Change search
Refine search result
1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, Sweden.
    Zaccaria, Valentina
    Mälardalen University, Sweden.
    Rahman, Moksadur
    Mälardalen University, Sweden.
    Kyprianidis, Konstantinos G.
    Mälardalen University, Sweden.
    Oostveen, Mark
    MTT Micro Turbine Technology, The Netherlands.
    Olsson, Tomas
    RISE - Research Institutes of Sweden, ICT, SICS.
    Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics2018Conference paper (Refereed)
    Abstract [en]

    Real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. Prediction of faults can change the maintenance model of a system from a fixed maintenance interval to a condition based maintenance interval, further decreasing the total cost of ownership of a system. Technologies developed for engine health monitoring and advanced diagnostic capabilities are generally developed for larger gas turbines, and generally focus on a single system; no solutions are publicly available for engine fleets. This paper presents a concept for fleet monitoring finely tuned to the specific needs of micro gas turbines. The proposed framework includes a physics-based model and a data-driven model with machine learning capabilities for predicting system behaviour, combined with a diagnostic tool for anomaly detection and classification. The integrated system will develop advanced diagnostics and condition monitoring for gas turbines with a power output under 100 kW.

  • 2. Boström, Gustav
    et al.
    Giambiagi, Pablo
    RISE, Swedish ICT, SICS.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Quality of Service Evaluation in Virtual Organizations Using SLAs2006In: 1st International Workshop on Interoperability Solutions to Trust, Security, Policies and QoS for Enhanced Enterprise Systems (IS-TSPQ 2006), 2006, 1, , p. 12Conference paper (Refereed)
    Abstract [en]

    Cooperating in Virtual organizations requires trust between the constituting organizations. SLA contracts are put in place in order to specify the quality of service of services offered. For these contracts to be effective they also need to be monitored effectively. In a Service Oriented Architecture this often means monitoring Web service invocations and evaluating if the service fulfills the obligations in its SLA. In this paper we present an implementation of a rule engine based SLA Evaluator specifically designed for the needs of a virtual organization. The evaluator fits in the context of a virtual organization through the use of open XML-based standards and a loosely coupled, event-driven architecture.

  • 3.
    Cöster, Rickard
    et al.
    RISE, Swedish ICT, SICS.
    Gustavsson, Andreas
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Rudström, Åsa
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Enhancing Web-Based Configuration with Recommendations and Cluster-Based Help2002Conference paper (Refereed)
  • 4.
    Källström, Elisabeth
    et al.
    Lulea University of Technology, Sweden.
    Olsson, Tomas
    RISE - Research Institutes of Sweden, ICT, SICS.
    Lindström, John
    Lulea University of Technology, Sweden.
    Håkansson, Lars
    Blekinge Institute of Technology, Sweden.
    Larsson, Jonas
    Volvo Construction Equipment, Sweden.
    On-board clutch slippage detection and diagnosis in heavy duty machine2018In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 9, no 1, article id 007Article in journal (Refereed)
    Abstract [en]

    In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized. This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

  • 5.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Heavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability.

  • 6.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Assessing Security Risk to a Network Using a Statistical Model of Attacker Community Competence2009Conference paper (Refereed)
    Abstract [en]

    We propose a novel approach for statistical risk modeling of network attacks that lets an operator perform risk analysis using a data model and an impact model on top of an attack graph in combination with a statistical model of the attacker community exploitation skill. The data model describes how data flows between nodes in the network -- how it is copied and processed by softwares and hosts -- while the impact model models how exploitation of vulnerabilities affects the data flows with respect to the confidentiality, integrity and availability of the data. In addition, by assigning a loss value to a compromised data set, we can estimate the cost of a successful attack. The statistical model lets us incorporate real-time monitor data from a honeypot in the risk calculation. The exploitation skill distribution is inferred by first classifying each vulnerability into a required exploitation skill-level category, then mapping each skill-level into a distribution over the required exploitation skill, and last applying Bayesian inference over the attack data. The final security risk is thereafter computed by marginalizing over the exploitation skill.

  • 7.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Decentralized Social Filtering based on Trust1998Conference paper (Refereed)
    Abstract [en]

    This paper describes a decentralised approach to social filtering based on trust between agents in a multiagent system. The social filtering in the proposed approach is built on the interactions between collaborative software agents performing content-based filtering. This means that it uses a mixture of content-based and social filtering and thereby, it takes advantage of both methods.

  • 8.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Evaluating Machine Learning for Predicting Next-Day Hot Water Production of a Heat Pump2013Conference paper (Refereed)
    Abstract [en]

    This paper describes an evaluation of five machine learning algorithms for predicting the domestic space and hot- water heating production for the next day. The evaluated algorithms were the k-nearest neighbour algorithm, linear regression, regression tree, decision table and support vector machine regression. The hot water production was measured in the ME3Gas project, where data was collected from two Swedish households that use the same type of geothermal heat pumps for space heating and hot-water production. The evaluation consisted of four experiments where we compared the regression performance by varying the number of previous days and the number of time periods for each day as input features. In the experiments, the k-nearest neighbour algorithm, linear regression and support vector machine regression had the best performance.

  • 9.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Impact estimation using data flows over attack graphs2009Conference paper (Refereed)
    Abstract [en]

    We propose a novel approach to estimating the impact of an attack using a data model and an impact model on top of an attack graph. The data model describes how data flows between nodes in the network -- how it is copied and processed by softwares and hosts -- while the impact model models how exploitation of vulnerabilities affects the data flows with respect to the confidentiality, integrity and availability of the data. In addition, by assigning a loss value to a compromised data set, we can estimate the cost of a successful attack. We show that our algorithm not only subsumes the simple impact estimation used in the literature but also improves it by explicitly modeling loss value dependencies between network nodes. With our model, the operator will be able to use less time when comparing different security patches to a network.

  • 10.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Information routing2001Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    The present invention relates to a problem with how to automatically or semi-automatically find and retrieve information for a user according to his preferences, is solved by sending an information object from a first interest agent to a second interest agent. Said information object comprising electronic information, a first array of references to other interest agents which recommends the electronic information and a second array of references to other interest agents which are indifferent to the content of the electronic information. Said second interest agent comprises a third array, where each position in said array comprises references to other interest agents and a confidence value. If the first agent which sent the information object is found in said third array and has a high confidence value the electronic information is presented before a user.

  • 11.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bjurling, Björn
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Chong, May Yee
    Ohlman, Börje
    Goal Refinement for Automated Service Discovery2011Conference paper (Refereed)
    Abstract [en]

    An important prerequisite for service composition is a versatile and efficient service discovery mechanism. The trends in service computing currently point toward a veritable explosion in the number of services that are or will become available as components in compositions of new services—Cloud Computing and the ’Internet of Things’ are just two examples of such new trends. We hypothesize that it will become critical to be able to filter the discovered services with respect to pre- and postconditions, as well as other semantic aspects such as relevance. We have studied the use of goals as a means for describing semantic aspects of services (e.g., their effects). By using goals, services can be described on any arbitrary and useful level of abstraction. By a goal refinement algorithm, goals can be used not only for describing services, but also for improving the performance of service discovery. In this paper, we describe the goal refinement algorithm and our approach to incorporating it into our service discovery machinery.

  • 12.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Funk, Peter
    Case-based reasoning combined with statistics for diagnostics and prognosis2012In: Journal of Physics: Conference Series, 2012, 10, Vol. 364, p. 0-0Conference paper (Refereed)
    Abstract [en]

    Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

  • 13.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Xiong, Ning
    Case-Based Reasoning for Explaining Probabilistic Machine Learning2014In: International Journal of Computer Science and Information Technology, Vol. 6, p. 87-101Article in journal (Refereed)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

  • 14.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Mälardalen University, Sweden.
    Xiong, Ning
    Mälardalen University, Sweden.
    Explaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning2014Conference paper (Refereed)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

  • 15.
    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).

  • 16.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Källström, Elisabeth
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Funk, Peter
    Lindström, John
    Håkansson, Lars
    Lundin, Joakim
    Svensson, Magnus
    Larsson, Jonas
    Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision making. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely integrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions.

  • 17.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Rasmusson, Andreas
    RISE, Swedish ICT, SICS.
    Janson, Sverker
    RISE, Swedish ICT, SICS, Computer Systems Laboratory.
    Personalized Decentralized Communication2000Conference paper (Refereed)
    Abstract [en]

    Search engines, portals and topic-centered web sites are all attempts to create more or less personalized web-services. However, no single service can in general fulfill all needs of a particular user, so users have to search and maintain personal profiles at several locations. We propose an architecture where each person has his own information management environment where all personalization is made locally. Information is exchanged with other’s if it’s of mutual interest that the information is published or received. We assume that users are self-interested, but that there is some overlap in their interests. Our recent work has focused on decentralized dissemination of information, specifically what we call decentralized recommender systems. We are investigating the behavior of such systems and have also done some preliminary work on the users’ information environment.

  • 18.
    Rasmusson, Andreas
    et al.
    RISE, Swedish ICT, SICS.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Hansen, Preben
    RISE, Swedish ICT, SICS.
    A Virtual Community Library: SICS Digital Library Infrastructure Project.1998Conference paper (Refereed)
    Abstract [en]

    In this project1, we aim to create an agent-based digital library architecture for a Virtual Community Library (VCL) where each user has a personal library and, at the same time, is part of a larger community. The community is dynamically composed of the users’ personal libraries and, through intermediators, other digital libraries. We want to stress the fact that the users participate in a large dynamic decentralised community where they continually interact with each other. Being a part of a community means that each user can benefit from the work put into the other libraries. For example, by obtaining documents through search queries or recommendations using social filtering, but also by getting help to organise the personal library. In the VCL, we try to combine the best aspects of the WWW, the library and the personal library. For example, ease to publish documents, personal information space, decentralised control of the document collection and ability to search for documents. We have currently implemented two prototypes of the system, one for the personal library and one for visualising the information spread between the users.

  • 19. Xiong, Ning
    et al.
    Funk, Peter
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information2012Conference paper (Refereed)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

  • 20. Xiong, Ning
    et al.
    Funk, Peter
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Representation and similarity evaluation of symbolic time series in uncertain environments2014Conference paper (Refereed)
  • 21. Xiong, Ning
    et al.
    Olsson, Tomas
    RISE, Swedish ICT, SICS.
    Funk, Peter
    Case-based reasoning supports fault diagnosis using sensor information2013In: International Journal of COMADEM, 2013, Vol. 16Article in journal (Refereed)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.8