Control of the induction soldering on the basis of process temperature indirect measurements

The article suggests the use of indirect measurements to control the process of induction soldering. Based on the equations of thermodynamics, it is proposed a scheme for approximating the temperature values in the solder zone on the basis of information from pyrometric sensors aimed at an area remote from the soldered joint site. A model-algorithmic instrument for indirect temperature measurements in the soldering technological process is developed, the scheme of which is presented in the article. The software of waveguide paths induction soldering control in the form of an already existing system module has been developed, which allows not only to carry out experimental studies on control algorithms, but also to implement a full-scale experiment, the results of which confirm the effectiveness of the proposed approach

Control of the induction soldering on the basis of process temperature indirect measurements

The article suggests the use of indirect measurements to control the process of induction soldering. Based on the equations of thermodynamics, it is proposed a scheme for approximating the temperature values in the solder zone on the basis of information from pyrometric sensors aimed at an area remote from the soldered joint site. A model-algorithmic instrument for indirect temperature measurements in the soldering technological process is developed, the scheme of which is presented in the article. The software of waveguide paths induction soldering control in the form of an already existing system module has been developed, which allows not only to carry out experimental studies on control algorithms, but also to implement a full-scale experiment, the results of which confirm the effectiveness of the proposed approach

A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability

Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting

Classification of Acoustic Influences Registered with Phase-Sensitive OTDR Using Pattern Recognition Methods

This article is devoted to the development of a classification method based on an artificial neural network architecture to solve the problem of recognizing the sources of acoustic influences recorded by a phase-sensitive OTDR. At the initial stage of signal processing, we propose the use of a band-pass filter to collect data sets with an increased signal-to-noise ratio. When solving the classification problem, we study three widely used convolutional neural network architectures: AlexNet, ResNet50, and DenseNet169. As a result of computational experiments, it is shown that the AlexNet and DenseNet169 architectures can obtain accuracies above 90%. In addition, we propose a novel CNN architecture based on AlexNet, which obtains the best results; in particular, its accuracy is above 98%. The advantages of the proposed model include low power consumption (400 mW) and high speed (0.032 s per net evaluation). In further studies, in order to increase the accuracy, reliability, and data invariance, the use of new algorithms for the filtering and extraction of acoustic signals recorded by a phase-sensitive reflectometer will be considered

Prediction of Critical Filling of a Storage Area Network by Machine Learning Methods

The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essential tool for ensuring the smooth operation of such systems. The aim of this study is to develop a system of the modelling and simulation of the further loading of SAN on previously observed load measurements. The system is based on machine learning applied to the load prediction problem. Its novelty relates to the method used for forming input attributes to solve the machine learning problem. The proposed method is based on the aggregation of data on observed loading measurements and the formalization of the problem in the form of a regression analysis problem. The artificial dataset, synthesized stochastically according to the given parameter intervals and simulating SAN behavior, allowed for more extensive experimentation. The most effective algorithm is CatBoost (gradient boosting on decision trees), which surpasses other regression analysis algorithms in terms of R2 scores and MAE. The selection of the most significant features allows for the simplification of the prediction model with virtually no loss of accuracy, thereby reducing the number of confessions used. The experiments show that the proposed prediction model is adequate to the situation under consideration and allows for the prediction of the SAN load for the planning period under review with an R2 value greater than 0.9. The model has been validated on a series of real data on SAN

The Orb-Weaving Spider Algorithm for Training of Recurrent Neural Networks

The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm

The Orb-Weaving Spider Algorithm for Training of Recurrent Neural Networks

The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm

Experimental Study of Oil Non-Condensable Gas Pyrolysis in a Stirred-Tank Reactor for Catalysis of Hydrogen and Hydrogen-Containing Mixtures Production

The present study is focused on improving the technology for deep oil sludge processing by pyrolysis methods, considered to be the most promising technology for their environmentally friendly utilization, in which a significant yield of fuel products is expected. The technology developed by the authors of this study is a two-stage process. The first stage, pyrolysis of oil sludge, was investigated in previous papers. A significant yield of non-condensable gases was obtained. This paper presents a study of the second stage of complex deep processing technology—pyrolysis of non-condensable gases (purified propane) using a stirrer with the help of the developed experimental setup. The expected benefit of using the stirrer is improved heat transfer due to circumferential and radial-axial circulation of the gas flow. The effect of a stirrer on the yield of final target decomposition products—H2-containing mixtures and H2 generated during non-catalytic (medium-temperature) and catalytic pyrolysis of non-condensable gases obtained by pyrolysis of oil sludge are estimated. Ni catalyst was used for catalytic pyrolysis. The study shows that the application of the stirrer leads to increasing in H2-containing mixtures and H2 concentrations. In particular, during the whole reaction time (10 h), the average H2 concentration in pyrolysis gas during catalytic pyrolysis increased by ~5.3%. In this case, the optimum reaction time to produce H2 was 4 h. The peak H2 concentration in the pyrolysis gas at reaction temperature 590 ± 10 °C was: 66.5 vol. % with the stirrer versus 62 vol. % without the stirrer with an error of ±0.4 %. A further increase in reaction time is cost-effective in order to obtain H2-containing mixtures

Experimental Study of Oil Non-Condensable Gas Pyrolysis in a Stirred-Tank Reactor for Catalysis of Hydrogen and Hydrogen-Containing Mixtures Production

The present study is focused on improving the technology for deep oil sludge processing by pyrolysis methods, considered to be the most promising technology for their environmentally friendly utilization, in which a significant yield of fuel products is expected. The technology developed by the authors of this study is a two-stage process. The first stage, pyrolysis of oil sludge, was investigated in previous papers. A significant yield of non-condensable gases was obtained. This paper presents a study of the second stage of complex deep processing technology—pyrolysis of non-condensable gases (purified propane) using a stirrer with the help of the developed experimental setup. The expected benefit of using the stirrer is improved heat transfer due to circumferential and radial-axial circulation of the gas flow. The effect of a stirrer on the yield of final target decomposition products—H2-containing mixtures and H2 generated during non-catalytic (medium-temperature) and catalytic pyrolysis of non-condensable gases obtained by pyrolysis of oil sludge are estimated. Ni catalyst was used for catalytic pyrolysis. The study shows that the application of the stirrer leads to increasing in H2-containing mixtures and H2 concentrations. In particular, during the whole reaction time (10 h), the average H2 concentration in pyrolysis gas during catalytic pyrolysis increased by ~5.3%. In this case, the optimum reaction time to produce H2 was 4 h. The peak H2 concentration in the pyrolysis gas at reaction temperature 590 ± 10 °C was: 66.5 vol. % with the stirrer versus 62 vol. % without the stirrer with an error of ±0.4 %. A further increase in reaction time is cost-effective in order to obtain H2-containing mixtures