Engineering Commons^™

Computational Geometry And Artifical Neural Networks: A Hybrid Approach To Optimal Sensor Placement For Aerospace Nde, Roberto A. Osegueda, Carlos M. Ferregut, Mary J. George, Jose M. Gutierrez, Vladik Kreinovich

Departmental Technical Reports (CS)

The ideal design of an airplane should include built-in sensors that are pre-blended in the perfect aerodynamic shape. Each built-in sensor is expensive to blend in and requires continuous maintenance and data processing, so we would like to use as few sensors as possible. The ideal formulation of the corresponding optimization problem is, e.g., to minimize the average detection error for fault locations. However, there are two obstacles to this ideal formulation:

--First, this ideal formulation requires that we know the probabilities of different fault locations etc., and there are usually not enough statistics to determine these probabilities.

--Second, even …

Sensor Placement For Aerospace Non-Destructive Evaluation (Nde): Optimization Under Fuzzy Uncertainty, Roberto A. Osegueda, Carlos M. Ferregut, Mary J. George, Jose M. Gutierrez, Vladik Kreinovich

Departmental Technical Reports (CS)

No abstract provided.

A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Despite relentless efforts on developing new approaches, there are still large gaps between schedules generated through various planning systems, and schedules actually used in the shop floor environment. An effective schedule generation is a knowledge intensive activity requiring a comprehensive model of a factory and its environment at all times. There are four main difficulties that need to be addressed. First, job shop scheduling belongs to a class of NP-hard problems. Second, it is a highly constrained problem that changes from shop to shop. Third, scheduling decisions depend upon other decisions which are not isolated from other functions. Thus, it …

Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham

Electrical Engineering and Computer Science Faculty Publications

We have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same …

Neural Network Approach To The Determination Of The Geophysical Model Function Of The Ers-1 C-Band Spaceborne Radar Scatterometer, Sami M. Alhumaidi, W. Linwood Jones

Electrical Engineering and Computer Science Faculty Publications

Geophysical Model Functions (GMF) describing the relationship between the scatterometer normalized radar cross section (sigma-0) and useful geophysical parameters such as sea-surface wind vectors, wave heights, and sea- surface temperatures have been undergoing extensive research and development during the last decade. In this study, we investigate the use of two feed-forward neural networks, Multilayer Perceptron and Radial Basis Functions, for developing a useful and accurate representation of the C- band GMF. Collocated radar sigma-0 cells with global wind vector models were used as the database of the study. The resulting well-known biharmonic relationship between the sigma-0 and the relative azimuth …

Comparison Of Three Clustering Algorithms And An Application To Color Image Compression, Jihun Cha, Laurene V. Fausett

Electrical Engineering and Computer Science Faculty Publications

This paper investigates a traditional clustering algorithm (K-means) and two neural networks (SOM and ART-F). The characteristics of each algorithm are illustrated by simulating geometric space data clustering. Then each algorithm is applied to image data sets to compress the size by reducing the number of colors from 256 to 16.