Study on the Law of Short Fatigue Crack Using Genetic Algorithm-BP Neural Networks
The initiation and propagation of short crack is a complex process of nonlinear dynamics. The material field has been concerning of the law of short crack and put forward a number of models and predictive equations. But most of them have to meet the problems which are unclear physical parameters meaning, narrow range of applications etc. That is the reason why there is not yet widely recognized quantitative model of crack evolution so far. The introduction of genetic algorithm-BP neural networks could solve those problems by avoiding building the explicit model equation and directly extracting the potential rules from the data. In this paper, the short crack for low cycle is studied under complex stress at high temperature. The material adopted in the experiment is Q245R steel. The initiation, propagation and coalescence of short crack are observed. The comparisons between experiment results and neural network simulation results show that genetic algorithm-BP neural networks simulation method can predict the law of short crack with higher prediction accuracy.
Prediction of phosphate concentrate grade based on artificial neural network modeling
Abstract In order to determine the grade of phosphate concentrate rapidly and accurately on-line, first, the surface color parameters of primary ore, concentrated and tailing of phosphate rock was collected and extracted based on a self-designing surface color acquisition device. Then these parameters were modeled by artificial neural network. The results showed that the 5-12-1 BP model established by artificial neural network could achieve better prediction results with error less than 5%, which provided theoretical support for realizing the on-line soft measurement of flotation concentrate grade.
Intentional poisoning cases of animals with anticholinesterase pesticide-carbofuran in Sri Lanka.
Carbofuran is a broad spectrum insecticide and nematicide which inhibits acetyl cholinesterase. Several intentional poisoning cases of animals and birds including crows, dogs, cow, and elephant, using carbofuran were reported in Sri Lanka. Qualitative analysis of carbofuran in biological specimens was carried out using T.L.C and GC-MS. The quantitative analysis was carried out by HPLC using Zorbax Eclips XDB-C18 (150 x 4.6 mm I.D x 5 microm particle size) column with acetonitrile: water 25:75 v/v mobile phase and UV detection at 210 nm. The liquid-liquid extraction with chloroform was reproducible and sensitive. The procedure was validated in terms of linearity (0.996<r<0.999) for concentrations ranging from 1 to 200 microg/ml, repeatability (RSD 4.4% at 100 microg/ml, n=10) extraction recovery (91.7% at 100 microg/g, 77.3% at 20 microg/g), limit of detection (S/N ratio=3, 0.7 microg/ml) and limit of quantification (S/N ratio=10, 2.4 microg/ml). The method was found satisfactory and successfully applied in fatal cases of animals and birds due to carbofuran poisoning. The fatal carbofuran levels detected were in the range of 42-910 microg/g in crow's gizzard and contents, 50-800 microg/g in dog's stomach contents, 13-20 microg/g in dog's liver, 0.9 microg/g in cow's stomach and contents, 35 microg/g in elephant's stomach and contents.