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Shake Table Data Visualization

Shake Table Data Visualization. Amit Chourasia Visualization Scientist Visualization Services San Diego Supercomputer Center Presented at : NEES 5 th Annual meeting, Jun 20 th Snowbird UTAH. Data Source. Observed: Sensors (600 channels) Data Attributes Time varying

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Shake Table Data Visualization

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  1. Shake Table Data Visualization Amit Chourasia Visualization Scientist Visualization Services San Diego Supercomputer Center Presented at : NEES 5th Annual meeting, Jun 20th Snowbird UTAH

  2. Data Source Observed: Sensors (600 channels) Data Attributes Time varying Multivariate Heterogeneous Original Footage

  3. Study Goal • Integrate the observed data into visual domain • Asses the requirements and applicability to Structural Engineering • Demonstrate such a system to Structural Engineers • Disseminate information about the state-of-the-art research in structural engineering

  4. Motivation: Understanding Data Large amount of numbers don’t make much sense to humans Visual information can encode large amount of numbers to gain insight High Bandwidth Low Bandwidth

  5. Quest for Visual Representation Desirable Attributes Intuitive (trainable) Highlights feature of interest Color: Perceptual or Rainbow style? Compact Unambiguous

  6. Dirty Data Issues Data comprised of 117 channels 5000+ timesteps @ 50 hz ~ 100 sec Data Formating and Translation

  7. 3D Model Movie: 3d Model

  8. Deformation Model Movie: Deformation

  9. Renderings Original video Combined Perspective npr

  10. Camera Match Match Check Match

  11. Lessons Learned • The visual system should include the capability of real-time interaction with deformation of a textured 3D model; incorporate contextual elements when possible; represent sensor locations and properties • Animation packages can be successfully utilized for scientific visualization. They are flexible and extensible for quick prototyping. The visual results are highly realistic with high fidelity. • Proper registration of data and metadata is important. Without registration, features like camera matching and compositing can be guesswork at best. • Matching environmental light is still a challenge. • The visualized results are valuable tools for dissemination of information and suitable for both a broader scientific and non-scientific community.

  12. Visualization Issues • Domain knowledge • Multivariate data representation • Temporal coherence • Precision Loss (compression, etc…) • Interaction vs. batch • Perceptual Issues • Personal Bias (author & viewer)

  13. Video Recap

  14. Acknowledgements • Steve Cutchin • Mike Rossmasler(Viz Intern) • Ruben Soto-Alcauter(NEES-IT Intern) • Andrew Collier (NEES-IT Intern) • Jose Restrepo • Marios Panagiotou • Lelli Van Den Einde • Anke Kamrath Study was funded by SDSC

  15. Thanks for your patience? -  Web: http://visservices.sdsc.edu/ Reference:Chourasia, A. (2007) “Digital Recreation of a Seven Story Building Shake during an Earthquake”, ACM Crossroads, 13-3, 2007

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