to new business opportunities and how to create optimal maintenance plans. One of the main results of the project, in the form of a simulation tool for strategy selection, is also described.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Keywords
Data driven maintenance, Maintenance planning, Service-related business models, Simulation tool
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-59768 (URN)10.1007/978-3-030-93639-6_18 (DOI)2-s2.0-85125283057 (Scopus ID)9783030936389 (ISBN)
Conference
International Congress and Workshop on Industrial AI, IAI 2021, Virtual, Online, 6 October 2021 through 7 October 2021
Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2023-05-09Bibliographically approved
Eriksson, J., Nelson, D., Holst, A., Hellgren, E., Friman, O. & Oldner, A. (2021). Temporal patterns of organ dysfunction after severe trauma. Critical Care, 25(1), Article ID 165.
Open this publication in new window or tab >>Temporal patterns of organ dysfunction after severe trauma
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2021 (English)In: Critical Care, ISSN 1364-8535, E-ISSN 1466-609X, Vol. 25, no 1, article id 165Article in journal (Refereed) Published
Abstract [en]

Background: Understanding temporal patterns of organ dysfunction (OD) may aid early recognition of complications after trauma and assist timing and modality of treatment strategies. Our aim was to analyse and characterise temporal patterns of OD in intensive care unit-admitted trauma patients. Methods: We used group-based trajectory modelling to identify temporal trajectories of OD after trauma. Modelling was based on the joint development of all six subdomains comprising the sequential organ failure assessment score measured daily during the first two weeks post trauma. Further, the time for trajectories to stabilise and transition to final group assignments were evaluated. Results: Six-hundred and sixty patients were included in the final model. Median age was 40 years, and median ISS was 26 (IQR 17–38). We identified five distinct trajectories of OD. Group 1, mild OD (n = 300), median ISS of 20 (IQR 14–27), had an early resolution of OD and a low mortality. Group 2, moderate OD (n = 135), and group 3, severe OD (n = 87), were fairly similar in admission characteristics and initial OD but differed in subsequent OD trajectories, the latter experiencing an extended course and higher mortality. In group 3, 56% of the patients developed sepsis as compared with 19% in group 2. Group 4, extreme OD (n = 40), received most blood transfusions, had the highest proportion of shock at admission and a median ISS of 41 (IQR 29–50). They experienced significant and sustained OD affecting all organ systems and a 28-day mortality of 30%. Group 5, traumatic brain injury with OD (n = 98), had the highest mortality of 35% and the shortest time to death for non-survivors, median 3.5 (IQR 2.4–4.8) days. Groups 1 and 5 reached their final group assignment early, > 80% of the patients within 48 h. In contrast, groups 2 and 3 had a prolonged time to final group assignment. Conclusions: We identified five distinct trajectories of OD after severe trauma during the first two weeks post-trauma. Our findings underline the heterogeneous course after trauma and describe some potentially important clinical insights that are suggested by the groupings and temporal trajectories. © 2021, The Author(s).

Place, publisher, year, edition, pages
BioMed Central Ltd, 2021
Keywords
Clustering, Critical care, Data modelling, Multiple organ dysfunction, Trauma
National Category
Clinical Medicine
Identifiers
urn:nbn:se:ri:diva-53006 (URN)10.1186/s13054-021-03586-6 (DOI)2-s2.0-85105237671 (Scopus ID)
Note

Funding details: Karolinska Institutet, KI; Funding text 1: The Swedish Carnegie Hero Funds and funds from Karolinska Institute supported the study. Financial support was also provided through the regional agreement on medical and clinical research (ALF) between Stockholm County Council and Karolinska Institute. None of the funding agents were involved in the study design, data collection, data analysis, manuscript preparation, or publication decisions.

Available from: 2021-05-26 Created: 2021-05-26 Last updated: 2023-05-09Bibliographically approved
Holst, A., Bouguelia, M.-R. -., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019.
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. © 2019 Copyright held for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-38261 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2023-06-07Bibliographically approved
Holst, A., Pashami, S. & Bae, J. (2019). Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery, Inc
Open this publication in new window or tab >>Incremental causal discovery and visualization
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39672 (URN)10.1145/3304079.3310287 (DOI)2-s2.0-85069768142 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Note

Funding text 1: This research has been conducted within the “A Big Data Analytics Framework for a Smart Society" (BIDAF) project supported by the Swedish Knowledge Foundation.

Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2023-11-06Bibliographically approved
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery, Inc
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. © 2019 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39673 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2023-05-09Bibliographically approved
Bouguelia, M.-R., Karlsson, A., Pashami, S., Nowaczyk, S. & Holst, A. (2018). Mode tracking using multiple data streams. Information Fusion, 43, 33-46
Open this publication in new window or tab >>Mode tracking using multiple data streams
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2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed) Published
Abstract [en]

Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.

Keywords
Clustering, Data streams, Knowledge discovery, Mode tracking, Time series, Data mining, Information fusion, Software engineering, Complex environments, Data stream, Degrees of complexity, High level knowledge, Multiple data streams, Real-world datasets, Fleet operations
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-33228 (URN)10.1016/j.inffus.2017.11.011 (DOI)2-s2.0-85037072003 (Scopus ID)
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2023-11-06Bibliographically approved
Olsson, T. & Holst, A. (2015). A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications (7ed.). In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015): . Paper presented at 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US (pp. 434-439).
Open this publication in new window or tab >>A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
2015 (English)In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), 2015, 7, p. 434-439Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24430 (URN)2-s2.0-84958181138 (Scopus ID)9781577357308 (ISBN)
Conference
28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US
Projects
STREAM
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8577-6745

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