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  • 1.
    Alm, Torbjörn
    et al.
    Linköping University, Department of Management and Engineering, Industrial ergonomics. Linköping University, The Institute of Technology.
    Kovordányi, Rita
    Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory. Linköping University, The Institute of Technology.
    Ohlsson, Kjell
    Linköping University, Department of Management and Engineering, Industrial ergonomics. Linköping University, The Institute of Technology.
    Continuous versus Situation-dependent Night Vision Presentation in Automotive Applications2006Conference paper (Refereed)
    Abstract [en]

    As the number of advanced driver assistance systems in modern cars increases the question of possible negative behavioral adaptation is raised. We have investigated this phenomenon for night vision systems in a driving simulator. One common opinion is that there is a risk for using the enhanced visual conditions that come with these systems to increase speed during nighttime driving and thereby eliminate the safety margins the system was designed to provide. In our study two system approaches were compared, one with continuous presentation and one with presentation only when dangerous objects were detected by the system. The latter approach was meant to minimize the risk of negative adaptation, which was partly confirmed in the study. Moreover, the results showed better and more consistent driver performance with the situation-dependent system and all subjects preferred this approach from a workload perspective.

  • 2.
    Alm, Torbjörn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Ohlsson, Kjell
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Kovordanyi, Rita
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory.
    Glass Cockpit Simulators - Tools for IT-based Car Systems Design and Evaluation2005Conference paper (Refereed)
  • 3.
    Eriksson, Henrik
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Rankin, Amy
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    CRISIS: Virtual-reality-based training for emergency management2010Conference paper (Refereed)
  • 4.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, Department of Computer and Information Science, Human-Centered systems.
    Controlled exploration of alternative mechanisms in cognitive modeling2000In: Twenty-Second Annual Meeting of the Cognitive Science Society,2000, Mahwah, NJ: Lawrence Erlbaum Associates , 2000, p. 280-285Conference paper (Refereed)
  • 5.
    Kovordanyi, Rita
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory. Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces.
    Factorial modeling: A method for enhancing the explanatory and predictive power of cognitive models2001In: Fourth International Conference on Cognitive Modeling, 2001, Mahwah, NJ: Lawrence Erlbaum Associates , 2001, p. 127-Conference paper (Refereed)
    Abstract [en]

    The construction and evaluation of cognitive models can, and often do, lead to novel insights into what might constitute a valid account for an empirical phenomenon. These insights constrain the space of viable models, and could be useful also on a theoretical plane, by promoting a deeper understanding of the studied phenomenon. We propose the factorial method for deriving novel, that is, not theory–based constraints in a principled way during model development. The method is based on a systematic comparison of alternative models, realized through a cross–combination of model components in a generic cognitive model. We illustrate the method by describing an application in the area of mental imagery. We conclude by discussing ways to increase the generalizability of results that can be obtained using the factorial method.

  • 6.
    Kovordanyi, Rita
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory.
    Full factorial simulation modeling of selective attention in mental imagery2000In: International Journal of Psychology, ISSN 0020-7594, E-ISSN 1464-066X, Vol. 35, no 3-4, p. 100-100Conference paper (Other academic)
  • 7.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, Department of Computer and Information Science, Human-Centered systems.
    Is mental imagery symbolic?: Exploratory simulations in an interactive activation model1998In: Proceedings of the 2nd EuropeanConference on Cognitive Modelling, 1998, Nottingham: Nottingham University Press, 1998, p. 197-Conference paper (Refereed)
  • 8.
    Kovordanyi, Rita
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory.
    Mental image reinterpretation in the intersection of conceptual and visual constraints1999In: Visual representations and interpretation, London: Springer Verlag , 1999, p. 263-269Chapter in book (Other academic)
  • 9.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Modeling and simulating inhibitory mechanisms in mental image reinterpretation: towards cooperative human-computer creativity1999Doctoral thesis, monograph (Other academic)
    Abstract [en]

    With the accelerating development of computer and software technology, human-computer cooperation issues are becoming more and more centered on the human user's abilities and weaknesses. The cognitive characteristics of visual communication and reasoning, and how these affect the way users take advantage of the richness of visually represented information comprise one area which needs to be further explored within this context.

    The work reported in this thesis aims to identify cognitive mechanisms which might inhibit the creative interpretation of visual information, and thereby indicate which aspects of visual creativity may benefit from support in a cooperative human-computer system.

    We approached this problem by initially focusing on one central mechanism, selective attention, with an analysis of its constraining role in mental image reinterpretation. From this kernel, a partial model of mental image reinterpretation was developed. Given this framework, a family of related, yet, at a detailed level contradictory cognitive models was simulated to determine which model components contributed in what way to overall model performance. Model performance was evaluated with regard to empirical data on human re-interpretation performance.

    Our work contributes an integrated theory for selective attention and a simulation-based investigation of its role in mental image reinterpretation. We have developed and evaluated a method for investigating the causal structure of cognitive models using interactive activation modeling and systematic computer simulations. Also, we account for our experience in combining computer science methods with the cognitive modeling paradigm.

  • 10.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, ASLAB - Application Systems Laboratory. Linköping University, The Institute of Technology.
    Sequencing of information versus interfacing between processing levels2002In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 18, no 1, p. 47-49Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    [No abstract available]

  • 11.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    When will adaptive support systems be user-adaptive? The case of adaptive cruise control.2005In: AAAI Spring Symposium on Challenges to Decision Support in a Changing World.,2005, 2005Conference paper (Refereed)
    Abstract [en]

    Advanced driver support systems are often used by drivers as comfort systems. Drivers tend to adapt their behavior to the increased safety margins created by the system by driving at higher speeds and paying less attention to driving. From a traffic safety perspective, it is therefore important that driver support systems can adapt their support to how drivers actually use the system. We propose a way to mitigate drivers- misuse of the safety margins created by the system by employing an adaptive support policy that is less predictable for the driver. We conclude by describing ongoing work, were the safety effect of such an adaptive support policy is empirically studied.

  • 12.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Alm, Torbjörn
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Ohlsson, Kjell
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Night-vision display unlit during uneventful periods may improve traffic safety2006In: 2006 Intelligent Vehicles Symposium sponsored by the IEEE Intelligent Transportation Systems Society,2006, IEEE conference proceedings, 2006, p. 282-287Conference paper (Refereed)
    Abstract [en]

    It is always uncertain if a new assistance system enhances traffic safety or not: empirical studies indicate that driving style may deteriorate when the driver experiences the increased safety margin created by an advanced driver assistance system. To minimize this negative effect on driving style, we redesigned a night vision system so that it appeared differently to the driver: we let the system's head-up display be turned off during operation, to be lit up only when the system detected an obstacle (e.g., a pedestrian or animal) on the road ahead. This presentation style was compared in a simulator study to the traditional solution of constantly lit-up display. The results indicate that drivers reacted more reliably (showed less variance in reaction times) using the new system, which implies that the lighting up of the IR-display constituted an effective warning. Also, drivers to a greater extent drove at normal (slower) speeds when using the re-designed system. More generally, systems offering discontinuous support (i.e. only in critical situations) may have less of a negative effect on driving style, as their presence is not felt as vividly by the driver

  • 13.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Advanced Decision Support in Simulator-Based Training for Crisis Management2013Conference paper (Refereed)
    Abstract [en]

    This paper describes the development of a decision support and knowledge management system as part of an EU FP7 funded project, CRISIS. In its final form, the decision support and knowledge management system was implemented as an Android app that uses Esper for complex event processing. The reasoning engine of the decision support and knowledge management system is backed with an ontology and knowledge representation implemented in Topic Maps.

  • 14.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Ohlsson, Kjell
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Alm, Torbjörn
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Industrial Ergonomics.
    Dynamically Deployed Support as a Potential Solution to Negative Behavioral Adaptation2005In: 2005 IEEE Intelligent Vehicles Symposium Proceedings, 2005, IEEE conference proceedings, 2005, p. 613-618Conference paper (Refereed)
    Abstract [en]

    Advanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adaptation, meaning that drivers tend to increase speed and pay less attention to driving when supported by an advanced assistance system. We relate behavioral adaptation to reinforcement learning at a subconscious level, and propose that driver assistance is dynamically varied within predetermined safety limits. The aim of employing a dynamic assistance policy is to prevent the driver from noticing a constant improvement in vehicle handling. We conclude by describing ongoing work for empirically evaluating an improved lane departure warning system that uses a dynamic assistance policy.

  • 15.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Ohlsson, Susanna
    Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory. Linköping University, The Institute of Technology.
    Toward adaptive support: Modelling drivers' allocation of attention2002Conference paper (Refereed)
    Abstract [en]

    Driver distraction and inattention are major contributing factors in traffic accidents (cf., e.g., Najm et al, 1995). Some of these accidents might be avoided in the future if drivers’ (miss)allocation of attention could be detected, and the driver be prompted toward key events in the traffic scene. Our objective is to develop a cognitively based driver model where drivers’ allocation of attention can be simulated for diagnostic purposes. As a first step, we present a connectionist model framework implemented in PDP++. This framework is based on the notion that focusing attention on a visual object is closely coupled with an intention to act on that object. Using our model, we want to continuously assess if the driver can maintain a general preparedness to act, and hence detect unexpected events, or if he/she is overloaded by non–driving–related tasks. In a first step, we intend to study how attention can be allocated to subtasks in the model, to find out to what extent multiple tasks can be parallelized by drivers. For further develop-ments on this project, visit http://www.ida.liu.se/~ritko.html

  • 16.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, ASLAB - Application Systems Laboratory. Linköping University, The Institute of Technology.
    Ohlsson, Susanna
    Linköping University, Department of Computer and Information Science, ASLAB - Application Systems Laboratory. Linköping University, The Institute of Technology.
    Toward adaptive support: Modelling drivers' allocation of attention2002In: Proceedings of the Thirty-Fourth Annual Congress of the Nordic Ergonomic Society, NES , 2002, p. 493-498Conference paper (Other academic)
  • 17.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Pelfrene, Jelle
    Space Application Services.
    Rankin, Amy
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Schreiner, Rudolf
    Object Security.
    Jenvald, Johan
    VSL Systems.
    Morin, Magnus
    VSL Systems.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Real-time Support for Exercise Managers’ Situation Assessment and Decision Making2012Conference paper (Refereed)
    Abstract [en]

    Exercise managers and instructors have a particularly challenging task in monitoring and controlling on-going exercises, which may involve multiple response teams and organizations in highly complex and continuously evolving crisis situations. Managers and instructors must handle potentially incomplete and conflicting field-observation data and make decisions in real-time in order to control the flow of the exercise and to keep it in line with the training objectives. In simulation-based exercises, managers and instructors have access to a rich set of real-time data, with an increased potential to closely monitor the trainees’ actions, and to keep the exercise on track. To assist exercise managers and instructors, data about the on-going exercise can be filtered, aggregated and refined by real-time decision-support systems. We have developed a model and a prototype decision-support system, using stream-based reasoning to assist exercise managers and instructors in real-time. The approach takes advantage of topic maps for ontological representation and a complex-event processing engine for analyzing the data stream from a virtual-reality simulator for crisis-management training. Aggregated data is presented both on-screen, in Twitter, and in the form of topic maps.

  • 18.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Rankin, Amy
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Foresight training as part of virtual-reality-based exercises for the emergency services2010Conference paper (Refereed)
    Abstract [en]

    Due to the chaotic nature of accidents and crisis, emergency responses tend to unfold in a highly dynamic fashion. It is therefore of key importance that emergency service staff are continually trained on being mindful of risks and to spot early signs of things that could go wrong during an emergency response. This article suggests a way to adapt existing regimes for foresight training to the needs of emergency response organizations. Foresight training is currently being tried out in healthcare, and similar ideas, i.e. to base training on “what-if” discussions of typical high-risk scenarios, have also been implemented in the mining industry, and in the off-shore oil and gas industry. We follow this trend and suggest a way for foresight training to be integrated into virtual-reality-based emergency response exercises as part of the after action review (when the emergency response exercise is debriefed). The material for foresight training could be based on events that were encountered during the preceding exercise, as well as other typical high-risk situations, and subsequent discussions could, for example, be focused on the factors contributing to an elevated risk level and to what extent a negative development of events could be avoided through insightful actions. Hence, focus is on training to recognize typical risk factors and associate these with appropriate defensive steps.

  • 19.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Roy, Chandan
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Cyclone track forecasting based on satellite images using artificial neural networks2009In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 64, no 6, p. 513-521Article in journal (Refereed)
    Abstract [en]

    Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on satellite images. The trained network produced correct directional forecast for 98% of test images, thus showing a good generalization capability. The results indicate that multi-layer neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds, water temperature, humidity, and air pressure.

  • 20.
    Kovordanyi, Rita
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Roy, Chandan
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Saifullah, Mohammad
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Local Feature Extraction—What Receptive Field Size Should Be Used?2009In: Proceedings of International Conference on Image Processing, Computer Vision and Pattern Recognition, 2009Conference paper (Refereed)
    Abstract [en]

    Biologically inspired hierarchical networks for image processing are based on parallel feature extraction across the image using feature detectors that have a limited Receptive Field (RF). It is, however, unclear how large these receptive fields should be. To study this, we ran systematic tests of various receptive field sizes using the same hierarchical network. After 40 epochs of training, we tested the network both by using similar but novel images of the same tropical cyclone that was used for training, and by using dissimilar images, depicting different cyclones. The results indicate that correct RF size is important for generalization in hierarchical networks, and that RF size should be chosen so that all RFs at least partially cover meaningful parts of the input image.

  • 21.
    Rankin, Amy
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Field, Joris
    National aerospace laboratories, NLR.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Instructor’s Tasks in Crisis Management Training2012In: Proceedings of the 9th International ISCRAM Conference, 2012, 2012Conference paper (Other academic)
    Abstract [en]

    In crisis management exercises the instructor’s performance is critical to the success of the training. It is their responsibility to monitor and evaluate the exercise, as well as appropriately adjust and adapt the scenario to the unfolding events. Despite the importance of the instructor’s skills in crisis management training little has been documented regarding successful methods or common pitfalls. The study presented in this paper is exploratory and aimed at investigating how instructors monitor and control large scale crisis management exercises. The results are intended to be used as a basis for further investigation on how instructors can be supported in virtual reality training systems. A summary of results from interviews is presented and followed by observations reports from two live exercises. Finally, key areas for instructor support in virtual-reality training systems are identified.

  • 22.
    Rankin, Amy
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Field, Joris
    National Aerospace Laboratory (NLR), Amsterdam, Netherlands.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Morin, Magnus
    VSL Research Labs, Linköping, Sweden.
    Jenvald, Johan
    VSL Research Labs, Linköping, Sweden.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Training Systems Design: Bridging The Gap Between User and Developers Using Storyboards2011In: Proceedings of the 29th Annual European Conference on Cognitive Ergonomics, 2011, p. 205-212Conference paper (Other academic)
    Abstract [en]

    Motivation -- Designing distributed training systems for crisis management (CM) requires an approach with the ability to address a great variety of needs and goals. Crisis responses involve multiple agents, each with different backgrounds, tasks, priorities, goals, responsibilities, organizations, equipment, and approaches. Identifying the different user training needs and translating these into user and functional requirement therefore poses great challenges.

    Research approach -- In this paper we present experiences of how to enable the collaboration between multiple stakeholders and partners when creating and adapting ideas throughout the design phase. The techniques have been used in a European project aimed at developing an interactive Virtual Reality (VR) environment for training crisis management.

    Findings/Design -- The focus of the paper is on the initial storyboard iterations and lo-fi prototypes, as this is a crucial stage for expressing ideas in a perceivable way without having to spend too much time and effort on creating detailed prototypes.

    Take away message -- Experiences using low-cost commercial software for creating storyboards are presented, as these provided the means to create, share, present, adapt and circulate ideas, facilitating the fusing of ideas, shared understanding and distributed working.

  • 23.
    Rankin, Amy
    et al.
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Episode Analysis for Evaluating Response Operations and Identifying Training Needs2010In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction, 2010Conference paper (Refereed)
    Abstract [en]

    In Emergency Management (EM) teams are faced with dynamic and complex situations, often involving multiple teams and organizations working together under stressful circumstances. One of the key issues observed in emergency responses is inadequate. The communicative problems stem from various issues such as political, personal or jurisdictional problems. In order to improve communication and coordination, we need to gain a profound understanding of what the communication issues are and, further, we need to provide meaningful inter-organizational training regimes targeting these issues. We suggest episode analysis, a qualitative research method, to better understand the communication taking place during an emergency response. Episode analysis provides a way to code and analyze data involving multiple teams and organizations as well as a way to study more informal communicative functions that would otherwise be difficult to capture. Moreover, we suggest that episode analyses can be used to identify training needs and be helpful in creating meaningful training scenarios.

  • 24.
    Roy, Chandan
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    The current cyclone early warning system in Bangladesh: Providers' and receivers' views2015In: International Journal of Disaster Risk Reduction, E-ISSN 2212-4209, Vol. 12, p. 285-299Article in journal (Refereed)
    Abstract [en]

    Bangladesh has experienced several catastrophic Tropical Cyclones (TCs) during the last decades. Despite the efforts of disaster management organizations, as well as the Bangladesh Meteorological Department (BMD), there were lapses in the residents’ evacuation behavior. To examine the processes of TC forecasting and warning at BMD and to understand the reasons for residents’ reluctance to evacuate after a cyclone warning, we conducted an individual in-depth interview among the meteorologists at BMD, as well as a questionnaire survey among the residents living in the coastal areas. The results reveal that the forecasts produced by BMD are not reliable for longer than 12-h. Therefore, longer-term warnings have to be based on gross estimates of TC intensity and motion, which renders the disseminated warning messages unreliable. Our results indicate that residents in the coastal areas studied, do not follow the evacuation orders due to mistrust of the warning messages—which can deter from early evacuation; and insufficient number of shelters and poor transportation possibilities—which discourages late evacuation. Suggestions made by the residents highlight the necessity of improved warning messages in the future. These findings indicate the need for improved forecasting, and more reliable and more informative warning messages for ensuring a timely evacuation response from residents.

  • 25.
    Roy, Chandan
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Tropical cyclone track forecasting techniques: A review2012In: Atmospheric research, ISSN 0169-8095, E-ISSN 1873-2895, Vol. 104-105, p. 40-69Article, review/survey (Refereed)
    Abstract [en]

    Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entail that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earths rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest. © 2011 Elsevier B.V.

  • 26. Roy, Chandan
    et al.
    Kovordanyi, Rita
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CSELAB - Cognitive Systems Engineering Laboratory.
    Ahmed, Raquib
    Gumos, Alexander
    Sivertun, Åke
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, GIS - Geographical Information Science Group.
    Cyclone Tracking and Forecasting in Bangladesh Using Satellite Images without Supplementary Data2006In: NordGIS 2006,2006, 2006Conference paper (Refereed)
    Abstract [en]

    The cost of Bangladesh is extremely exposed to tropical cyclones because of its long costal line containing shallow deltas and densely populated offshore islands. This entails that tropical cyclones will have a particularly severe impact causing high fatality mostly among the poor people on the costal region. In spite of regular attack of devastating cyclones in Bangladesh coast, it-s impact on human lives as well as infrastructure and natural resources have been reduced considerably. The damage reduction reached at a level which is now difficult to down further because of two reasons. First is the insufficient information for early preparedness and second is the ineffective after-cyclone damage restoration system. It is therefore especially important in this part of the world that cyclones can be predicted well in advance before landfall. Normally, cyclone tracking and forecasting is based on satellite images supplemented with data collected using floating buoys, naval ships, dropsondes, airplanes carrying a wide range of active and passive sensors. In Bangladesh however, there is a lack of supplementary data. Meteorologists in Bangladesh are therefore forced to rely on satellite images only (NOAA-AVHRR). The aim of this project is to assess the informational contents of satellite images and identify their limitations in forecasting tropical cyclones. A first step in the project is to assess to what extent current models used for forecasting around the world are dependent on supplementary data, and on the other hand to what extent they could be used to predict tropical cyclones using satellite images only.

  • 27.
    Roy, Chandan
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Kovordányi, Rita
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Tropical Cyclone Track Forecasting2018In: Exploring Natural Hazards: A Case Study Approach / [ed] Darius Bartlett; Ramesh P. Singh, Taylor & Francis, 2018Chapter in book (Refereed)
    Abstract [en]

    Tropical cyclones are large-scale low-pressure systems that form over warm tropical and subtropical waters. These low-pressure systems are characterized by high-speed surface wind circulation, rotating spirals of thick clouds, heavy rain, and surges, the water masses sometimes reaching a height of 10 meters when they hit a coastline. Tropical cyclones are one of the most destructive meteorological disasters due to their high damaging power, both through strong winds and flooding. To minimize economic loss and to save human lives, meteorologists have developed a range of techniques for forecasting tropical cyclone track. The most common techniques utilize statistical and mathematical equations to integrate the movement pattern of historical tropical cyclones with the recently observed movement of the current tropical cyclone. Alternatively, forecasting techniques can focus on the forces responsible for tropical cyclone motion to produce a cyclone track forecast. Today, improved cyclone track forecasting techniques have enabled meteorological offices to warn residents in the affected areas before a tropical cyclone impact, and help to reduce the losses created by them.

  • 28.
    Saifullah, Mohammad
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Kovordanyi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Roy, Chandan
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Bidirectional Hierarchical Neural Networks: Hebbian Learning Improves Generalization2010In: Proceedings of the Fifth International Conference on Computer Vision Theory and Applications,  Volume 1, 2010, p. 105-111Conference paper (Other academic)
    Abstract [en]

    Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfactorily instandard three-layer feed-forward artificial neural networks. For this reason, an increasing number ofresearchers are using networks whose architecture resembles the human visual system. These biologicallybasednetworks are bidirectionally connected, use receptive fields, and have a hierarchical structure, withthe input layer being the largest layer, and consecutive layers getting increasingly smaller. These networksare large and complex, and therefore run a risk of getting overfitted during learning, especially if smalltraining sets are used, and if the input patterns are noisy. Many data sets, such as, for example, handwrittencharacters, are intrinsically noisy. The problem of overfitting is aggravated by the tendency of error-drivenlearning in large networks to treat all variations in the noisy input as significant. However, there is one wayto balance off this tendency to overfit, and that is to use a mixture of learning algorithms. In this study, weran systematic tests on handwritten character recognition, where we compared generalization performanceusing a mixture of Hebbian learning and error-driven learning with generalization performance using pureerror-driven learning. Our results indicate that injecting even a small amount of Hebbian learning, 0.01 %,significantly improves the generalization performance of the network.

  • 29.
    Saifullah, Mohammad
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Kovordányi, Rita
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network2011In: Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I / [ed] Andrej Dobnikar, Uroš Lotrič, Branko Šter, Springer Berlin/Heidelberg, 2011, p. 200-209Chapter in book (Refereed)
    Abstract [en]

    We present a computational model for visual processing where attentional focus emerges fundamental mechanisms inherent to human vision. Through detailed analysis of activation development in the network we demonstrate how normal interaction between top-down and bottom-up processing and intrinsic mutual competition within processing units can give rise to attentional focus. The model includes both spatial and object-based attention, which are computed simultaneously, and can mutually reinforce each other. We show how a non-salient location and a corresponding non-salient feature set that are at first weakly activated by visual input can be reinforced by top-down feedback signals (centrally controlled attention), and instigate a change in attentional focus to the weak object. One application of this model is highlight a task-relevant object in a cluttered visual environment, even when this object is nonsalient (non-conspicuous).

1 - 29 of 29
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