GIS-based intelligent system for infectious disease disaster response
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Number of Parts | 266 | |
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License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
Identifiers | 10.5446/66427 (DOI) | |
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00:00
UML
00:14
WordData managementPresentation of a groupOrder (biology)Computer animation
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InformationDependent and independent variablesResultantPartial derivativeMathematical analysis
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Projective planeMusical ensembleComputer animation
01:14
Continuous functionMusical ensembleModule (mathematics)
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Gateway (telecommunications)Module (mathematics)Water vaporResultantGateway (telecommunications)Program flowchart
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Module (mathematics)Decision support systemComputing platformInformation
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Texture mappingDisintegrationData managementSatelliteConditional-access moduleModule (mathematics)
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Overlay-NetzInformationPoint (geometry)Electronic visual displayPoint (geometry)Electronic visual displayImplementationComputer animation
02:50
Data managementMereologyComputer animation
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Computer animation
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Electronic mailing listLevel (video gaming)
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InformationInformationTable (information)Module (mathematics)Computer animation
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Level (video gaming)Selectivity (electronic)InformationComputer animation
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LogicTable (information)Computer animation
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Texture mappingGeometryWater vaporTable (information)InformationGreen's functionConcentricFood energyComputer animation
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InformationComputer animation
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Musical ensembleSatelliteExtension (kinesiology)CASE <Informatik>Dependent and independent variablesDenial-of-service attackTable (information)Computing platformEstimatorAreaComputer animation
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Linear mapInterpolationSatelliteTwitterCartesian coordinate systemMedical imagingComputer animation
Transcript: English(auto-generated)
00:08
Hello, my name is Min Young Lee from South Korea. Let's begin. The presentation is in the order of background, word description, situation monitoring, and management.
00:25
The background, there are various risks due to water pollution caused by climate change. The purpose is to prepare emergency response methods
00:41
and technique for pathogen pollution through partial information based analysis results. The PESSTOR project is a consortium of 23 partners from Europe and Korea.
01:00
The goal is that quickly detects waterborne pathogens and induce cooperation among various stakeholders in emergencies. There are modules related to images,
01:22
pathoset, CAM, and drone. These are used to continuously monitor water quality. Next are modules for checking and responding to the situation. PathTweet monitors data to filter
01:43
what it deems to be a risk. And PathSense is an IoT gateway measuring the water quality. The monitoring results in various modules mentioned above can be received through Alletra.
02:05
It is a pathoGIS that combines these modules to provide the information. Use the pathoGIS platform to detect water and manage the disaster situation for decision support.
02:24
Each data is collected, processed, and delivered through PESSTORware so that it can be monitored by PESSTORGIS. PESSTORGIS provides the following features.
02:43
Scenario-based implementation and point data display and so on. Situation monitoring and management parts. If you log in to the platform,
03:01
select a scenario from the globe map. You can see the information on the modules can be found in the content table. The information is shown in the data table below.
03:22
Each information is displayed partially on the map and details are displayed when selected. It can filter the data in the table to view it within the desired logic.
03:43
Click the information. The table to see the green energy concentration in the water. You can check the abnormal information
04:03
that occurs in PESSTORSense or PESSTORTEET through the alarm. To pilot, yes. Use cases are designed to test the use of the platform.
04:21
The first is an arts case in Limassol, Cyprus. Flooding occurs in the river and analyze the river through satellite image. Next, estimate the extent of the flood-stricken area.
04:47
Finally, respond to the situation on the table in the region. The second one is, applicants are in the river.
05:01
The river also analyze the, with satellite image. And partially, interpolation analyzes, so we get the water pollution trends. Final step, people respond to the situation by tweet.
05:24
And the end. Thank you.