scholarly journals Analysis of Water, Ethanol, and Fructose Mixtures Using Nondestructive Resonant Spectroscopy of Mechanical Vibrations and a Grouping Genetic Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2695
Author(s):  
Pilar García Díaz ◽  
Juan Martínez Rojas ◽  
Manuel Utrilla Manso ◽  
Leticia Monasterio Expósito
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Resonant Cavity ◽  
Alcoholic Beverage ◽  
Liquid Mixtures ◽  
Absorption Bands ◽  
Average Classification Accuracy ◽  
Grouping Genetic Algorithm ◽  
Learning Machine ◽  
Non Destructive

A new haptic sensor that is based on vibration produced by mechanical excitation from a clock coupled to a resonant cavity is presented. This sensor is intended to determine the chemical composition of liquid mixtures in a completely non-destructive method. In this case, a set of 23 samples of water, ethanol, and fructose mixtures has been used to simulate different kinds of alcoholic beverage. The spectral information from the vibrational absorption bands of liquid samples is analyzed by a Grouping Genetic Algorithm. An Extreme Learning Machine implements the fitness function that is able to classify the mixtures according to the concentration of ethanol and fructose. The 23 samples range from 0%–13% by volume of ethanol and from 0–3 g/L of fructose, all of them with different concentration. The new technique achieves an average classification accuracy of 96%.

scholarly journals Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms

2021 ◽  
Vol 11 (16) ◽  
pp. 7301
Author(s):  
Pilar García Díaz ◽  
Manuel Utrilla Manso ◽  
Jesús Alpuente Hermosilla ◽  
Juan A. Martínez Rojas
Keyword(s):  
Acoustic Analysis ◽  
Fitness Function ◽  
Audio Engineering ◽  
Ultrasonic Sound ◽  
Grouping Genetic Algorithm ◽  
Audible Sound ◽  
Biomimetic Approach ◽  
Learning Machine ◽  
Optimal Waveforms ◽  
Non Destructive

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.

scholarly journals A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4907
Author(s):  
Ke Zhang ◽  
Zhi Hu ◽  
Yufei Zhan ◽  
Xiaofen Wang ◽  
Keyi Guo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Smart Grid ◽  
Extreme Learning Machine ◽  
Detection Rate ◽  
Fitness Function ◽  
Test Error ◽  
Detection Model ◽  
Learning Machine ◽  
Hidden Layer

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment

2019 ◽  
Vol 23 (2) ◽  
pp. 797-836 ◽  
Author(s):  
Seyedeh Yasaman Rashida ◽  
Masoud Sabaei ◽  
Mohammad Mehdi Ebadzadeh ◽  
Amir Masoud Rahmani
Keyword(s):  
Genetic Algorithm ◽  
Cloud Environment ◽  
Vm Placement ◽  
Grouping Genetic Algorithm ◽  
Cost Efficient ◽  
Multi Cloud

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

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