To Örebro University

oru.seÖrebro University Publications
Change search
Refine search result
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Badica, Costin
    University of Craiova, Craiova, Romania.
    Comes, Tina
    Karslruhe Institute of Technology, Karslruhe, Germany.
    Conrado, Claudine
    Thales Research and Technology, Delft, The Netherlands.
    Evers, Vanessa
    University of Amsterdam, Amsterdam, The Netherlands.
    Groen, Frans
    University of Amsterdam, Amsterdam, The Netherlands.
    Illie, Sorin
    University of Craiova, Craiova, Romania.
    Steen Jensen, Jan
    Danish Emergency Management Agency (DEMA), Birkerød, Denmark.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Milan, Bianca
    DCMR, Delft, The Netherlands.
    Neidhart, Thomas
    Space Applications Services, Zaventem, Belgium.
    Nieuwenhuis, Kees
    Thales Research and Technology, Delft, The Netherlands.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Pavlin, Gregor
    Thales Research and Technology, Delft, The Netherlands.
    Pehrsson, Jan
    Prolog Development Center, Brøndby Copenhagen, Denmark.
    Pinchuk, Rani
    Space Applications and Services, Zaventem, Belgium.
    Scafes, Mihnea
    University of Craiova, Craiova, Romania.
    Schou-Jensen, Leo
    DCMR, Brøndby Copenhagen, Denmark.
    Schultmann, Frank
    Karslruhe Institute of Technology, Karlsruhe, Germany.
    Wijngaards, Niek
    Thales Research and Technology, Delft, the Netherlands.
    ICT solutions supporting collaborative information acquisition, situation assessment and decision making in contemporary environmental management problems: the DIADEM approach2011In: Proceedings of the 25th EnviroInfo Conference "Environmental Informatics", Herzogenrath: Shaker Verlag, 2011, p. 920-931Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project.

    Download full text (pdf)
    fulltext
  • 2.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    TD Kernel DM+V: time-dependent statistical gas distribution modelling on simulated measurements2011In: Olfaction and Electronic Nose: proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN) / [ed] Perena Gouma, Springer Science+Business Media B.V., 2011, p. 281-282Conference paper (Refereed)
    Abstract [en]

    To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.

    Download full text (pdf)
    fulltext
  • 3.
    Dashti, HesamAddin T.
    et al.
    School of Math and Computer Science, University of Tehran.
    Aghaeepour, NimaSchool of Math and Computer Science, University of Tehran.Asadi, SaharÖrebro University, School of Science and Technology.Bastani, MeysamSchool of Math and Computer Science, University of Tehran.Delafkar, ZahraSchool of Math and Computer Science, University of Tehran.Disfani, Fatemeh M.School of Math and Computer Science, University of Tehran.Ghaderi, Serveh M.School of Math and Computer Science, University of Tehran.Kamali, ShahinSchool of Math and Computer Science, University of Tehran.Pashami, SepidehÖrebro University, School of Science and Technology.Siahpirani, Alireza F.School of Math and Computer Science, University of Tehran.
    Dynamic Positioning based on Voronoi Cells (DPVC)2006Collection (editor) (Refereed)
    Abstract [en]

    In this paper we are proposing an approach for flexible positioning of players in Soccer Simulation in a Multi-Agent environment. We introduce Dynamic Positioning based on Voronoi Cells (DPVC) as a new method for players' positioning which uses Voronoi Diagram for distributing agents in the field. This method also uses Attraction Vectors that indicate agents' tendency to specific objects in the field with regard to the game situation and players' roles. Finally DPVC is compared with SBSP as the conventional method of positioning.

  • 4.
    Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Bringing Artificial Olfaction and Mobile Robotics Closer Together: An Integrated 3D Gas Dispersion Simulator in ROS2015In: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses, 2015, article id 137Conference paper (Refereed)
    Abstract [en]

    Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.

  • 5.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Change detection in metal oxide gas sensor signals for open sampling systems2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis addresses the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an Open Sampling System (OSS). Changes can occur due to gas source activity such as a sudden alteration in concentration or due to exposure to a different compound. Applications such as gas-leak detection in mines or large-scale pollution monitoring can benefit from reliable change detection algorithms, especially where it is impractical to continuously store or transfer sensor readings, or where reliable calibration is difficult to achieve. Here, it is desirable to detect a change point indicating a significant event, e.g. presence of gas or a sudden change in concentration. The main challenges are turbulent dispersion of gas and the slow response and recovery times of MOX sensors. Due to these challenges, the gas sensor response exhibits fluctuations that interfere with the changes of interest.

    The contributions of this thesis are centred on developing change detection methods using MOX sensor responses. First, we apply the Generalized Likelihood Ratio algorithm (GLR), a commonly used method that does not make any a priori assumption about change events. Next, we propose TREFEX, a novel change point detection algorithm, which models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We also propose the rTREFEX algorithm as an extension of TREFEX. The core idea behind rTREFEX is an attempt to improve the fitted exponentials of TREFEX by minimizing the number of exponentials even further.

    GLR, TREFEX and rTREFEX are evaluated for various MOX sensors and gas emission profiles. A sensor selection algorithm is then introduced and the change detection algorithms are evaluated with the selected sensor subsets. A comparison between the three proposed algorithms shows clearly superior performance of rTREFEX both in detection performance and in estimating the change time. Further, rTREFEX is evaluated in real-world experiments where data is gathered by a mobile robot. Finally, a gas dispersion simulation was developed which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model.

    Download full text (pdf)
    Thesis
    Download full text (pdf)
    Spikblad
    Download (pdf)
    Cover
  • 6.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Integration of OpenFOAM Flow Simulation and Filament-Based Gas Propagation Models for Gas Dispersion Simulation2010Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data.

    Download (pdf)
    OSCIC-10_PashamiSepideh.pdf
  • 7.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6/7, p. 1123-1127Article in journal (Refereed)
    Abstract [en]

     The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.

  • 8.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    TREFEX: trend estimation and change detection in the response of mox gas sensors2013In: Sensors, E-ISSN 1424-8220, Vol. 13, no 6, p. 7323-7344Article in journal (Refereed)
    Abstract [en]

    Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time.

    Download full text (pdf)
    sensors-13-07323.pdf
  • 9.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    A trend filtering approach for change point detection in MOX gas sensors2013Conference paper (Refereed)
    Abstract [en]

    Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6].

    Download full text (pdf)
    Pashami_etal_2013-ISOEN.pdf
  • 10.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Change detection in an array of MOX sensors2012Conference paper (Refereed)
    Abstract [en]

    In this article we present an algorithm for online detection of change points in the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system.True change points occur due to changes in the emission modality of the gas source. The main challenge for change point detection in an open sampling system is the chaotic nature of gas dispersion, which causes fluctuations in the sensor response that are not related to changes in the gas source. These fluctuations should not be considered change points in the sensor response. The presented algorithm is derived from the well known Generalized Likelihood Ratio algorithm and it is used both on the output of a single sensor as well on the output of two or more sensors on the array. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, or mixture ratio. The performance measures considered are the detection rate, the number of false alarms and the delay of detection.

    Download full text (pdf)
    fulltext
  • 11.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Detecting changes of a distant gas source with an array of MOX gas sensors2012In: Sensors, E-ISSN 1424-8220, Vol. 12, no 12, p. 16404-16419Article in journal (Refereed)
    Abstract [en]

    We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.

    Download full text (pdf)
    fulltext
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf