hh.sePublikationer
Ändra sökning
Avgränsa sökresultatet
12 1 - 50 av 57
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Andreasson, Henrik
    et al.
    Örebro University, Örebro, Sweden.
    Bouguerra, Abdelbaki
    Örebro University, Örebro, Sweden.
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Gold-fish SLAM: An application of SLAM to localize AGVs2014Ingår i: Field and Service Robotics: Results of the 8th International Conference / [ed] Kazuya Yoshida & Satoshi Tadokoro, Heidelberg: Springer, 2014, s. 585-598Konferensbidrag (Refereegranskat)
    Abstract [en]

    The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs. © Springer-Verlag Berlin Heidelberg 2014.

  • 2.
    Antonelo, Eric A.
    et al.
    Electronics and Information Systems (ELIS) department, Ghent university, Belgium.
    Baerveldt, Albert-Jan
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Figueiredo, Mauricio
    State University of Maringá, Brazil.
    Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors2006Ingår i: International Joint Conference on Neural Networks, 2006. IJCNN '06, Piscataway, N.J.: IEEE Press, 2006, s. 498-505Konferensbidrag (Refereegranskat)
    Abstract [en]

    Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.

  • 3.
    Blomqvist, Daniel
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Holmberg, Ulf
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Different Strategies for Transient Control of the Air-Fuel Ratio in a SI Engine2000Ingår i: SAE transactions : journal of fuels and lubricants, Warrendale, Pa.: Society of automotive engineers (SAE) , 2000, Vol. 109Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper compares several strategies for air-fuel ratio tran-sient control. The strategies are: A factory-standard look-up table based system (a SAAB Trionic 5), a feedback PI controller with and without feed-forward throttle correction, a linear feed-forward control algorithm, and two nonlinear feed- forward algorithms based on artificial neural networks. The control strategies have been implemented and evaluated in a SAAB 9000 car during a transient driving test, consisting of an acceleration in the second gear from an engine speed of 1500 rpm to 3000 rpm. The best strategies are found to be the neural network based ones, followed by the table based factory system. The two feedback PI controllers offer the poorest performance.

  • 4.
    Bouguerra, Abdelbaki
    et al.
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden.
    Andreasson, Henrik
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden.
    Lilienthal, Achim J.
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden.
    Åstrand, Björn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    An autonomous robotic system for load transportation2009Ingår i: IEEE Conference on Emerging Technologies & Factory Automation, 2009. ETFA 2009, Piscataway, N.J.: IEEE Press, 2009, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an overview of an autonomous robotic system for material handling. The system is being developed by extending the functionalities of traditional AGVs to be able to operate reliably and safely in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires runtime object detection and tracking. Another requirement to be fulfilled by the system is the ability to generate trajectories dynamically, which is uncommon in industrial AGV systems. ©2009 IEEE.

  • 5.
    Bouguerra, Abdelbaki
    et al.
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University.
    Andreasson, Henrik
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University.
    Lilienthal, Achim J
    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University.
    Åstrand, Björn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    MALTA: A System of Multiple Autonomous Trucks for Load Transportation2009Ingår i: Proceedings of the 4th European Conference on Mobile Robots: ECMR’09, September 23 – 25, 2009 Mlini/Dubrovnik, Croatia / [ed] Ivan Petrovi´c Achim J. Lilienthal, Zagreb: KoREMA , 2009, s. 91-96Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems.

  • 6.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Gothenburg, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    A field test with self-organized modeling for knowledge discovery in a fleet of city buses2013Ingår i: 2013 IEEE International Conference on Mechatronics and Automation (ICMA 2013) / [ed] Shuxiang Guo, Piscataway, NJ: IEEE Press, 2013, s. 896-901, artikel-id 6618034Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fleets of commercial vehicles represent an excellent real life setting for ubiquitous knowledge discovery. There are many electronic control units onboard a modern bus or truck, with hundreds of signals being transmitted between them on the controller area network. The growing complexity of the vehicles has lead to a significant desire to have systems for fault detection, remote diagnostics and maintenance prediction. This paper aims to show that it is possible to discover useful diagnostic knowledge by a self-organized algorithm in the scenario of a fleet of city buses. The approach is demonstrated as a process consisting of two parts; Unsupervised modeling (where interesting features are discovered) and Guided search (where the previously found features are coupled to additional information sources). The modeling part searches for simple linear models in a group of vehicles, where interesting features are selected based on both non-randomness in relations and variability in the group. It is shown in an eight months long data collection study that this approach was able to discover features related to broken wheelspeed sensors. Strikingly, deviations in these features (for the vehicles with broken sensors) can be observed up to several months before a breakdown occur. This potentially allows for sufficient time to schedule the vehicle for maintenance and prepare the workshop with relevant components. © 2013 IEEE.

  • 7.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Consensus self-organized models for fault detection (COSMO)2011Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 24, nr 5, s. 833-839Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field-bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles’ data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.

  • 8.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Rögnvaldsson, Thorsteinn
    AASS, Örebro University, 701 82 Örebro, Sweden.
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Finding the odd-one-out in fleets of mechatronic systems using embedded intelligent agents2010Ingår i: Embedded reasoning: intelligence in embedded systems : papers from the AAAI Spring Symposium, Menlo Park, California: AAAI Press, 2010, s. 17-19Konferensbidrag (Refereegranskat)
    Abstract [en]

    With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

  • 9.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Technology, Göteborg, Sweden.
    Modeling for Vehicle Fleet Remote Diagnostics2007Ingår i: Proceedings of SAE 2007 Commercial Vehicle Engineering Congress, Warrendale, PA: SAE Inc. , 2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    Quality and up-time management of vehicles is today receiving much attention from vehicle manufacturers. One of the reasons is that there is a desire to avoiding on-road failures to addressing potential issues during routine maintenance intervals or at times more convenient to the operator. Forthcoming telematic platforms and advanced diagnostic algorithms can enable the possibility to proactively handle problems and minimize stops. The platforms bring the possibility of increasing knowledge of fault characteristics and making diagnostic decisions by using a population of vehicles. However, this requires real-time diagnostic algorithms that process data both onboard and offboard at a central server. The paper presents a self organizing approach for failure and deviation detection on a fleet of vehicles. The approach builds on using parametric models for encoding the characteristical relations between different sensor readings for a vehicle sub-system or component. The models are low-dimensional representations of the operating characteristics of a sub-system or component and are possible to transfer over a limited wireless communication channel. The approach is demonstrated on simulated data of an electronically controlled suspension system for detecting a slow valve and a leaking bellow.

  • 10.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Technology, 405 08 Göteborg, Sweden.
    Self-organized Modeling for Vehicle Fleet Based Fault Detection2008Ingår i: Proceedings of the SAE World Congress & Exhibition, Warrendale, PA: SAE Inc. , 2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    Operators of fleets of vehicles desire the best possible availability and usage of their vehicles. This means the preference is that maintenance of a vehicle is scheduled with as long intervals as possible. However, it is then important to be able to detect if a component in a specific vehicle is not functioning properly earlier than expected (due to e.g. manufacturing variations). This paper proposes a telematic based fault detection scheme for enabling fault detection for diagnostics by using a population of vehicles. The basic idea is that it is possible to create low-dimensional representations of a sub-system or component in a vehicle, where the representation (or model parameters) of a vehicle can be monitored for changes compared to the model parameters observed in a fleet of vehicles. If a model in a vehicle is found to deviate compared to a group of models from a fleet of vehicles, then the vehicle is judged to need diagnostics for that component (assuming the deviation in the model cannot be attributed to e.g. a different driver behavior). The representation should be low-dimensional so it is possible to have it transferred over a limited wireless communication channel to a communications center where the comparison is made. The algorithm is shown to be able to detect leakage on simulated data from a cooling system, work is currently in progress for detecting other types of faults.

  • 11.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Bitar, George
    Volvo Technology of America, 7825 National Service Rd., Greensboro, NC 27409, United States.
    Chominsky, Wesley
    Volvo Trucks North America, 7900 National Service Rd., Greensboro, NC 27409, United States.
    Networked vehicles for automated fault detection2009Ingår i: 2009 IEEE International Symposium on Circuits and Systems: circuits and systems for human centric smart living technologies, conference program, Taipei International Convention Center, Taipei, Taiwan, May 24-May 27, 2009 / [ed] Guo li Chenggong da xue, Piscataway, N.J.: IEEE Press, 2009, s. 1213-1216Konferensbidrag (Refereegranskat)
    Abstract [en]

    Creating fault detection software for complex mechatronic systems (e.g. modern vehicles) is costly both in terms of engineer time and hardware resources. With the availability of wireless communication in vehicles, information can be transmitted from vehicles to allow historical or fleet comparisons. New networked applications can be created that, e.g., monitor if the behavior of a certain system in a vehicle deviates compared to the system behavior observed in a fleet. This allows a new approach to fault detection that can help reduce development costs of fault detection software and create vehicle individual service planning. The COSMO (consensus self-organized modeling) methodology described in this paper creates a compact representation of the data observed for a subsystem or component in a vehicle. A representation that can be sent to a server in a backoffice and compared to similar representations for other vehicles. The backoffice server can collect representations from a single vehicle over time or from a fleet of vehicles to define a norm of the vehicle condition. The vehicle condition can then be monitored, looking for deviations from the norm. The method is demonstrated for measurements made on a real truck driven in varied conditions with ten different generated faults. The proposed method is able to detect all cases without prior information on what a fault looks like or which signals to use.

  • 12.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Estimation of combustion variability using in-cylinder ionization measurements2001Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark-ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real-time estimation of combustion stability is discussed. © Copyright 2001 Society of Automotive Engineers, Inc.

  • 13.
    Byttner, Stefan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Strategies for handling the fuel additive problem in neural network based ion current interpretation2001Konferensbidrag (Refereegranskat)
    Abstract [en]

    With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g., for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak. © 2001 Society of Automotive Engineers, Inc.

  • 14.
    Cameron, J.
    et al.
    DiLab i Lund AB.
    Jacobson, C.
    AstraZeneca R and D.
    Nilsson, Kenneth
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    A biometric approach to laboratory rodent identification2007Ingår i: Lab animal, ISSN 0093-7355, E-ISSN 1548-4475, Vol. 36, nr 3, s. 36-40Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Individual identification of laboratory rodents typically involves invasive methods, such as tattoos, ear clips, and implanted transponders. Beyond the ethical dilemmas they may present, these methods may cause pain or distress that confounds research results. The authors describe a prototype device for biometric identification of laboratory rodents that would allow researchers to identify rodents without the complications of other methods. The device, which uses the rodent's ear blood vessel pattern as the identifier, is fast, automatic, noninvasive, and painless.

  • 15.
    Carpatorea, Iulian
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Elmer, Marcus
    Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
    APPES Maps as Tools for Quantifying Performance of Truck Drivers2014Ingår i: Proceedings of the 2014 International Conference on Data Mining, DMIN'14 / [ed] Robert Stahlbock & Gary M. Weiss, USA: CSREA Press, 2014, s. 10-16Konferensbidrag (Refereegranskat)
    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.

  • 16.
    Carpatorea, Iulian
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Elmer, Marcus
    Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
    Towards Data Driven Method for Quantifying Performance of Truck Drivers2014Ingår i: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, s. 133-142Konferensbidrag (Refereegranskat)
    Abstract [en]

    Understanding factors that influence fuel consumption is a very important task both for the OEMs in the automotive industry and for their customers. There is a lot of knowledge already available concerning this topic, but it is poorly organized and often more anecdotal than rigorously verified. Nowadays, however, rich datasets from actual vehicle usage are available and a data-mining approach can be used to not only validate earlier hypotheses, but also to discover unexpected influencing factors.

    In this paper we particularly focus on analyzing how behavior of drivers affects fuel consumption. To this end we introduce a concept of “Base Value”, a number that incorporates many constant, unmeasured factors. We show our initial results on how it allows us to categorize driver’s performance more accurately than previously used methods. We present a detailed analysis of 32 trips by Volvo trucks that we have selected from a larger database. Those trips have a large overlap in the route traveled, of over 100 km, and at the same time exhibit different driver and fuel consumption characteristics.

  • 17.
    Carpatorea, Iulian
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Lodin, Johan
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers2017Ingår i: 2017 Intelligent Systems Conference (IntelliSys), 2017, s. 476-481Konferensbidrag (Refereegranskat)
    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

  • 18.
    Carpatorea, Iulian
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Slawomir, Nowaczyk
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Elmer, Marcus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Lodin, Johan
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data2016Ingår i: Proceedings: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) / [ed] Lisa O’Conner, Los Alamitos, CA: IEEE Computer Society, 2016, s. 1067-1072Konferensbidrag (Refereegranskat)
    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.

  • 19.
    Erlandsson, Lena-Karin
    et al.
    Department of Clinical Neuroscience, Division of Occupational Therapy, Lund University, Lund, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Eklund, Mona
    Department of Clinical Neuroscience, Lund University, Lund, Sweden.
    Recognition of Similarities: A Methodological Approach to Analysing and Characterising Patterns of Daily Occupations2004Ingår i: Journal of Occupational Science, ISSN 1442-7591, E-ISSN 2158-1576, Vol. 11, nr 1, s. 3-13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It has been proposed that it should be possible to identify patterns if daily occupations that promote health or cause illness. This study aimed to develop and to evaluate a process for analysing and characterising subjectively perceived patterns of daily occupations, by describing patterns as consisting if main, hidden, and unexpected occupations. Yesterday diaries describing one day if 100 working married mothers were collected through interviews. The diaries were transformed into time-and-occupation graphs. An analysis based on visual interpretation of the patterns was performed. The graphs were grouped into the categories low, medium, or high complexity. In order to identify similarities the graphs were then compared both pair-wise and group-wise. Finally, the complexity and similarities perspectives were integrated, identifying the most typical patterns of daily occupations representing low, medium, and high complexity. Visual differences in complexity were evident. In order to validate the Recognition of Similarities (ROS) process developed, a measure expressing the probability if change was computed. This probability was found to differ statistically significantly between the three groups, supporting the validity of the ROS process. © 2004, Taylor & Francis Group, LLC. All rights reserved.

  • 20.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet2015Ingår i: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, s. 447-456Artikel i tidskrift (Refereegranskat)
    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.

  • 21.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet2015Ingår i: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, s. 58-67Artikel i tidskrift (Refereegranskat)
    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.

  • 22.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Using Histograms to Find Compressor Deviations in Bus Fleet Data2014Ingår i: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, s. 123-132Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cost effective methods for predictive maintenance are increasingly demanded in the automotive industry. One solution is to utilize the on-board signals streams on each vehicle and build self-organizing systems that discover data deviations within a fleet. In this paper we evaluate histograms as features for describing and comparing individual vehicles. The results are based on a long-term field test with nineteen city buses operating around Kungsbacka in Halland. The purpose of this work is to investigate ways of discovering abnormal behaviors and irregularities between histograms of on-board signals, here specifically focusing on air pressure. We compare a number of distance measures and analyze the variability of histograms collected over different time spans. Clustering algorithms are used to discover structure in the data and track how this changes over time. As data are compared across the fleet, observed deviations should be matched against (often imperfect) reference data coming from workshop maintenance and repair databases.

  • 23.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Antonelo, Eric Aislan
    Federal University of Santa Catarina, Florianópolis, Brazil.
    Predicting Air Compressor Failures with Echo State Networks2016Ingår i: PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 / [ed] Ioana Eballard, Anibal Bregon, PHM Society , 2016, s. 568-578Konferensbidrag (Refereegranskat)
    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.

  • 24.
    Hansson, Jörgen
    et al.
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Rögnvaldsson, Thorsteinn
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Byttner, Stefan
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Remote Diagnosis Modelling2008Patent (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    A diagnosis and maintenance method, a diagnosis and maintenance assembly comprising a central server and a system, and a computer program for diagnosis and maintenance for a plurality of systems, particularly for a plurality of vehicles, wherein each system provides at least one system-related signal which provides the basis for the diagnosis and/or maintenance of/for the system are provided. The basis for diagnosis and/or maintenance is determined by determining for each system at least one relation between the system-related signals, comparing the compatible determined relations, determining for the plurality of systems based on the result of the comparison which relations are significant relations and providing a diagnosis and/or maintenance decision based on the determined significant relations.

  • 25.
    Hellring, Magnus
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Munther, Thomas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Carlsson, Christian
    Mecel AB, Gothenburg, Sweden.
    Larsson, Magnus
    Mecel AB, Gothenburg, Sweden.
    Nytomt, Jan
    Mecel AB, Gothenburg, Sweden.
    Robust AFR estimation using the ion current and neural networks1999Ingår i: SAE transactions, ISSN 0096-736X, Vol. 108, nr 03, s. 1585-1589Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A robust air/fuel ratio "soft sensor" is presented based on non-linear signal processing of the ion current signal using neural networks. Care is taken to make the system insensitive to amplitude variations, due to e.g. fuel additives, by suitable preprocessing of the signal. The algorithm estimates the air/fuel ratio to within 1.2% from the correct value, defined by a universal exhaust gas oxygen (UEGO) sensor, when tested on steady state test-bench data and using the raw ion current signal. Normalizing the ion current increases robustness but also increases the error by a factor of two. The neural network soft sensor is about 20 times better in the case where the ion current is not normalized, compared with a linear model. On normalized ion currents the neural network model is about 4 times better than the corresponding linear model. Copyright © 1999 Society of Automotive Engineers, Inc.

  • 26.
    Hellring, Magnus
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Munther, Thomas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Carlsson, Christian
    Mecel AB, Gothenburg, Sweden.
    Larsson, Magnus
    Mecel AB, Gothenburg, Sweden.
    Nytomt, Jan
    Mecel AB, Gothenburg, Sweden.
    Spark advance control using the ion current and neural soft sensors1999Ingår i: SAE transactions, ISSN 0096-736X, Vol. 108, nr 03, s. 1590-1595Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Two spark advance control systems are outlined; both based on feedback from nonlinear neural network soft sensors and ion current detection. One uses an estimate on the location of the pressure peak and the other uses an estimate of the location of the center of combustion. Both quantities are estimated from the ion current signal using neural networks. The estimates are correct within roughly two crank angle degrees when evaluated on a cycle to cycle basis, and roughly within one crank angle degree when the quantities are averaged over consecutive cycles.

    The pressure peak detection based control system is demonstrated on a SAAB 9000 car, equipped with a 2.3 liter low-pressure turbo charged engine, during normal highway driving. © 1998 Society of Automotive Engineers, Inc.

  • 27.
    Hellring, Magnus
    et al.
    Volvo.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Larsson, Magnus
    Mecel AB.
    Ion current based pressure peak detection under different air humidity conditions2000Ingår i: Advanced Microsystems for Automotive Applications 2000 / [ed] Sven Krüger, Wolfgang Gessner, New York: Springer , 2000, s. 125-138Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    A model based soft sensor that estimates the location of the in-cylinder pressure peak from the ion current is described. The soft sensor uses a neural network algorithm and has been implemented in a SAAB 9000 low-pressure turbo production car. It estimates the pressure peak location, in real time, during normal highway driving with an error of 2-3 crank angle degrees. The soft sensor has been tested during normal Scandinavian weather conditions, with a relative air humidity of about 50%, as well as when water is sprayed into the intake manifold, resulting in approximately 100% relative humidity. The neural network based soft sensor is significantly better than that of another method, based on nonlinear Gaussian curve fits, for the same task.

  • 28.
    Kaminska, Hanna
    et al.
    Wroclaw University of Technology.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Assessment of the new scoring function for protein identification by PMF2010Ingår i: Acta Biochimica Polonica, Supplement 1, 2010, Warszawa, Poland: Polish Biochemical Society , 2010, s. 34-34Konferensbidrag (Övrigt vetenskapligt)
  • 29.
    Kamińska, Hanna
    et al.
    Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Wroclaw, Poland.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Evaluation of a new probabilistic approach to scoring algorithms in protein identification by Peptide Mass Fingerprinting (PMF)2011Ingår i: Nutraceutics, biomedical remedies and physiotherapeutic methods for prevention of civilization-related diseases / [ed] Halina Podbielska, Tadeusz Trziszka, Wroclaw, Poland: Indygo Zahir Media , 2011, s. 171-180Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    The protein identification performs a crucial role in the contemporary medicine. Proteins may act as the potential biomarkers for investigating many diseases, e.g. the civilization-related ones.  Peptide mass fingerprinting (PMF) is a widely used protein identification method basing on mass spectrometry data. Economical reasons and time savings are of great importance in the identification experiments. Thereby, innovative ideas, which have the potential to improve the PMF identification, are still desired. A novel probability-based scoring scheme, which constitutes the last part of the PMF identification procedure, was developed. Presented scoring scheme incorporates an innovative idea, which assumes a different approach to modelling the distribution of proteins derived from the database, on the basis of which the score is computed. In the paper we assess a performance of the proposed scoring method against popular scoring scheme, i.e. Mascot (http://www.matrixscience.com/). The comparison of the methods includes scoring results obtained for the simulated data. Different levels of proteins samples contamination and different coverage of peptides sequences were considered in the empirical study.

  • 30.
    Levander, F.
    et al.
    Department of Protein Technology, Lund University, Lund, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Samuelsson, J.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    James, P.
    Department of Protein Technology, Lund University, Lund, Sweden.
    Automated methods for improved protein identification by peptide mass fingerprinting2004Ingår i: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 4, nr 9, s. 2594-2601Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.

  • 31.
    Manasa, Justen
    et al.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Varghese, Vici
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Kosakovsky Pond, Sergei
    Department of Biology, Temple University, Philadelphia, PA, USA.
    Rhee, Soo-Yon
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Tzou, Philip
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Fessel, Jeffrey
    Department of Internal Medicine, Kaiser Permanente Northern California, San Francisco Medical Center, San Francisco, CA, USA.
    Jang, Karen
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    White, Elizabeth
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Katzenstein, David A.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Shafer, Robert A.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors2017Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, nr 1, artikel-id 11559Artikel i tidskrift (Refereegranskat)
    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).

  • 32.
    Mosallam, Ahmed
    et al.
    Örebro University.
    Byttner, Stefan
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Svensson, Magnus
    Volvo Technology.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Nonlinear relation mining for maintenance prediction2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a method for mining nonlinear relationships in machine data with the purpose of using such relationships to detect faults, isolate faults and predict wear and maintenance needs. The method is based on the symmetrical uncertainty measure from information theory, hierarchical clustering and self-organizing maps. It is demonstrated on synthetic data sets where it is shown to be able to detect interesting signal relations and outperform linear methods. It is also demonstrated on real data sets where it is considerably harder to select small feature sets. It is also demonstrated on the real data sets that there is information about system wear and system faults in the detected relationships. The work is part of a long-term research project with the aim to construct a self-organizing autonomic computing system for self-monitoring of mechatronic systems.

  • 33.
    Nilsson, Kenneth
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Cameron, Jens
    DiLab, Lund, Sweden.
    Jacobson, Christina
    Astra Zeneca, Lund, Sweden.
    Biometric identification of mice2006Ingår i: The 18th International Conference on Pattern Recognition: Proceedings : 20 - 24 August, 2006, Hong Kong, Los Alamitos, Calif.: IEEE Computer Society, 2006, s. 465-468Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a new application area for biometric recognition: the identification of laboratory animals to replace today's invasive methods. Through biometric identification a non invasive identification technique is applied with a code space that is restricted only by the uniqueness of the biometric identifier in use, and with an error rate that is predictable. In this work we present the blood vessel pattern in a mouse-ear as a suitable biometric identifier used for mouse identification. Genuine and impostor score distributions are presented using a total of 50 mice. An EER of 2.5% is reported for images captured at the same instance of time which verifies the distinctive property of the biometric identifier.

  • 34.
    Nowaczyk, Sławomir
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data2013Ingår i: Twelfth Scandinavian Conference on Artificial Intelligence / [ed] Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani, Amsterdam: IOS Press, 2013, s. 205-214Konferensbidrag (Refereegranskat)
    Abstract [en]

    Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data.

    We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski's classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more exibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner. © 2013 The authors and IOS Press. All rights reserved.

  • 35.
    Prytz, Rune
    et al.
    Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Analysis of Truck Compressor Failures Based on Logged Vehicle Data2013Ingår i: / [ed] Hamid Reza Arabnia, CSREA Press, 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.

    In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.

  • 36.
    Prytz, Rune
    et al.
    Volvo Group Trucks Technology, Gothenburg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data2015Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 41, s. 139-150Artikel i tidskrift (Refereegranskat)
    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.

  • 37.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    A simple trick for estimating the weight decay parameter1998Ingår i: Neural Networks: Tricks of the Trade / [ed] Genevieve B. Orr and Klaus-Robert Müller, Berlin: Springer Berlin/Heidelberg, 1998, s. 71-92Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a simple trick to get an approximate estimate of the weight decay parameter lambda. The method combines early stopping and weight decay, into the estimate lambda=parallel to del E(W(es))parallel to/parallel to 2W(es)parallel to, where W(es) is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for lambda selection on one synthetic and four real life data sets. The result is that lambda is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stop ping.

  • 38.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    A Simple trick for estimating the weight decay parameter2012Ingår i: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 7700 LECTURE NO, s. 69-89Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a simple trick to get an approximate estimate of the weight decay parameter λ. The method combines early stopping and weight decay, into the estimate λ̂ = ||∇E(Wes)||/||2W es||, where Wes is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for λ selection on one synthetic and four real life data sets. The result is that is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stopping. © Springer-Verlag Berlin Heidelberg 2012.

  • 39.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Brink, Joachim
    Högskolan i Halmstad.
    Florén, Henrik
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL).
    Gaspes, Veronica
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Holmgren, Noél
    University of Skövde, Skövde, Sweden.
    Lutz, Mareike
    Högskolan i Halmstad.
    Nilsson, Pernilla
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Forskning om utbildning och lärande inom lärarutbildningen (FULL).
    Olsfelt, Jonas
    Högskolan i Halmstad.
    Svensson, Bertil
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ericsson, Claes
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Forskning om utbildning och lärande inom lärarutbildningen (FULL).
    Gustafsson, Linnea
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Kontext & kulturgränser (KK).
    Hoveskog, Maya
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL).
    Hylander, Jonny
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Jonsson, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nygren, Jens
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI).
    Rosén, Bengt-Göran
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Maskinteknisk produktframtagning (MTEK).
    Sandberg, Mikael
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Centrum för samhällsanalys (CESAM).
    Benner, Mats
    Lund University, Lund, Sweden.
    Berg, Martin
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Centrum för samhällsanalys (CESAM).
    Bergvall, Patrik
    Högskolan i Halmstad.
    Carlborg, Anna
    Högskolan i Halmstad.
    Fleischer, Siegfried
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Hållander, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Mattsson, Marie
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Olsson, Charlotte
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Pettersson, Håkan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rundquist, Jonas
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL).
    Sahlén, Göran
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Waara, Sylvia
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Weisner, Stefan
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    Werner, Sven
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).
    ARC13 – Assessment of Research and Coproduction: Reports from the assessment of all research at Halmstad University 20132014Rapport (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    During 2013, an evaluation of all the research conducted at Halmstad University was carried out. The purpose was to assess the quality of the research, coproduction, and collaboration in research, as well as the impact of the research. The evaluation was dubbed the Assessment of Research and Coproduction 2013, or ARC13. (Extract from Executive Summary)

  • 40.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Wisdom of Crowds for Intelligent Monitoring of Vehicle FleetsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

  • 41.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Etchells, Terence A
    School of Computing and Mathematical Sciences, Liverpool John Moores University.
    You, Liwen
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Garwicz, Daniel
    Department of Molecular Medicine and Surgery, Karolinska Institutet.
    Jarman, Ian
    School of Computing and Mathematical Sciences, Liverpool John Moores University.
    Lisboa, Paulo J G
    School of Computing and Mathematical Sciences, Liverpool John Moores University.
    How to find simple and accurate rules for viral protease cleavage specificities2009Ingår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 10, s. 149-156Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    BACKGROUND:

    Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.

    RESULTS:

    A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.

    CONCLUSION:

    A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

  • 42.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Häkkinen, Jari
    Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund, Sweden.
    Lindberg, Claes
    Molecular Sciences, AstraZeneca RandD Lund, SE-221 87 Lund, Sweden.
    Marko-Varga, György
    Molecular Sciences, AstraZeneca RandD Lund, SE-221 87 Lund, Sweden.
    Potthast, Frank
    Funct. Genomics Center Zürich, Winterthurerstr. 190 Y32 H52, CH-8057 Zürich, Switzerland.
    Samuelsson, Jim
    Genedata GmbH, Lena-Christ-Str. 50, D-82152 Martinsried, Germany.
    Improving automatic peptide mass fingerprint protein identification by combining many peak sets2004Ingår i: Journal of chromatography. B, ISSN 1570-0232, E-ISSN 1873-376X, Vol. 807, nr 2, s. 209-215Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An automated peak picking strategy is presented where several peak sets with different signal-to-noise levels are combined to form a more reliable statement on the protein identity. The strategy is compared against both manual peak picking and industry standard automated peak picking on a set of mass spectra obtained after tryptic in gel digestion of 2D-gel samples from human fetal fibroblasts. The set of spectra contain samples ranging from strong to weak spectra, and the proposed multiple-scale method is shown to be much better on weak spectra than the industry standard method and a human operator, and equal in performance to these on strong and medium strong spectra. It is also demonstrated that peak sets selected by a human operator display a considerable variability and that it is impossible to speak of a single “true” peak set for a given spectrum. The described multiple-scale strategy both avoids time-consuming parameter tuning and exceeds the human operator in protein identification efficiency. The strategy therefore promises reliable automated user-independent protein identification using peptide mass fingerprints.

  • 43.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Norrman, Henrik
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Estimating p-Values for Deviation Detection2014Ingår i: Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014 / [ed] Randall Bilof, Los Alamitos, CA: IEEE Computer Society, 2014, s. 100-109Konferensbidrag (Refereegranskat)
    Abstract [en]

    Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervisi- n and without prior knowledge about what is being monitored. © 2014 IEEE.

  • 44.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Self-monitoring for maintenance of vehicle fleets2018Ingår i: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, nr 2, s. 344-384Artikel i tidskrift (Refereegranskat)
    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)

  • 45.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Panholzer, Georg
    Salzburg Research Advanced Networking Center Jakob-Haringer-Str. 51111 5020, Salzburg, Austria.
    Byttner, Stefan
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Technology, 405 08 Goteborg, Sweden.
    A self-organized approach for unsupervised fault detection in multiple systems2008Ingår i: 19th International Conference on Pattern Recognition: (ICPR 2008) ; Tampa, Florida, USA 8-11 December 2008, Piscataway, N.J.: IEEE Press, 2008, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    An approach is proposed for automatic fault detection in a population of mechatronic systems. The idea is to employ self-organizing algorithms that produce low-dimensional representations of sensor and actuator values on the vehicles, and compare these low-dimensional representations among the systems. If a representation in one vehicle is found to deviate from, or to be not so similar to, the representations for the majority of the vehicles, then the vehicle is labeled for diagnostics. The presented approach makes use of principal component coding and a measure of distance between linear sub-spaces. The method is successfully demonstrated using simulated data for a commercial vehiclepsilas engine coolant system, and using real data for computer hard drives.

  • 46.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    You, Liwen
    Lund University, Department of Theoretical Physics, Lund, Sweden.
    Why neural networks should not be used for HIV-1 protease cleavage site prediction2004Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, nr 11, s. 1702-1709Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Several papers have been published where non-linear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers.

  • 47.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    You, Liwen
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Garwicz, Daniel
    Karolinska Institutet, Department of Molecular Medicine and Surgery, Karolinska University Hospital, SE-17176, Stockholm, Sweden.
    Bioinformatic approaches for modeling the substrate specificity of HIV-1 protease: an overview2007Ingår i: Expert Review of Molecular Diagnostics, ISSN 1473-7159, E-ISSN 1744-8352, E-ISSN 1744-8352, Vol. 7, nr 4, s. 435-451Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    HIV-1 protease has a broad and complex substrate specificity, which hitherto has escaped a simple comprehensive definition. This, and the relatively high mutation rate of the retroviral protease, makes it challenging to design effective protease inhibitors. Several attempts have been made during the last two decades to elucidate the enigmatic cleavage specificity of HIV-1 protease and to predict cleavage of novel substrates using bioinformatic analysis methods. This review describes the methods that have been utilized to date to address this important problem and the results achieved. The data sets used are also reviewed and important aspects of these are highlighted.

  • 48.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    You, Liwen
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Garwicz, Daniel
    Uppsala University, Uppsala, Sweden.
    State of the art prediction of HIV-1 protease cleavage sites2015Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, nr 8, s. 1204-1210Artikel i tidskrift (Refereegranskat)
    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.

  • 49.
    Samuelsson, Jim
    et al.
    Genedata GmbH, Lena-Christ-Strasse 50, 82152 Martinsried, Germany.
    Dalevi, Daniel
    Computing Science, Chalmers University of Technology, SE-412 96 Göteborg.
    Levander, Fredrik
    Department of Protein Technology, Lund University, Sölvegatan 33A.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting2004Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, nr 18, s. 3628-3635Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified.

  • 50.
    Svensson, Magnus
    et al.
    Volvo Technology, 405 08 Göteborg, Sweden.
    Byttner, Stefan
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Self-organizing maps for automatic fault detection in a vehicle cooling system2008Ingår i: 4th International IEEE Conference Intelligent Systems, 2008. IS '08, Piscataway, N.J.: IEEE Press, 2008, s. 24-8-24-12Konferensbidrag (Refereegranskat)
    Abstract [en]

    A telematic based system for enabling automatic fault detection of a population of vehicles is proposed. To avoid sending huge amounts of data over the telematics gateway, the idea is to use low-dimensional representations of sensor values in sub-systems in a vehicle. These low-dimensional representations are then compared between similar systems in a fleet. If a representation in a vehicle is found to deviate from the group of systems in the fleet, then the vehicle is labeled for diagnostics for that subsystem. The idea is demonstrated on the engine coolant system and it is shown how this self-organizing approach can detect varying levels of clogged radiator.

12 1 - 50 av 57
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf