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Volume 22 (2022): Issue 5 (October 2022)

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Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

Volume 22 (2022): Issue 3 (June 2022)

Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

6 Articles
Open Access

An Integrated Testing Solution for Piezoelectric Sensors and Energy Harvesting Devices

Published Online: 22 Apr 2022
Page range: 100 - 106

Abstract

Abstract

With the fast growth of wireless communications between nodes and sensor units and the increase of devices installed in remote places, and the development of IIoT applications, new requirements for power energy supply are needed to assure device functionality and data communication capabilities during extended periods of time. For these applications, energy harvesting takes place as a good solution to increase the autonomy of remote measuring solutions, since the usage of conventional power supply solutions has clear limitations in terms of equipment access and increased maintenance costs. In this context, regenerative energy sources such as thermoelectric, magnetic and piezoelectric based, as well as renewable energy sources, such as photovoltaic and wind based, among others, make the development of different powering solutions for remote sensing units possible. The main purpose of this paper is to present a flexible testing platform to characterize piezoelectric devices and to evaluate their performance in terms of harvesting energy. The power harvesting solutions are focused on converting the energy from mechanical vibrations, provided by different types of equipment and mechanical structures, to electrical energy. This study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude, frequency and spectral contents of the vibration stimulus. Several experimental results using, as an example, a specific piezoelectric module, are included in the paper.

Keywords

  • Power harvesting
  • piezoelectric sensors
  • vibration exciters
  • accelerometers
  • calibration
  • testing
Open Access

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Published Online: 22 Apr 2022
Page range: 107 - 111

Abstract

Abstract

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.

Keywords

  • Defect inspection
  • metal screw
  • deep learning
  • convolutional neural network
Open Access

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Published Online: 22 Apr 2022
Page range: 112 - 121

Abstract

Abstract

The growing complexity of industrial processes and manufactured parts, the growing need for safety in service and the desire to optimize the life of parts, require the implementation of increasingly complex quality assessments. Among the various anomalies to consider, sub-millimeter surface defects must be the subject of particular care. These defects are extremely dangerous as they are often the starting point for larger defects such as fatigue cracks, which can lead to the destruction of the parts.

Penetrant testing is now widely used for this type of defect, due to its good performance. Nevertheless, it should be abandoned eventually due to environmental standards. Among the possible alternatives, the use of eddy currents (EC) for conductive materials is a reliable, fast, and inexpensive alternative.

The study concerns the design and modeling of eddy current probe structures comprising micro-sensors for non-destructive testing. The moving band finite element method is implemented for this purpose to take into account the movement of the sensor, experimental validations were conducted on a nickel-based alloy specimen. The real and imaginary parts of the impedance at every position of the sensor computed by experiments and simulations were in good agreement. The crack detection quality was quantified and the geometric characteristics of the defects were estimated using RBF NN (Radial Basis Function Neural Networks) that were designed and implemented on the acquired signals.

Keywords

  • Defect inspection
  • Eddy current
  • Finite element method
  • Microsensor
  • RBF
  • Moving band method
  • Neural network
Open Access

Review of Measurement Techniques of Hydrocarbon Flame Equivalence Ratio and Applications of Machine Learning

Published Online: 22 Apr 2022
Page range: 122 - 135

Abstract

Abstract

Flame combustion diagnostics is a technique that uses different methods to diagnose the flame combustion process and study its physical and chemical basis. As one of the most important parameters of the combustion process, the flame equivalence ratio has a significant influence on the entire flame combustion, especially on the combustion efficiency and the emission of pollutants. Therefore, the measurement of the flame equivalence ratio has a huge impact on efficient combustion and environment protection. In view of this, several effective measuring methods were proposed, which were based on the different characteristics of flames radicals such as spectral properties. With the rapid growth of machine learning, more and more scholars applied it in the combustion diagnostics due to the excellent ability to fit parameters. This paper presents a review of various measuring techniques of hydrocarbon flame equivalent ratio and the applications of machine learning in combustion diagnostics, finally making a brief comparison between different measuring methods.

Keywords

  • Combustion diagnostics
  • measuring techniques
  • equivalence ratio
  • multispectral imaging
  • machine learning
Open Access

Complex Analysis of the Necessary Geometric Parameters of the Tested Component in the Ring-Core Evaluation Process

Published Online: 22 Apr 2022
Page range: 136 - 142

Abstract

Abstract

Residual stress measurement in different sorts of mechanical and mechatronic objects has become an important part of the designing process and following maintenance. Therefore, a sufficient experimental method could significantly increase the accuracy and reliability of the evaluation process. Ring-Core method is a well-known semi-destructive method, yet it is still not standardized. This work tries to improve the evaluation process of the Ring-Core method by analyzing the influence of the necessary geometric parameters of the investigated object. Subsequently, residual stress computation accuracy is increased by proposed recommendations.

Keywords

  • Residual stresses
  • Ring-Core method
  • FEM
  • Incremental method
  • Differential method
  • Integral method
Open Access

Development of Modified Blum-Blum-Shub Pseudorandom Sequence Generator and its Use in Education

Published Online: 22 Apr 2022
Page range: 143 - 151

Abstract

Abstract

In information security systems, the algorithm of the Blum-Blum-Shub (BBS) generator, which is based on the use of a one-way function and is a cryptographically secure pseudorandom number generator, became widespread. In this paper, the problem of the analysis of modified algorithms of the BBS generator operation is considered to improve their statistical characteristics, namely, the sequence repetition period. It has been established that in order to improve the characteristics of the classic BBS algorithm, it is necessary to systematize approaches to change the recurrent equation itself, the relationship between the current and the previous members of the sequence. For this purpose, a generalized unified model of the modification of the classical BBS algorithm is derived. The repetition period with computational complexity were analyzed for classical algorithm and 80 proposed modifications. A gain in statistical characteristics is improved with slight increase in the required computing power of the system. The proposed modified BBS pseudorandom sequence generator can be used in training of students when teaching cryptographic stability of information security systems. The study of this generator combines the knowledge of students acquired in both digital electronics and mathematics.

Keywords

  • Pseudorandom sequence
  • pseudorandom sequence generators
  • one-way functions
  • Blum-Blum-Shub generators
  • computational complexity
6 Articles
Open Access

An Integrated Testing Solution for Piezoelectric Sensors and Energy Harvesting Devices

Published Online: 22 Apr 2022
Page range: 100 - 106

Abstract

Abstract

With the fast growth of wireless communications between nodes and sensor units and the increase of devices installed in remote places, and the development of IIoT applications, new requirements for power energy supply are needed to assure device functionality and data communication capabilities during extended periods of time. For these applications, energy harvesting takes place as a good solution to increase the autonomy of remote measuring solutions, since the usage of conventional power supply solutions has clear limitations in terms of equipment access and increased maintenance costs. In this context, regenerative energy sources such as thermoelectric, magnetic and piezoelectric based, as well as renewable energy sources, such as photovoltaic and wind based, among others, make the development of different powering solutions for remote sensing units possible. The main purpose of this paper is to present a flexible testing platform to characterize piezoelectric devices and to evaluate their performance in terms of harvesting energy. The power harvesting solutions are focused on converting the energy from mechanical vibrations, provided by different types of equipment and mechanical structures, to electrical energy. This study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude, frequency and spectral contents of the vibration stimulus. Several experimental results using, as an example, a specific piezoelectric module, are included in the paper.

Keywords

  • Power harvesting
  • piezoelectric sensors
  • vibration exciters
  • accelerometers
  • calibration
  • testing
Open Access

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Published Online: 22 Apr 2022
Page range: 107 - 111

Abstract

Abstract

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.

Keywords

  • Defect inspection
  • metal screw
  • deep learning
  • convolutional neural network
Open Access

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Published Online: 22 Apr 2022
Page range: 112 - 121

Abstract

Abstract

The growing complexity of industrial processes and manufactured parts, the growing need for safety in service and the desire to optimize the life of parts, require the implementation of increasingly complex quality assessments. Among the various anomalies to consider, sub-millimeter surface defects must be the subject of particular care. These defects are extremely dangerous as they are often the starting point for larger defects such as fatigue cracks, which can lead to the destruction of the parts.

Penetrant testing is now widely used for this type of defect, due to its good performance. Nevertheless, it should be abandoned eventually due to environmental standards. Among the possible alternatives, the use of eddy currents (EC) for conductive materials is a reliable, fast, and inexpensive alternative.

The study concerns the design and modeling of eddy current probe structures comprising micro-sensors for non-destructive testing. The moving band finite element method is implemented for this purpose to take into account the movement of the sensor, experimental validations were conducted on a nickel-based alloy specimen. The real and imaginary parts of the impedance at every position of the sensor computed by experiments and simulations were in good agreement. The crack detection quality was quantified and the geometric characteristics of the defects were estimated using RBF NN (Radial Basis Function Neural Networks) that were designed and implemented on the acquired signals.

Keywords

  • Defect inspection
  • Eddy current
  • Finite element method
  • Microsensor
  • RBF
  • Moving band method
  • Neural network
Open Access

Review of Measurement Techniques of Hydrocarbon Flame Equivalence Ratio and Applications of Machine Learning

Published Online: 22 Apr 2022
Page range: 122 - 135

Abstract

Abstract

Flame combustion diagnostics is a technique that uses different methods to diagnose the flame combustion process and study its physical and chemical basis. As one of the most important parameters of the combustion process, the flame equivalence ratio has a significant influence on the entire flame combustion, especially on the combustion efficiency and the emission of pollutants. Therefore, the measurement of the flame equivalence ratio has a huge impact on efficient combustion and environment protection. In view of this, several effective measuring methods were proposed, which were based on the different characteristics of flames radicals such as spectral properties. With the rapid growth of machine learning, more and more scholars applied it in the combustion diagnostics due to the excellent ability to fit parameters. This paper presents a review of various measuring techniques of hydrocarbon flame equivalent ratio and the applications of machine learning in combustion diagnostics, finally making a brief comparison between different measuring methods.

Keywords

  • Combustion diagnostics
  • measuring techniques
  • equivalence ratio
  • multispectral imaging
  • machine learning
Open Access

Complex Analysis of the Necessary Geometric Parameters of the Tested Component in the Ring-Core Evaluation Process

Published Online: 22 Apr 2022
Page range: 136 - 142

Abstract

Abstract

Residual stress measurement in different sorts of mechanical and mechatronic objects has become an important part of the designing process and following maintenance. Therefore, a sufficient experimental method could significantly increase the accuracy and reliability of the evaluation process. Ring-Core method is a well-known semi-destructive method, yet it is still not standardized. This work tries to improve the evaluation process of the Ring-Core method by analyzing the influence of the necessary geometric parameters of the investigated object. Subsequently, residual stress computation accuracy is increased by proposed recommendations.

Keywords

  • Residual stresses
  • Ring-Core method
  • FEM
  • Incremental method
  • Differential method
  • Integral method
Open Access

Development of Modified Blum-Blum-Shub Pseudorandom Sequence Generator and its Use in Education

Published Online: 22 Apr 2022
Page range: 143 - 151

Abstract

Abstract

In information security systems, the algorithm of the Blum-Blum-Shub (BBS) generator, which is based on the use of a one-way function and is a cryptographically secure pseudorandom number generator, became widespread. In this paper, the problem of the analysis of modified algorithms of the BBS generator operation is considered to improve their statistical characteristics, namely, the sequence repetition period. It has been established that in order to improve the characteristics of the classic BBS algorithm, it is necessary to systematize approaches to change the recurrent equation itself, the relationship between the current and the previous members of the sequence. For this purpose, a generalized unified model of the modification of the classical BBS algorithm is derived. The repetition period with computational complexity were analyzed for classical algorithm and 80 proposed modifications. A gain in statistical characteristics is improved with slight increase in the required computing power of the system. The proposed modified BBS pseudorandom sequence generator can be used in training of students when teaching cryptographic stability of information security systems. The study of this generator combines the knowledge of students acquired in both digital electronics and mathematics.

Keywords

  • Pseudorandom sequence
  • pseudorandom sequence generators
  • one-way functions
  • Blum-Blum-Shub generators
  • computational complexity

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