Crack Parameter Characterization by a Neural Network

A neural network with binary outputs is presented to determine the angle and the depth of a surface-breaking crack from ultrasonic backscattering data. The estimation procedure is divided into two steps: (1) The angle of the crack is estimated in the range from 10 to 70 degrees with a precision of 5 degrees. To improve the accuracy of estimation, information on the integral of the backscattered signal is utilized. (2) When the angle of the crack has been estimated, the depth of the crack is determined with a precision of 0.5mm in the range from 2.0mm to 4.0mm. This determination is achieved by employing sets of neural networks corresponding to various angles of the crack

Lamellae Stability in Confined Systems with Gravity

The microphase separation of a diblock copolymer melt confined by hard walls and in the presence of a gravitational field is simulated by means of a cell dynamical system model. It is found that the presence of hard walls normal to the gravitational field are key ingredients to the formation of well ordered lamellae in BCP melts. To this effect the currents in the directions normal and parallel to the field are calculated along the interface of a lamellar domain, showing that the formation of lamellae parallel to the hard boundaries and normal to the field correspond to the stable configuration. Also, it is found thet the field increases the interface width.Comment: 4 pages, 2 figures, submitted to Physical Review

Crack-depth determination by a neural network with a synthetic training data set

A neural network with an analog output is presented for crack-depth estimation from ultrasonic signals backscattered from a surface-breaking crack in a steel plate. The network has only one response unit and this unit directly reports the crack depth from the measured signals. A completely synthetic data set, spot-checked by comparison with experimental results, is utilized for the training of the network. The synthetic data set has been obtained by solving governing boundary integral equations by the boundary element method. A Gaussian modulated sinusoid has been utilized as incident signal. The architecture of the present network, which is a feedforward three-layered network together with an error back- propagation algorithm, has been discussed in Refs. [1,2]

Neural Network for Crack-Depth Determination from Ultrasonic Backscattering Dat

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of a feedforward three-layered network together with a back-propagation algorithm for error corrections[1,2]. The signal used for crack insonification is a mode converted 45° transverse wave. The plate containing a surface breaking crack is immersed in water and the crack is insonified from the opposite uncracked side of the plate. A numerical analysis of the backscattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The computed backscattered field provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on experimental data for cracks of different depths than used for network training

Modern Russian informational technologies of enterprise management. Platform 1C

The history of the development of the 1C program is briefly described. The topic of licensing and product safety was touched upon. Little is said about the corporate line and the new direction of 1C: EnterpriseDevelopmentTools. Also get acquainted with the products of 1C:ManufacturingEnterpriseManagement and 1C:EnterpriseResourcesPlanning

An Artificial Intelligence Technique to Characterizae Surface-Breaking Cracks

A neural network with an analog output is presented to determine the angle of inclination of a surface-breaking crack from ultrasonic backscattering data. A neural network which was trained by the use of synthetic data set to estimate the depth of a crack, assuming that the inclined crack angle is known, was presented earlier[1,2]. In this study, a neural network estimates the angle of inclination of the surface-breaking crack, assuming that the depth of the crack is 2.0mm, by utilizing the waveforms of backscattered signals from the crack. The plate with a surface-breaking crack is immersed in water and the crack is insonified from the opposite side of the plate. The angle of incidence with the normal to the insonified face of the plate is taken to be 18.9°. The neural network is a feed-forward three layered network. The training algorithm is an error back-propagation algorithm which has been discussed in Refs. [3,4]. The theoretical data obtained by the boundary element method are used for the training. The performance of the trained network is tested by synthetic and experimental data

Phase-ordering of conserved vectorial systems with field-dependent mobility

The dynamics of phase-separation in conserved systems with an O(N) continuous symmetry is investigated in the presence of an order parameter dependent mobility M(\phi)=1-a \phi^2. The model is studied analytically in the framework of the large-N approximation and by numerical simulations of the N=2, N=3 and N=4 cases in d=2, for both critical and off-critical quenches. We show the existence of a new universality class for a=1 characterized by a growth law of the typical length L(t) ~ t^{1/z} with dynamical exponent z=6 as opposed to the usual value z=4 which is recovered for a<1.Comment: RevTeX, 8 pages, 13 figures, to be published in Phys. Rev.

Coarsening Dynamics of a One-Dimensional Driven Cahn-Hilliard System

We study the one-dimensional Cahn-Hilliard equation with an additional driving term representing, say, the effect of gravity. We find that the driving field $E$ has an asymmetric effect on the solution for a single stationary domain wall (or `kink'), the direction of the field determining whether the analytic solutions found by Leung [J.Stat.Phys.{\bf 61}, 345 (1990)] are unique. The dynamics of a kink-antikink pair (`bubble') is then studied. The behaviour of a bubble is dependent on the relative sizes of a characteristic length scale $E^{-1}$, where $E$ is the driving field, and the separation, $L$, of the interfaces. For $EL \gg 1$ the velocities of the interfaces are negligible, while in the opposite limit a travelling-wave solution is found with a velocity $v \propto E/L$. For this latter case ($EL \ll 1$) a set of reduced equations, describing the evolution of the domain lengths, is obtained for a system with a large number of interfaces, and implies a characteristic length scale growing as $(Et)^{1/2}$. Numerical results for the domain-size distribution and structure factor confirm this behavior, and show that the system exhibits dynamical scaling from very early times.Comment: 20 pages, revtex, 10 figures, submitted to Phys. Rev.

Phase Separation Kinetics in a Model with Order-Parameter Dependent Mobility

We present extensive results from 2-dimensional simulations of phase separation kinetics in a model with order-parameter dependent mobility. We find that the time-dependent structure factor exhibits dynamical scaling and the scaling function is numerically indistinguishable from that for the Cahn-Hilliard (CH) equation, even in the limit where surface diffusion is the mechanism for domain growth. This supports the view that the scaling form of the structure factor is "universal" and leads us to question the conventional wisdom that an accurate representation of the scaled structure factor for the CH equation can only be obtained from a theory which correctly models bulk diffusion.Comment: To appear in PRE, figures available on reques

Робочий зошит до конспекту лекцій з дисциплін “Основи побудови мікропроцесорних систем керування”, “Мікропроцесорна техніка”, “Програмні засоби систем керування” для студентів спеціальностей АГ – 8.092501 Автоматизоване управління технологічними процесами; АТ,ME – 8.091401 Системи управління й автоматики; СМ – 8.091501 Комп`ютерні системи та мережі

Дані методичні вказівки призначуються для вивчення апаратних та програмних засобів мікропроцесорних систем управління (МПС). У процесі вивчення дисципліни "Мікропроцесорні системи" студенти вивчають структуру, архітектуру, сигнали та системи команд однокристального мікроконтролера К1816 ВЕ51. Розглянуті питання організації паралельного та послідовного вводу- виводу, оганізація мікропроцесорних контролерів МПК). Схемні рішення, які приведені у методичних вказівках можуть бути використані при виконанні курсових і дипломних проектів студентами спеціальностей "Комп’ютеризовані системи управління і автоматики" (АТ) і "Автоматизація технологічних процесів гірничих підприємств" (АГ) та "Комп’ютерні системи та мережі" (СМ)