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 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%.

Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function

2019 ◽  
Vol 57 (12) ◽  
pp. 2673-2682 ◽  
Author(s):  
Kaveri Chatra ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Ajeet Kumar Verma
Keyword(s):  
Extreme Learning Machine ◽  
Fitness Function ◽  
Data Classification ◽  
Cancer Data ◽  
Learning Machine

Detection of Cracks in Crankshaft Using an Intelligent Audible Sound-Based Non-Destructive Method

2021 ◽  
Author(s):  
Kwarne Twum Asamoah Boateng ◽  
Henry Nunoo-Mensah ◽  
Justice Ohene-Akoto ◽  
Prince Ebenezer Adjei ◽  
Kwame Osei Boateng
Keyword(s):  
Audible Sound ◽  
Non Destructive

scholarly journals Design and Validation of Low-Power Secure and Dependable Elliptic Curve Cryptosystem

2021 ◽  
Vol 11 (4) ◽  
pp. 43
Author(s):  
Bikash Poudel ◽  
Arslan Munir ◽  
Joonho Kong ◽  
Muazzam A. Khan
Keyword(s):  
Elliptic Curve ◽  
Acoustic Analysis ◽  
Fitness Function ◽  
Scalar Multiplication ◽  
Fault Analysis ◽  
Boolean Logic ◽  
Side Channel ◽  
Elliptic Curve Cryptosystem ◽  
Non Invasive ◽  
Field Programmable

The elliptic curve cryptosystem (ECC) has been proven to be vulnerable to non-invasive side-channel analysis attacks, such as timing, power, visible light, electromagnetic emanation, and acoustic analysis attacks. In ECC, the scalar multiplication component is considered to be highly susceptible to side-channel attacks (SCAs) because it consumes the most power and leaks the most information. In this work, we design a robust asynchronous circuit for scalar multiplication that is resistant to state-of-the-art timing, power, and fault analysis attacks. We leverage the genetic algorithm with multi-objective fitness function to generate a standard Boolean logic-based combinational circuit for scalar multiplication. We transform this circuit into a multi-threshold dual-spacer dual-rail delay-insensitive logic (MTD3L) circuit. We then design point-addition and point-doubling circuits using the same procedure. Finally, we integrate these components together into a complete secure and dependable ECC processor. We design and validate the ECC processor using Xilinx ISE 14.7 and implement it in a Xilinx Kintex-7 field-programmable gate array (FPGA).

scholarly journals Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Heng-di Wang ◽  
Si-er Deng ◽  
Jian-xi Yang ◽  
Hui Liao ◽  
Wen-bo Li
Keyword(s):  
Fault Diagnosis ◽  
Fitness Function ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Penalty Term ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Learning Machine ◽  
Parameter Adaptive

In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

scholarly journals An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
J. Del Ser ◽  
Z. W. Geem
Keyword(s):  
Fuzzy Clustering ◽  
Fitness Function ◽  
Distance Measures ◽  
Mutation Operators ◽  
Fuzzy Partitioning ◽  
Grouping Genetic Algorithm ◽  
Partitioning Problems ◽  
Overall Performance ◽  
Clustering Problems ◽  
Crossover And Mutation

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.

scholarly journals PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine

2021 ◽  
Vol 9 ◽  
Author(s):  
Bing Li ◽  
Huang Chen ◽  
Tian Tan
Keyword(s):  
Extreme Learning Machine ◽  
Heuristic Algorithm ◽  
Cell Parameter ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Parameter Extraction ◽  
High Accuracy ◽  
Data Prediction ◽  
Pv Cell ◽  
Learning Machine

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction–based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current–voltage (I–V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I–V data; thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

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