4,602 research outputs found
Data modelling for emergency response
Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud
There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud
This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)
Web-based Geographical Visualization of Container Itineraries
Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair
Capacities and Gap analysis
Analysis of the capacities and gaps of the present Atlantic Ocean Observing Syste
Report of Working Group 26 on Jellyfish Blooms around the North Pacific Rim: Causes and Consequences
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
On Small Satellites for Oceanography: A Survey
The recent explosive growth of small satellite operations driven primarily
from an academic or pedagogical need, has demonstrated the viability of
commercial-off-the-shelf technologies in space. They have also leveraged and
shown the need for development of compatible sensors primarily aimed for Earth
observation tasks including monitoring terrestrial domains, communications and
engineering tests. However, one domain that these platforms have not yet made
substantial inroads into, is in the ocean sciences. Remote sensing has long
been within the repertoire of tools for oceanographers to study dynamic large
scale physical phenomena, such as gyres and fronts, bio-geochemical process
transport, primary productivity and process studies in the coastal ocean. We
argue that the time has come for micro and nano satellites (with mass smaller
than 100 kg and 2 to 3 year development times) designed, built, tested and
flown by academic departments, for coordinated observations with robotic assets
in situ. We do so primarily by surveying SmallSat missions oriented towards
ocean observations in the recent past, and in doing so, we update the current
knowledge about what is feasible in the rapidly evolving field of platforms and
sensors for this domain. We conclude by proposing a set of candidate ocean
observing missions with an emphasis on radar-based observations, with a focus
on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table
A comprehensive ship weather routing system using CMEMS products and A* algorithm
We describe the implementation of a comprehensive software for Ship Weather Routing referred to as SIM- ROUTE. The A* pathfinding algorithm is used to optimize the sailing route as a function of the wave action. The aim of the software is to provide a comprehensive, open and easy tool including pre- and post-processing for ship weather routing simulations. The software is constructed considering the Copernicus Marine Environment Monitoring Service (CMEMS) wave predictions systems which are available for free use. The code provides the optimized route and the minimum distance route together with additional modules to compute ship emission and safety on navigation monitoring. SIMROUTE has been tested in several cases using different CMEMS products over short and long distances. The comprehensive structure of the code enables it to be easily modified to include additional ship wave resistance models and the effect of the water currents and winds on navigation. SIMROUTE is also used for academic purposes, providing skills for ship routing optimization in the framework of standards of training, certification and watchkeeping (STCW) for competence-based maritime education and training. Due to the simplicity of its use, SIMROUTE is a good candidate for benchmarking strategies and inter-comparison exercises with advanced methods for ship weather routing. This contribution highlights the technical aspects, code organization and structure behind SIMROUTE, demonstrating its capabilities through examples of route optimization.Postprint (published version
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