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Vanderbilt University

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2 papers

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AAAI Conference 1999 Short Paper

Data Driven Profiling of Dynamic System Behavior Using Hidden Markov Model Based Combined Unsupervised and Supervised Classification

Cen Li
Vanderbilt University

Dynamic systems are often best characterized by a combination of static and temporal features, with the static features describing time-invariant properties of the system, and the temporal features capturing dynamic aspects of the system. Our goal is to construct context based temporal behavior models of dynamic systems using information from both types of features. Our dynamic system profiling framework consists of three main steps: (i) model generation, (ii) model validation, and (iii) model interpretation. Model generation step can be further decomposed into two components: (ia) temporal model generation, and (ib) context generation.

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AAAI Conference 1999 Conference Paper

Model-Based Support for Mutable Parametric Design Optimization

Ravi Kapadia
Gautam Biswas
Vanderbilt University

Traditional methodsfor parametric design optimization assumethat the relations between performance criteria anddesign variables are known algebraic functions with fixed coefficients. However, the relations maybe mutable, i. e. , the functions and/or coefficients maynot be knownexplicitly because they depend on input parameters and vary in different parts of the design space. Wepresent a model-based reasoning methodologyto support parametric, mutable, design optimization. First, wederive event modelsto represent the effects of the system’s parameterson the material that flowsthroughit. Next, weuse these models to discover mutablerelations betweenthe system’s design variables and its optimization criteria. Wethen present an algorithm that searches for "optimal" designs by employingsensitivity analysis techniques on the derivedrelations.

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