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Rögnvaldsson, ThorsteinnORCID iD iconorcid.org/0000-0001-5163-2997
Publications (10 of 58) Show all publications
Rögnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R. & Svensson, M. (2018). Self-monitoring for maintenance of vehicle fleets. Data mining and knowledge discovery, 32(2), 344-384
Open this publication in new window or tab >>Self-monitoring for maintenance of vehicle fleets
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2018 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, no 2, p. 344-384Article in journal (Refereed) Published
Abstract [en]

An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g. a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost four years. © 2017 The Author(s)

Place, publisher, year, edition, pages
New York: Springer-Verlag New York, 2018
Keywords
Data Mining, Knowledge Discovery, Empirical Studies, Vehicle Fleet Maintenance
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-34746 (URN)10.1007/s10618-017-0538-6 (DOI)2-s2.0-85027693423 (Scopus ID)
Projects
ReDi2ServiceCAISR
Funder
VINNOVAKnowledge Foundation
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-02-27Bibliographically approved
Manasa, J., Varghese, V., Kosakovsky Pond, S., Rhee, S.-Y., Tzou, P., Fessel, J., . . . Shafer, R. A. (2017). Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors. Scientific Reports, 7(1), Article ID 11559.
Open this publication in new window or tab >>Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, no 1, article id 11559Article in journal (Refereed) Published
Abstract [en]

Several groups have proposed that genotypic determinants in gag and the gp41 cytoplasmic domain (gp41-CD) reduce protease inhibitor (PI) susceptibility without PI-resistance mutations in protease. However, no gag and gp41-CD mutations definitively responsible for reduced PI susceptibility have been identified in individuals with virological failure (VF) while receiving a boosted PI (PI/r)-containing regimen. To identify gag and gp41 mutations under selective PI pressure, we sequenced gag and/or gp41 in 61 individuals with VF on a PI/r (n = 40) or NNRTI (n = 20) containing regimen. We quantified nonsynonymous and synonymous changes in both genes and identified sites exhibiting signal for directional or diversifying selection. We also used published gag and gp41 polymorphism data to highlight mutations displaying a high selection index, defined as changing from a conserved to an uncommon amino acid. Many amino acid mutations developed in gag and in gp41-CD in both the PI- and NNRTI-treated groups. However, in neither gene, were there discernable differences between the two groups in overall numbers of mutations, mutations displaying evidence of diversifying or directional selection, or mutations with a high selection index. If gag and/or gp41 encode PI-resistance mutations, they may not be confined to consistent mutations at a few sites. © 2017 The Author(s).

Place, publisher, year, edition, pages
London: Nature Publishing Group, 2017
Keywords
Antivirals, Retrovirus, Viral evolution
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:hh:diva-35120 (URN)10.1038/s41598-017-11893-8 (DOI)000410739000057 ()28912582 (PubMedID)2-s2.0-85029527838 (Scopus ID)
Note

Funding: R.W.S., V.V., P.T., and S.Y.R. were supported in part by the N.I.H. grant AI0168581. D.A.K. and E.W. were supported in part by the N.I.H. grant AI102792. J.M. received support from the Stanford SPARK program and the Fogarty Global Health Equity Scholars Fellowship (NIAID R25 TW009338).

Available from: 2017-10-02 Created: 2017-10-02 Last updated: 2018-04-25Bibliographically approved
Carpatorea, I., Nowaczyk, S., Rögnvaldsson, T. & Lodin, J. (2017). Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers. In: 2017 Intelligent Systems Conference (IntelliSys): . Paper presented at Intelligent Systems Conference (IntelliSys 2017), London, United Kingdom, 7-8 September, 2017 (pp. 476-481).
Open this publication in new window or tab >>Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers
2017 (English)In: 2017 Intelligent Systems Conference (IntelliSys), 2017, p. 476-481Conference paper, Published paper (Refereed)
Abstract [en]

Improving the performance of systems is a goal pursued in all areas and vehicles are no exception. In places like Europe, where the majority of goods are transported over land, it is imperative for fleet operators to have the best efficiency, which results in efforts to improve all aspects of truck operations. We focus on drivers and their performance with respect to fuel consumption. Some of relevant factors are not accounted for inavailable naturalistic data, since it is not feasible to measure them. An alternative is to set up experiments to investigate driver performance but these are expensive and the results are not always conclusive. For example, drivers are usually aware of the experiment’s parameters and adapt their behavior.

This paper proposes a method that addresses some of the challenges related to categorizing driver performance with respect to fuel consumption in a naturalistic environment. We use expert knowledge to transform the data and explore the resulting structure in a new space. We also show that the regions found in APPES provide useful information related to fuel consumption. The connection between APPES patterns and fuel consumption can be used to, for example, cluster drivers in groups that correspond to high or low performance. © 2017 IEEE

Keywords
truck driver, driver performance, driver behavior, fuel economy, heavy-duty vehicle performance
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-33232 (URN)10.1109/IntelliSys.2017.8324336 (DOI)978-1-5090-6435-9 (ISBN)978-1-5090-6436-6 (ISBN)
Conference
Intelligent Systems Conference (IntelliSys 2017), London, United Kingdom, 7-8 September, 2017
Available from: 2017-02-08 Created: 2017-02-08 Last updated: 2018-04-03Bibliographically approved
Carpatorea, I., Slawomir, N., Rögnvaldsson, T., Elmer, M. & Lodin, J. (2016). Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data. In: Lisa O’Conner (Ed.), Proceedings: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Paper presented at IEEE 15th International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18-20 December, 2016 (pp. 1067-1072). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data
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2016 (English)In: Proceedings: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) / [ed] Lisa O’Conner, Los Alamitos, CA: IEEE Computer Society, 2016, p. 1067-1072Conference paper, Published paper (Refereed)
Abstract [en]

Fuel used by heavy duty trucks is a major cost for logistics companies, and therefore improvements in this area are highly desired. Many of the factors that influence fuel consumption, such as the road type, vehicle configuration or external environment, are difficult to influence. One of the most under-explored ways to lower the costs is training and incentivizing drivers. However, today it is difficult to measure driver performance in a comprehensive way outside of controlled, experimental setting.

This paper proposes a machine learning methodology for quantifying and qualifying driver performance, with respect to fuel consumption, that is suitable for naturalistic driving situations. The approach is a knowledge-based feature extraction technique, constructing a normalizing fuel consumption value denoted Fuel under Predefined Conditions (FPC), which captures the effect of factors that are relevant but are not measured directly.

The FPC, together with information available from truck sensors, is then compared against the actual fuel used on a given road segment, quantifying the effects associated with driver behavior or other variables of interest. We show that raw fuel consumption is a biased measure of driver performance, being heavily influenced by other factors such as high load or adversary weather conditions, and that using FPC leads to more accurate results. In this paper we also show evaluation the proposed method using large-scale, real-world, naturalistic database of heavy-duty vehicle operation.

Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2016
Keywords
data mining, expert features, heavy-duty vehicle, vehicle driver, truck driver, driver classification, feature extraction
National Category
Computer Sciences Vehicle Engineering Transport Systems and Logistics Infrastructure Engineering Applied Psychology
Identifiers
urn:nbn:se:hh:diva-33078 (URN)10.1109/ICMLA.2016.0194 (DOI)000399100100185 ()2-s2.0-85015439319 (Scopus ID)978-1-5090-6166-2 (ISBN)
Conference
IEEE 15th International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18-20 December, 2016
Available from: 2017-01-26 Created: 2017-01-16 Last updated: 2018-01-13Bibliographically approved
Fan, Y., Nowaczyk, S., Rögnvaldsson, T. & Antonelo, E. A. (2016). Predicting Air Compressor Failures with Echo State Networks. In: Ioana Eballard, Anibal Bregon (Ed.), PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016. Paper presented at Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016 (pp. 568-578). PHM Society
Open this publication in new window or tab >>Predicting Air Compressor Failures with Echo State Networks
2016 (English)In: PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 / [ed] Ioana Eballard, Anibal Bregon, PHM Society , 2016, p. 568-578Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment.

A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal's characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.

Place, publisher, year, edition, pages
PHM Society, 2016
Keywords
predictive maintenance, fault detection, Vehicle diagnostics, reservoir model, echo state network
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:hh:diva-31644 (URN)978-1-936263-21-9 (ISBN)
Conference
Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016
Projects
In4Uptime
Funder
VINNOVA
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2016-11-28Bibliographically approved
Fan, Y., Nowaczyk, S. & Rögnvaldsson, T. (2015). Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. Paper presented at INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015. Procedia Computer Science, 53, 447-456
Open this publication in new window or tab >>Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
2015 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, p. 447-456Article in journal (Refereed) Published
Abstract [en]

Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2015
Keywords
Vehicle diagnostics, predictive maintenance, fault detection, self-organizing systems
National Category
Signal Processing Information Systems
Identifiers
urn:nbn:se:hh:diva-29240 (URN)10.1016/j.procs.2015.07.322 (DOI)000360311000051 ()2-s2.0-84939156791 (Scopus ID)
Conference
INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015
Projects
In4Uptime
Funder
VINNOVA
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2018-01-11Bibliographically approved
Fan, Y., Nowaczyk, S. & Rögnvaldsson, T. (2015). Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. Paper presented at The 13th Scandinavian Conference on Artificial Intelligence (SCAI), Halmstad University, Halmstad, Sweden, 5-6 November, 2015. Frontiers in Artificial Intelligence and Applications, 278, 58-67
Open this publication in new window or tab >>Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
2015 (English)In: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, p. 58-67Article in journal (Refereed) Published
Abstract [en]

In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.

In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2015
Keywords
Vehicle diagnostics, Predictive maintenance, Fault detection, Receiver Operating Characteristic curve, Expert knowledge
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hh:diva-29809 (URN)10.3233/978-1-61499-589-0-58 (DOI)2-s2.0-84963636151 (Scopus ID)
Conference
The 13th Scandinavian Conference on Artificial Intelligence (SCAI), Halmstad University, Halmstad, Sweden, 5-6 November, 2015
Projects
In4Uptime
Funder
VINNOVAKnowledge Foundation
Note

ISBN: 978-1-61499-588-3 (print) | 978-1-61499-589-0 (online)

Editor: Sławomir Nowaczyk

Available from: 2015-11-24 Created: 2015-11-24 Last updated: 2018-01-10Bibliographically approved
Prytz, R., Nowaczyk, S., Rögnvaldsson, T. & Byttner, S. (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering applications of artificial intelligence, 41, 139-150
Open this publication in new window or tab >>Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
2015 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 41, p. 139-150Article in journal (Refereed) Published
Abstract [en]

Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.

Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2015
Keywords
Machine Learning, Diagnostics, Fault Detection, Automotive Industry, Air Compressor
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-27808 (URN)10.1016/j.engappai.2015.02.009 (DOI)000353739800012 ()2-s2.0-84926374379 (Scopus ID)
Projects
in4uptime
Funder
VINNOVAKnowledge Foundation
Note

The authors thank Vinnova (Swedish Governmental Agency for Innovation Systems), AB Volvo, Halmstad University, and the Swedish Knowledge Foundation for financial support for doing this research.

Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2018-03-22Bibliographically approved
Rögnvaldsson, T., You, L. & Garwicz, D. (2015). State of the art prediction of HIV-1 protease cleavage sites. Bioinformatics, 31(8), 1204-1210
Open this publication in new window or tab >>State of the art prediction of HIV-1 protease cleavage sites
2015 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, no 8, p. 1204-1210Article in journal (Refereed) Published
Abstract [en]

Motivation: Understanding the substrate specificity of HIV-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

Results: The linear support vector machine with orthogonal encod-ing is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor ser-vices. It is also found that schemes using physicochemical proper-ties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.

Availability: The data sets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. © 2014 The Author.

Place, publisher, year, edition, pages
Oxford: Oxford University Press, 2015
Keywords
Bioinformatics, HIV-1
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:hh:diva-27165 (URN)10.1093/bioinformatics/btu810 (DOI)000354453700007 ()25504647 (PubMedID)2-s2.0-84927720595 (Scopus ID)
Available from: 2014-12-04 Created: 2014-12-04 Last updated: 2018-03-22Bibliographically approved
Carpatorea, I., Nowaczyk, S., Rögnvaldsson, T. & Elmer, M. (2014). APPES Maps as Tools for Quantifying Performance of Truck Drivers. In: Robert Stahlbock & Gary M. Weiss (Ed.), Proceedings of the 2014 International Conference on Data Mining, DMIN'14: . Paper presented at The 10th International Conference on Data Mining, DMIN´14, July 21-24, Las Vegas, Nevada, USA (pp. 10-16). USA: CSREA Press
Open this publication in new window or tab >>APPES Maps as Tools for Quantifying Performance of Truck Drivers
2014 (English)In: Proceedings of the 2014 International Conference on Data Mining, DMIN'14 / [ed] Robert Stahlbock & Gary M. Weiss, USA: CSREA Press, 2014, p. 10-16Conference paper, Published paper (Refereed)
Abstract [en]

Understanding and quantifying drivers’ influence on fuel consumption is an important and challenging problem. A number of commonly used approaches are based on collection of Accelerator Pedal Position - Engine Speed (APPES) maps. Up until now, however, most publicly available results are based on limited amounts of data collected in experiments performed under well-controlled conditions. Before APPES maps can be considered a reliable solution, there is a need to evaluate the usefulness of those models on a larger and more representative data.

In this paper we present analysis of APPES maps that were collected, under actual operating conditions, on more than 1200 trips performed by a fleet of 5 Volvo trucks owned by a commercial transporter in Europe. We use Gaussian Mixture Models to identify areas of those maps that correspond to different types of driver behaviour, and investigate how the parameters of those models relate to variables of interest such as vehicle weight or fuel consumption.

Place, publisher, year, edition, pages
USA: CSREA Press, 2014
Keywords
data mining, truck drivers, fuel, fuel consumptions, histograms
National Category
Information Systems
Identifiers
urn:nbn:se:hh:diva-27411 (URN)9781601323132 (ISBN)
Conference
The 10th International Conference on Data Mining, DMIN´14, July 21-24, Las Vegas, Nevada, USA
Projects
Learning Fleet
Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5163-2997

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