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Hemez, Francois M.

Publications and source records attributed to Hemez, Francois M..

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Video Analysis in Multi-Intelligence

This is a project which was performed by a graduated high school student at Los Alamos National Laboratory (LANL). The goal of the Multi-intelligence (MINT) project is to determine the state of a facility from multiple data streams. The data streams are indirect observations. The researcher is using DARHT (Dual-Axis Radiographic Hydrodynamic Test Facility) as a proof of concept. In summary, videos from the DARHT facility contain a rich amount of information. Distribution of car activity can inform us about the state of the facility. Counting large vehicles shows promise as another feature for identifying the state of operations. Signal processing techniques are limited by the low resolution and compression of the videos. We are working on integrating these features with features obtained from other data streams to contribute to the MINT project. Future work can pursue other observations, such as when the gate is functioning or non-functioning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, and finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.

58 GEOSCIENCES↗
NASA NTRS1993

Updating finite element dynamic models using an element-by-element sensitivity methodology

A sensitivity-based methodology for improving the finite element model of a given structure using test modal data and a few sensors is presented. The proposed method searches for both the location and sources of the mass and stiffness errors and does not interfere with the theory behind the finite element model while correcting these errors. The updating algorithm is derived from the unconstrained minimization of the squared L sub 2 norms of the modal dynamic residuals via an iterative two-step staggered procedure. At each iteration, the measured mode shapes are first expanded assuming that the model is error free, then the model parameters are corrected assuming that the expanded mode shapes are exact. The numerical algorithm is implemented in an element-by-element fashion and is capable of 'zooming' on the detected error locations. Several simulation examples which demonstate the potential of the proposed methodology are discussed.

Farhat, Charbel↗