1. University of Illinois at Urbana-Champaign PET Program Year-End Review Wednesday, August 4, 1999 William H. Hsu, Loretta Auvil, Tom Redman, Michael Welge Michael Bach, Peter L. Johnson, Mike Perry, Kristopher Wuollett Automated Learning Group National Center for Supercomputing Applications http://www.ncsa.uiuc.edu/STI/ALG Neural, Bayesian, and Evolutionary Systems for High-Performance Computational Knowledge Management: Demonstrations

2. University of Illinois at Urbana-Champaign PET Program Year-End Review Overview: Tools for Dealing with Multisensor T&E Data Short-Term Objectives: Building a Data Model –Progress to date: data channel typing for ontology –Current work: CGI form for data channel grouping, selection –Future work: integrity-checking, data preparation modules Longer-Term Objectives –Multimodal Sensor Integration: multiple models in data fusion itinerary –Relevance Determination: genetic algorithm wrapper (current work) –Causal (Explanatory) Models: Bayesian network based on ontology Test Bed: Super ADOCS Data Format (SDF) –1719-channel asynchronous data bus (General Dynamics) –Experiment/Data Design –Typing: interactive tool for constructing data model –Specification of prediction target based on caution/warning channels –Interactive specification tool for learning architectures, algorithms –Target end users: test/instrumentation report designers, implementors –Analytical Applications: Decision Support

3. University of Illinois at Urbana-Champaign PET Program Year-End Review Super ADOCS Data Format (SDF) Data Conversion and Selection Interface CGI (Perl-based) form: Apache, MS Internet Explorer 5

4. University of Illinois at Urbana-Champaign PET Program Year-End Review Application Testbed –Aberdeen Test Center: M1 Abrams main battle tank (SEP data, SDF) –Generic Data Model (Facility for Experiment Specification) T&E Information Systems: Common Characteristics –Large-Scale Data Model Objective: develop system capable of reducing model complexity Methodology: build a relational (taxonomic, definitional) model of data –Data Integrity Requirements Interactive form-based specification of test objective Specification of error metrics, visualization criteria –Multimodality Selection of relevant data channels Interactive, support for automation –Data Reduction Requirements Non-uniform downsampling - requires database of engineering units Irrelevant data channels - requires type hierarchy An Ontology for T&E Data

5. University of Illinois at Urbana-Champaign PET Program Year-End Review SDF Ontology: Data Channel Types Caution/WarningFuel Systems Spatial/GPS/ Navigation Data Bus/Control/ Diagnostics Electrical ProfilometerTiming HydraulicsBallisticsUnused

6. University of Illinois at Urbana-Champaign PET Program Year-End Review Intranet Operating Environment Database Access –SDF import, flat file export –Internal data model: interaction with learning modules –Future development: SQL/Oracle 8 (JDBC) interface Deployment –CGI, JavaScript stand-alone applications –Management of modules, data flow through forms Presentation: Web-Based Interface –Simple, HTML-based invocation system Common Gateway Interface (CGI) and Perl Alternative implementation: servlets (http://www.javasoft.com) –Configuration of data model (file generation) –Management of experiments Construction of models Specification of learning systems (model architecture, training algorithm) Messaging Systems (Deployment  Presentation)

7. University of Illinois at Urbana-Champaign PET Program Year-End Review Super ADOCS Data Format (SDF) Experiment Design Interface D2K Genetic “Wrapper” for Data Channel Selection

8. University of Illinois at Urbana-Champaign PET Program Year-End Review Visible Decisions Inc. (VDI) In3D Time Series Analysis and Visualization: System Prototype

9. University of Illinois at Urbana-Champaign PET Program Year-End Review Summary and Conclusion Model Identification –Queries: test/instrumentation reports –Specification of data model –Grouping of data channels by type Prediction Objective Identification –Specification of test objective –Identification of metrics Reduction –Refinement of data model –Selection of relevant data channels (given prediction objective) Synthesis: New Data Channels Integration: Multiple Time Series Data Sources Environment (Data Model) Learning Element Knowledge Base Time Series Analysis/Prediction