University of Illinois at Urbana-ChampaignPET Program Year-End Review

Wednesday, August 4, 1999

William H. Hsu, Loretta Auvil, Tom Redman, Michael WelgeMichael Bach, Peter L. Johnson, Mike Perry, Kristopher Wuollett

Automated Learning Group

National Center for Supercomputing Applicationshttp://www.ncsa.uiuc.edu/STI/ALG

Neural, Bayesian, and Evolutionary Systemsfor High-Performance

Computational Knowledge Management:Demonstrations

University of Illinois at Urbana-ChampaignPET Program Year-End Review

Overview: Tools for DealingOverview: Tools for Dealingwith Multisensor T&E Datawith 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

University of Illinois at Urbana-ChampaignPET Program Year-End Review

Super ADOCS Data Format (SDF)Super ADOCS Data Format (SDF)Data Conversion and Selection InterfaceData Conversion and Selection Interface

• CGI (Perl-based) form: Apache, MS Internet Explorer 5

University of Illinois at Urbana-ChampaignPET 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 DataAn Ontology for T&E Data

University of Illinois at Urbana-ChampaignPET Program Year-End Review

SDF Ontology:SDF Ontology:Data Channel TypesData Channel Types

Caution/Warning Fuel SystemsSpatial/GPS/Navigation

Data Bus/Control/Diagnostics

Electrical

Profilometer Timing

Hydraulics Ballistics Unused

University of Illinois at Urbana-ChampaignPET Program Year-End Review

Intranet Operating EnvironmentIntranet 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)

University of Illinois at Urbana-ChampaignPET Program Year-End Review

Super ADOCS Data Format (SDF)Super ADOCS Data Format (SDF)Experiment Design InterfaceExperiment Design Interface

• D2K Genetic “Wrapper” for Data Channel Selection

University of Illinois at Urbana-ChampaignPET Program Year-End Review

• Visible Decisions Inc. (VDI) In3D

Time Series Analysis and Visualization:Time Series Analysis and Visualization:System PrototypeSystem Prototype

University of Illinois at Urbana-ChampaignPET Program Year-End Review

Summary and ConclusionSummary and Conclusion

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ObjectiveDetermination

Data Preparation MachineLearning

Analysis &Assimilation

Eff

ort

(%

)

• 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)

LearningElement

KnowledgeBase

Time SeriesAnalysis/Prediction