Alexander Gabriel, Babette Tecklenburg, Yann Guillouet, & Frank Sill Torres. (2021). Threat analysis of offshore wind farms by Bayesian networks – a new modeling approach. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 174–185). Blacksburg, VA (USA): Virginia Tech.
Abstract: As a result of the ongoing commitment to climate protection in more and more countries and the corresponding expansion of renewable energies, the importance of renewables for the security of electricity supply is also increasing. Wind energy generated in offshore wind farms already accounts for a significant share of the energy mix and will continue to grow in the future. Therefore, approaches and models for security assessment and protection against threats are also needed for these infrastructures. Due to the special characteristics and geographical location of offshore wind farms, they are confronted with particular challenges. In this context, this contribution outlines how an approach for threat analysis of offshore wind farms is to be developed within the framework of the new research project “ARROWS” of the German Aerospace Center. The authors first explain the structure of offshore wind farms and then present a possible modeling approach using Qualitative function models and Bayesian networks.
|
|
Don J.M. Willems, & Louis Vuurpijl. (2007). Designing interactive maps for crisis management. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 159–166). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes the design, implementation, and evaluation of pen input recognition systems that are suited for so-called interactive maps. Such systems provide the possibility to enter handwriting, drawings, sketches and other modes of pen input. Typically, interactive maps are used to annotate objects or mark situations that are depicted on the display of video walls, handhelds, PDAs, or tablet PCs. Our research explores the possibility of employing interactive maps for crisis management systems, which require robust and effective communication of, e.g., the location of objects, the kind of incidents, or the indication of route alternatives. The design process described here is a mix of “best practices” for building perceptive systems, combining research in pattern recognition, human factors, and human-computer interaction. Using this approach, comprising data collection and annotation, feature extraction, and the design of domain-specific recognition technology, a decrease in error rates is achieved from 9.3% to 4.0%.
|
|
Dragos Datcu, & Leon J.M. Rothkrantz. (2008). A Dialog Action Manager for automatic crisis management. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 384–393). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents the results of our research on the development of a Dialog Action Manager-DAM as part of a complex crisis management system. Imagine the utility of such an automatic system to detect the crisis and to provide support to people in contexts similar to what happened recently at the underground in London and Madrid. Preventing and handling the scenarios of terrorism and other crisis are nowadays maybe the most important issues for a modern and safe society. In order to automate the crisis support, DAM simulates the behavior of an employee at the crisis centre handling telephone calls from human observers. Firstly, the system has to mimic the natural support for the paradigm 'do you hear me?' and next for the paradigm 'do you understand me?'. The people witnessing the crisis event as well as human experts provide reports and expertise according to their observations and knowledge on the crisis. The system knowledge and the data communication follow the XML format specifications. The system is centered on the results of our previous work on creating a user-centered multimodal reporting tool that works on mobile devices. In our paper we describe the mechanisms for creating an automatic DAM system that is able to analyze the user messages, to identify and track the crisis contexts, to support dialogs for crisis information disambiguation and to generate feedback in the form of advice to the users. The reasoning is done by using a data frame and rule based system architecture and an alternative Bayesian Network approach. In the paper we also present a series of experiments we have attempted in our endeavor to correctly identify natural solutions for the crisis situations.
|
|
Olof Görnerup, Per Kreuger, & Daniel Gillblad. (2013). Autonomous accident monitoring using cellular network data. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 638–646). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.
|
|
Leon J.M. Rothkrantz, & Zhenke Yang. (2009). Crowd control by multiple cameras. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: One of the goals of the crowd control project at Delft University of Technology is to detect and track people during a crisis event, classify their behavior and assess what is happening. The assumption is that the crisis area is observed by multiple cameras (fixed or mobile). The cameras sense the environment and extract features such as the amount of motion. These features are the input to a Bayesian network with nodes corresponding to situations such as terroristic attack, fire, and explosion. Given the probabilities of the observed features, by reasoning, the likelihood of the possible situations can be computed. A prototype was tested in a train compartment and its environment. Forty scenarios, performed by actors, were recorded. From the recordings the conditional probabilities have been computed. The scenarios are designed as scripts which proved to be a good methodology. The models, experiments and results will be presented in the paper.
|
|