Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms
| Autor: | Manuel Utrilla Manso, Pilar García Díaz, Juan Antonio Martínez Rojas, Jesús Alpuente Hermosilla |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Anechoic chamber Computer science QH301-705.5 Acoustics QC1-999 Feature extraction automatic classification Classifier (linguistics) Genetic algorithm Range (statistics) Waveform non-destructive analysis General Materials Science Biology (General) Instrumentation QD1-999 acoustic chemical analysis Fluid Flow and Transfer Processes Fitness function Process Chemistry and Technology feature extraction Physics General Engineering Engineering (General). Civil engineering (General) Computer Science Applications Chemistry Ultrasonic sensor TA1-2040 |
| Zdroj: | Applied Sciences, Vol 11, Iss 7301, p 7301 (2021) Applied Sciences Volume 11 Issue 16 |
| ISSN: | 2076-3417 |
| Popis: | Acoustic analysis of materials is a common non-destructive technique, but most efforts are focused on the ultrasonic range. In the audible range, such studies are generally devoted to audio engineering applications. Ultrasonic sound has evident advantages, but also severe limitations, like penetration depth and the use of coupling gels. We propose a biomimetic approach in the audible range to overcome some of these limitations. A total of 364 samples of water and fructose solutions with 28 concentrations between 0 g/L and 9 g/L have been analyzed inside an anechoic chamber using audible sound configurations. The spectral information from the scattered sound is used to identify and discriminate the concentration with the help of an improved grouping genetic algorithm that extracts a set of frequencies as a classifier. The fitness function of the optimization algorithm implements an extreme learning machine. The classifier obtained with this new technique is composed only by nine frequencies in the (3–15) kHz range. The results have been obtained over 20,000 independent random iterations, achieving an average classification accuracy of 98.65% for concentrations with a difference of ±0.01 g/L. |
| Databáze: | OpenAIRE |
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