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  • 1.
    Aronsson, Martin
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Doganay, Kivanc
    RISE, Swedish ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kjellqvist, Tommy
    Östlund, Stefan
    An Integrated Adaptive Maintenance Concept2010Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a novel maintenance concept based on condition monitoring and dynamic maintenance packaging, by showing how to connect the information flow from low-level sensors to high-level operations and planning under uncertainty. Today, condition-based maintenance systems are focused on data collection and custom-made rule based systems for data analysis. In many cases, the focus is on measuring "everything" without considering how to use the measurements. In addition, the measurements are often noisy and the future is unpredictable which adds a lot of uncertainty. As a consequence, maintenance is often planned in advance and not replanned when new condition data is available. This often reduces the benefits of condition monitoring. The concept is based on the combination of robust, dynamically adapted maintenance optimization and statistical data analysis where the uncertainty is considered. This approach ties together low-level data acquisition and high-level planning and optimization. The concept has been illustrated in a context of rail vehicle maintenance, where measurements of brake pad and pantograph contact strip wear is used to predict the near future condition, and plan the maintenance activities.

  • 2.
    Bohlin, Markus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    The opportunistic replacement and inspection problem for components with a stochastic life time2011Report (Other academic)
    Abstract [en]

    The problem of finding efficient maintenance and inspection schemes in the case of components with a stochastic life time is studied and a mixed integer programming solution is proposed. The problem is compared with the two simpler problems of which the studied problem is a generalisation: The opportunistic replacement problem, assuming components with a deterministic life time and The opportunistic replacement problem for components with a stochastic life time, for maintenance schemes without inspections.

  • 3.
    Bohlin, Markus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Forsgren, Malin
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Aronsson, Martin
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Reducing vehicle maintenance using condition monitoring and dynamic planning2008In: Proceedings of the 4th IET International Conference on Railway Condition Monitoring (RCM'08), 2008, 1Conference paper (Refereed)
  • 4.
    Bohlin, Markus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sellin, Ola
    Lindström, Björn
    Larsen, Stefan
    Statistical Anomaly Detection for Train Fleets2012Conference paper (Refereed)
  • 5.
    Bohlin, Markus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Wärja, Mathias
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Slottner, Pontus
    Doganay, Kivanc
    RISE, Swedish ICT, SICS.
    Optimization of Condition-Based Maintenance for Industrial Gas Turbines: Requirements and Results2009Conference paper (Refereed)
  • 6.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, Sweden.
    Karlsson, Alexander
    University of Skövde, Sweden.
    Pashami, Sepideh
    Halmstad University, Sweden.
    Nowaczyk, Sławomir
    Halmstad University, Sweden.
    Holst, Anders
    RISE - Research Institutes of Sweden, ICT, SICS.
    Mode tracking using multiple data streams2018In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed)
    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.

  • 7.
    Ekman, Jan
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Incremental stream clustering and anomaly detection2008Report (Other academic)
    Abstract [en]

    This report concerns the "ISC-tool", a tool for classification of patterns and detection of anomalous patterns, where a pattern is a set of values. The tool has a graphical user interface "the anomalo-meter" that shows the degree of anomaly of a pattern and how it is classified. The report describes the user interaction with the tool and the underlying statistical methods used, which basically are Bayesian inference for finding expected or "predictive" distributions for clusters of patterns and using these distributions for classifying and assessing a degree of anomaly to a new pattern. The report also briefly discusses what in general are appropriate methods for clustering and anomaly detection. The project has been supported by SSF via the Butler2 programme.

  • 8.
    Ekman, Jan
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Aronsson, Martin
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Forsgren, Malin
    RISE - Research Institutes of Sweden, ICT, SICS.
    Larsen, Stefan
    TIME - en gemensam informationsutbytesplattform för järnvägstransportbranschen2006Report (Other academic)
    Abstract [sv]

    TIME står för Train Information Management Environment. TIME är ett tänkt övergripande informationssystem för Järnväg. Viktiga aspekter hos TIME är utformningen av en plattform för kommunikation mellan aktörerna i järnvägstransportbranschen och information mellan fordon och system med en fast plats. TIME gäller alla delar i ett informationssystem, hur data produceras och processas, infrastruktur för information och principer för datalagring och informationsutbyte samt funktioner och tjänster baserade på denna information. TIME avser t.ex. att medverka till att samverkan mellan järnvägstransportbranschens aktörer fungerar bra, dessa aktörers egen verksamhet blir effektiv och att kunder till järnvägen och andra som beror av järnvägen erhåller rätt information.

  • 9.
    Ekman, Jan
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS, Software and Systems Engineering Laboratory.
    Condition based maintenance of trains doors2011Report (Other academic)
    Abstract [en]

    As part of the project DUST financed by Vinnova, we have investigated whether event data generated on trains can be used for finding evidence of wear on train doors. We have compared the event data and maintenance reports relating to doors of Regina trains. Although some interesting relations were found, the overall result is that the information in event data about wear of doors is very limited.

  • 10.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A brief introduction to hierarchical graph mixtures2004Conference paper (Refereed)
  • 11.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Dependency derivation in industrial process data2001In: Proceedings of the 2001 International Conference on Data Mining, Los Alamitos, California: IEEE Computer Society Press , 2001, 1, , p. 4p. 599-602Conference paper (Refereed)
  • 12.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Finding dependencies and delays in measured data2008In: Use of Modeling and Simulation in Pulp and Paper Industry, COST , 2008, 1Chapter in book (Refereed)
  • 13.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Finding dependencies in industrial process data2002In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 50Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Dependency derivation and the creation of dependency graphs are critical tasks for increasing the understanding of an industrial process. However, the most commonly used correlation measures are often not appropriate to find correlations between time series. We present a measure that solves some of these problems.

  • 14.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Incremental diagnosis with limited historical data2006Conference paper (Refereed)
  • 15.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Mixture models, graphical models, and hierarchical combinations2008In: Use of Modeling and Simulation in Pulp and Paper Industry, COST , 2008, 1Chapter in book (Refereed)
  • 16.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sales prediction with marketing data integration for supply chains2004In: E-Manufacturing: Business Paradigms and Supporting Technologies, Germany: Springer Verlag , 2004, 1Chapter in book (Refereed)
  • 17.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sales prediction with marketing data integration for supply chains2002In: Proceedings of the 18th International Conference on CAD/CAM, robotics and Factories of the Future, Vol. 1, 2002, 1Conference paper (Refereed)
  • 18.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    The gmdl Modeling and Analysis System2004Report (Other academic)
    Abstract [en]

    This report describes the gmdl modeling and analysis environment. gmdl was designed to provide powerful data analysis, modeling, and visualization with simple, clear semantics and easy to use, well defined syntactic conventions. It provides an extensive set of necessary for general data preparation, analysis, and modeling tasks.

  • 19.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Emulating first principles process simulators with learning systems2005In: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, Germany: Springer , 2005, 1, p. 377-382Chapter in book (Refereed)
  • 20.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Aronsson, Martin
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    An English butler for complex industrial systems2003In: Proceedings of the 16th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2003), Växjö, Sweden: Växjö University Press , 2003, 1, , p. 9p. 405-413Conference paper (Refereed)
  • 21.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Gudmundsson, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Approximating process simulators with learning systems2005Report (Other academic)
    Abstract [en]

    We explore the possibility of replacing a first principles process simulator with a learning system. This is motivated in the presented test case setting by a need to speed up a simulator that is to be used in conjunction with an optimisation algorithm to find near optimal process parameters. Here we will discuss the potential problems and difficulties in this application, how to solve them and present the results from a paper mill test case.

  • 22.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Fault-tolerant incremental diagnosis with limited historical data2006Report (Other academic)
    Abstract [en]

    In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. All relevant information may not be available initially and must be acquired manually or at a cost. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. Here, we will describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system.

  • 23.
    Gillblad, Daniel
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Fault-tolerant incremental diagnosis with limited historical data2008Conference paper (Refereed)
    Abstract [en]

    We describe a novel incremental diagnostic system based on a statistical model that is trained from empirical data. The system guides the user by calculating what additional information would be most helpful for the diagnosis. We show that our diagnostic system can produce satisfactory classification rates, using only small amounts of available background information, such that the need of collecting vast quantities of initial training data is reduced. Further, we show that incorporation of inconsistency-checking mechanisms in our diagnostic system reduces the number of incorrect diagnoses caused by erroneous input.

  • 24.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Analys och prediktion av slitage på hjulprofiler och bromsbelägg på tåg2011Report (Other academic)
    Abstract [sv]

    Vi har som en del av det Vinnova-finansierade projektet DUST undersökt hur Bayesiansk statistisk modellering och avvikelsedetektion kan användas för att analysera slitage på hjulprofiler och bromsbelägg på tåg. Vi visar hur man med denna analys kan filtrera data, upptäcka onormalt slitage, och förutsäga när det är dags för underhåll. Resultaten visar att de föreslagna metoderna fungerar mycket bra för analys av den typ av tidsseriedata med trender som det handlar om här, och att det går att få ut ganska mycket trots att data är relativt få och brusiga.

  • 25.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Data analysis of sensor network data2009Report (Other academic)
    Abstract [en]

    In this report we illustrate how a number of data analysis methods can be used to monitor data from a sensor network. Analysis is made in the forms of visualization, dependency analysis, and anomaly detection. The sensor network is monitored with respect to both the measurements made by the sensors and the operation of the network itself.

  • 26.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Optimal test planning for High Cycle Fatigue limit testing2011In: Annals of Operations Research, p. 1-10Article in journal (Refereed)
  • 27.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    The DALLAS project. Report from the NUTEK-supported project AIS-8: Application of Data Analysis with Learning Systems, 1999-20012002Report (Other academic)
    Abstract [en]

    The DALLAS ("application of Data AnaLysis with LeArning Systems") project has been designed to bring together groups using learning systems (e.g. artificial neural networks, non-linear multi-variate statistics, inductive logic etc) at five universities and research institutes, with seven companies with data analysis tasks from various industrial sectors in Sweden. An objective of the project has been to spread knowledge and the use of learning systems methods for data analysis in industry. Further objectives have been to test the methods on real world problems in order to find strengths and weaknesses in the methods and to inspire research in the area.

  • 28.
    Holst, Anders
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bouguelia, M. -R
    CAISR, Sweden.
    Görnerup, Olof
    RISE - Research Institutes of Sweden, ICT, SICS.
    Pashami, S.
    CAISR, Sweden.
    Al-Shishtawy, Ahmad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Falkman, G.
    University of Skövde, Sweden.
    Karlsson, A.
    University of Skövde, Sweden.
    Said, A.
    University of Skövde, Sweden.
    Bae, J.
    University of Skövde, Sweden.
    Girdzijauskas, Sarunas
    RISE - Research Institutes of Sweden, ICT, SICS.
    Nowaczyk, S.
    CAISR, Sweden.
    Soliman, Amina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Eliciting structure in data2019In: CEUR Workshop Proceedings, 2019Conference 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.

  • 29.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Incremental stream clustering for anomaly detection and classification.2011Conference paper (Refereed)
  • 30.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Deviation detection of industrial processes2004In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 56, p. 13-14Article in journal (Other (popular science, discussion, etc.))
  • 31.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Larsen, Stefan
    Abnormality detection in event data and condition counters on Regina trains.2006Conference paper (Refereed)
  • 32.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Larsen, Stefan
    Abnormality detection in event data and condition counters on Regina trains2006In: The IETInternational Conference on Railway Condition Monitoring 2006, 2006, 1, , p. 4p. 53-56Conference paper (Refereed)
    Abstract [en]

    The Regina trains, manufactured by Bombardier Transportation, contain software and hardware to generate as well condition data as event data that can be used to monitor the condition of the trains. In this paper we present the necessary equations for abnormality detection of both event data and condition counters in a general setting. The use of the equations is illustrated on authentic data from Regina trains.

  • 33.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ekman, Jan
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Larsen, Stefan
    Avvikelsedetektion i signaler från Regina2006Report (Other academic)
    Abstract [sv]

    I Reginatågen genereras signaler om såväl s.k. "events" (meddelanden om både rutinhändelser och mer eller mindre allvarliga fel i olika enheter) som "condition" (driftsräknare för olika enheter). Det är önskvärt att övervaka båda dessa typer av signaler för att upptäcka avvikelser som kraftigt förändrad frekvens eller driftsintensitet. Sådana avvikelser skulle kunna signalera olika servicebehov, och det skulle alltså vara till användning om servicepersonal fick reda på dem i god tid innan de lett till allvarligare fel. Vi kommer här att gå igenom en grundläggande och generellt användbar statistisk modell för detta scenario. Metoden utvärderas på autentiska data från Reginatågen.

  • 34.
    Holst, Anders
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Karlsson, Alexander
    University of Skövde, Sweden.
    Bae, Juhee
    University of Skövde, Sweden.
    Bouguelia, Mohamed-Rafik
    Halmstad University, Sweden .
    Interactive clustering for exploring multiple data streams at different time scales and granularity2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference 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).

  • 35.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Butler: Fallanalys 1 - Outokumpu2004Report (Other academic)
    Abstract [sv]

    Denna rapport beskriver resultatet av en dataanalys gjord på produktionsdata från Outokumpu:s valsverk i Avesta. Syftet har varit att fastställa samband mellan övriga produktionsparametrar och uppkomsten av sk. "slivers" en typ av ytlig sprick- eller veck-bildning i det färdiga stålet. Ett annat syfte har varit att studera metodologiska frågor i ett arbete av detta slag.

  • 36.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kreuger, Per
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Per
    Tenth Scandinavian Conference on Artificial Intelligence, SCAI 20082008 (ed. 1)Book (Refereed)
  • 37.
    Holst, Anders
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Pashami, Sepideh
    Halmstad University, Sweden .
    Bae, Juhee
    University of Skövde, Sweden .
    Incremental causal discovery and visualization2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference 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).

  • 38.
    Holst, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Dispersing the conceptual confusion2001Conference paper (Refereed)
    Abstract [en]

    In few subjects it is as easy to talk past each other as when discussing consciousness. Not only is the subject elusive and everyone has their own opinion of what it is all about; different people also make quite different use of words and language when discussing consciousness. This contribution tries to exemplify some common misunderstanding between people with different starting points and different use of language. The suggestion is that 'the problem of consciousness' is after all quite similar to all of us, although this is muddled by the way we talk about it, and the way we have locked ourselves into our different slogans and world views.

  • 39.
    Kanerva, Pentti
    et al.
    RISE, Swedish ICT, SICS.
    Kristoferson, Jan
    RISE, Swedish ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Random indexing of text samples for latent semantic analysis2000In: Proceedings of the 22nd Annual Conference of the Cognitive Science Society, 2000, 1Conference paper (Refereed)
  • 40.
    Kanerva, Pentti
    et al.
    RISE, Swedish ICT, SICS.
    Sjödin, Gunnar
    RISE, Swedish ICT, SICS.
    Kristoferson, Jan
    RISE, Swedish ICT, SICS.
    Karlsson, R.
    Levin, Björn
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Karlgren, Jussi
    RISE, Swedish ICT, SICS.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Computing with large random patterns2001In: Foundations of Real-World Intelligence, Stanford, California: CSLI Publications , 2001, 1, p. 251-311Chapter in book (Refereed)
    Abstract [en]

    We describe a style of computing that differs from traditional numeric and symbolic computing and is suited for modeling neural networks. We focus on one aspect of ``neurocomputing,'' namely, computing with large random patterns, or high-dimensional random vectors, and ask what kind of computing they perform and whether they can help us understand how the brain processes information and how the mind works. Rapidly developing hardware technology will soon be able to produce the massive circuits that this style of computing requires. This chapter develops a theory on which the computing could be based.

  • 41.
    Karlgren, Jussi
    et al.
    RISE, Swedish ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Filaments of Meaning in Word Space2008Conference paper (Refereed)
    Abstract [en]

    Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dimensionality of typical vector space models lead to unintuitive effects on modeling likeness of meaning and that the local structure of word spaces is where interesting semantic relations reside. We show that the local structure of word spaces has substantially different dimensionality and character than the global space and that this structure shows potential to be exploited for further semantic analysis using methods for local analysis of vector space structure rather than globally scoped methods typically in use today such as singular value decomposition or principal component analysis.

  • 42.
    Levin, Björn
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Steinert, Rebecca
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Aronsson, Martin
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Dynamic maintenance2008In: Proceedings of the 21st International Congress and Exhibition On Condition Monitoring and Diagnostic Engineering Management (COMADEM'08), 2008, 1Conference paper (Refereed)
  • 43.
    Levin, Björn
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lansner, Anders
    RISE - Research Institutes of Sweden, ICT, SICS.
    Haraszti, Zolt
    Simulation support and ATM performance prediction1998In: ICANN 98 Proceedings: International Conference on Artificial Neural Networks, 2-4 Sep 1998, Skövde, Sweden, 1998, 1Conference paper (Refereed)
  • 44. Nelson, David W.
    et al.
    Thornquist, Björn
    MacCallum, Robert M.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Nyström, Harriet
    Rudehill, Anders
    Wanecek, Michael
    Bellander, Bo-Michael
    Analysis of cerebral microdialysis in patients with traumatic brain injury; relations to intracranial pressure, cerebral perfusion pressure and catheter placement.2011In: BMC Medicine, Vol. 9Article in journal (Refereed)
  • 45.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications2015In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), 2015, 7, p. 434-439Conference 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).

  • 46.
    Sahlgren, Magnus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kanerva, Pentti
    Permutations as a means to encode order in word space2008Conference paper (Refereed)
    Abstract [en]

    We show that sequence information can be encoded into high-dimensional fixed-width vectors using permutations of coordinates. Computational models of language often represent words with high-dimensional semantic vectors compiled from word-use statistics. A word's semantic vector usually encodes the contexts in which the word appears in a large body of text but ignores word order. However, word order often signals a word's grammatical role in a sentence and thus tells of the word's meaning. Jones and Mewhort (2007) show that word order can be included in the semantic vectors using holographic reduced representation and convolution. We show here that the order information can be captured also by permuting of vector coordinates, thus providing a general and computationally light alternative to convolution.

1 - 46 of 46
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