System Identification I

A self-scanned 1024 element photodiode array and minicomputer are used to measure the phase (wavefront) in the interference pattern of an interferometer to lambda/100. The photodiode array samples intensities over a 32 x 32 matrix in the interference pattern as the length of the reference arm is varied piezoelectrically. Using these data the minicomputer synchronously detects the phase at each of the 1024 points by a Fourier series method and displays the wavefront in contour and perspective plot on a storage oscilloscope in less than 1 min (Bruning et al. Paper WE16, OSA Annual Meeting, Oct. 1972). The array of intensities is sampled and averaged many times in a random fashion so that the effects of air turbulence, vibrations, and thermal drifts are minimized. Very significant is the fact that wavefront errors in the interferometer are easily determined and may be automatically subtracted from current or subsequent wavefrots. Various programs supporting the measurement system include software for determining the aperture boundary, sum and difference of wavefronts, removal or insertion of tilt and focus errors, and routines for spatial manipulation of wavefronts. FFT programs transform wavefront data into point spread function and modulus and phase of the optical transfer function of lenses. Display programs plot these functions in contour and perspective. The system has been designed to optimize the collection of data to give higher than usual accuracy in measuring the individual elements and final performance of assembled diffraction limited optical systems, and furthermore, the short loop time of a few minutes makes the system an attractive alternative to constraints imposed by test glasses in the optical shop.

Digital wavefront measuring interferometer for testing optical surfaces and lenses

http://osp.mans.edu.eg/elbeltagi/AI%20Comparison%205EAs.pdf

Comparison among five evolutionary-based optimization algorithms

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

https://ccc.inaoep.mx/~ariel/2013/An%20extensive%20comparative%20study%20of%20cluster%20validity%20indices.pdf

An extensive comparative study of cluster validity indices

A direct stochastic algorithm for global search

Reference IMAC-CONF-2008-007Afficher la publication dans Web of Science Notice creee le 2008-03-06, modifiee le 2016-12-23

A Bounded Index for Cluster Validity

Clustering is one of the most well known types of unsupervised learning Evaluating the quality of results and determining the number of clusters in data is an important issue Most current validity indices only cover a subset of important aspects of clusters Moreover, these indices are relevant only for data sets containing at least two clusters In this paper, a new bounded index for cluster validity, called the score function (SF), is introduced The score function is based on standard cluster properties Several artificial and real-life data sets are used to evaluate the performance of the score function The score function is tested against four existing validity indices The index proposed in this paper is found to be always as good or better than these indices in the case of hyperspheroidal clusters It is shown to work well on multi-dimensional data sets and is able to accommodate unique and sub-cluster cases

/pdf/a-bounded-index-for-cluster-validity-oxpxrfyo86.pdf

Keywords: Theory of structures ; modele statique ; static model ; encorbellement ; deformation ; analyse des structures ; balanced cantilever construction ; deformation Note: Malden, USA Reference EPFL-ARTICLE-111699 URL: http://is-beton.epfl.ch//Publications/2005/Robert_Nicoud05.pdf Record created on 2007-09-12, modified on 2016-08-08

/pdf/model-identification-of-bridges-using-measurement-data-3c76b1zrcn.pdf

Model Identification of Bridges Using Measurement Data

System identification methodologies are useful for identifying characteristics of structural systems using measurement data. However, incorrect systems might be identified when many combinations of system characteristics result in the same predicted responses at measured locations. The reliability of identification is affected by a number of factors that most previous work has overlooked. This paper presents a system identification methodology that explicitly treats factors that affect the success of identification. Rather than simply determining parametric values, this methodology also involves identification of model characteristics including boundary conditions. Due to inevitable modeling errors, models that provide absolute minimum differences between predictions and measurements are rarely correct models. In such situations, the challenge is to define a population of candidate models that result in such differences being below threshold values that are determined by the magnitude of modeling errors. The methodology is illustrated using a case study in civil engineering. This work contributes to providing engineers with general strategies to meet interpretation challenges associated with sensor data.

Benny Raphael

Papers

A direct stochastic algorithm for global search

A Bounded Index for Cluster Validity

A Bounded Index for Cluster Validity

Model Identification of Bridges Using Measurement Data

System Identification through Model Composition and Stochastic Search