Person:

Vachtsevanos, George J.

Permanent Link

https://hdl.handle.net/1853/72208

Associated Organization(s)

Organizational Unit

School of Electrical and Computer Engineering

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Now showing 1 - 10 of 18

Markov Modeling of Component Fault Growth Over A Derived Domain of Feasible Output Control Effort Modifications

(Georgia Institute of Technology, 2012-08) Bole, Brian ; Goebel, Kai ; Vachtsevanos, George J.

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system’s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

(Georgia Institute of Technology, 2012-06-19) Bole, Brian ; Goebel, Kai ; Vachtsevanos, George J.

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.
Evolution of seizure precursors in refractory epilepsy

(Georgia Institute of Technology, 2007-09-28) Vachtsevanos, George J.
Voice and gesture recognition experiments

(Georgia Institute of Technology, 2007-07-02) Vachtsevanos, George J.
Multi-agent prognostics health and usage monitoring

(Georgia Institute of Technology, 2007-04-14) Vachtsevanos, George J.
Prediction of epileptic services

(Georgia Institute of Technology, 2006-09-30) Vachtsevanos, George J. ; Burrell, Lauren S.
Risk stratification for cardiac arrhythmia patients

(Georgia Institute of Technology, 2006-09) Vachtsevanos, George J.
Air force satellite control network

(Georgia Institute of Technology, 2003) O'Neill, Gary S. ; Vachtsevanos, George J. ; Rufus, Freeman, Jr. ; Abbott, Freeland
An Intelligent Approach to Prediction of Epileptic Seizures

(Georgia Institute of Technology, 2003) Vachtsevanos, George J.
A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications

(Georgia Institute of Technology, 2002) Ding, Yuhua ; Vachtsevanos, George J. ; Yezzi, Anthony ; Zhang, Yingchuan ; Wardi, Yorai Y.

Segmentation accuracy is shown to be a critical factor in detection rate improvement. With accurate segmentation, results are easier to interpret, and classification performance is better. Therefore, it is required to have a performance measure for segmentation evaluation. However, a number of restrictions limit using existing segmentation performance measures. In this paper a recursive segmentation and classification scheme is proposed to improve segmentation accuracy and classification performance in real-time machine vision applications. In this scheme, the confidence level of classification results is used as a new performance measure to evaluate the accuracy of segmentation algorithm. Segmentation is repeated until a classification with desired confidence level is achieved. This scheme can be implemented automatically. Experimental results show that it is efficient to improve segmentation accuracy and the overall detection performance, especially for real-time machine vision applications, where the scene is complicated and a single segmentation algorithm cannot produce satisfactory results.