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This paper delves into a significant area of research, exploring the application of the standard boundary element method (BEM) to analyze inverse acoustic frequency scattering problems in 2D space using a rigid corrugated circular object. The inverse problem is reformulated as an optimization problem, with the boundary of the scatterer parametrized to reduce the number of optimization variables. The influence of these parameters on imaging results based on near-field data is examined. The analysis uses a flat wave illuminating the object along the positive x-axis direction. The study evaluates the accuracy of the solution across various parameters defining the boundary of the analyzed object. The findings, which significantly contribute to advancements in computational methods, non-destructive testing, and the understanding of functional properties of materials and structures, offer valuable insights into numerical techniques and their practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin refill friction stir spot welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. The optimized SVM model, employing the radial basis function (RBF) kernel, achieved a lower root mean square error of 257.9 and a high correlation coefficient of 0.95, indicating a strong linear relationship between the predicted and actual shear load capacities. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model's loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. [ABSTRACT FROM AUTHOR]

Aim of the study was to evaluate the effect of supplementation of high gravity media with mineral compounds and myo-inositol, at concentration which would be obtained as a result of degradation of phytates present in raw material during alcoholic fermentation. The process of alcoholic fermentation was conducted under laboratory conditions in a 72 h system at 37°C with the use of S. cerevisiae D-2 strain. Calcium chloride proved to be the most effective of all supplements tested. Final ethanol concentration increased by 1.2% v v−1 and the yield of process increased by ca. 7 dm−3 ethanol 100 kg−1 of starch in comparison with control. Selective supplementation with KH2PO4, ZnSO4 and MgSO4 also increased the ethanol concentration, but the effect was accompanied by a deterioration in composition of volatile products. The hydrolysis of phytate complexes with microbial phytases can be an alternative solution to supplementation of HG mashes presented in this work.

The main problem with any tomography is the transformation of measurements into images. It is the so-called "inverse problem", which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality. [ABSTRACT FROM AUTHOR]

This study concerns research on using electrical impedance tomography (EIT) to image moisture inside the porous walls of buildings. In order to transform the electrical measurements into the values of the reconstructed 3D images, a neural network containing the LSTM layer was used. The objective of the study was to evaluate the impact of various loss functions on the efficacy of a neural network's learning process. During the training process, three distinct variations of the loss function were employed, namely mean squared error (MSE), Huber, and a hybrid of MSE + Huber, to attain the desired outcome. Given that the primary focus of the study was on the loss function, the particular neural network architecture employed was deemed non-essential. In order to minimize the influence of the neural network architecture on the outcomes of the test, a comparatively uncomplicated neural model was implemented, comprising a solitary LSTM layer and a single fully connected layer. [ABSTRACT FROM AUTHOR]

The mechanisms by which yeast cells respond to environmental stress include the production of heat shock proteins (HSPs) and the reduction of oxidative stress. The response of yeast exposed to aflatoxins B2+G1 (AFB2+G1), ochratoxin A (OTA), and zearalenone (ZEA) in aerobic conditions was studied. After 72 h of yeast cultivation in media contaminated with mycotoxins, the growth of yeast biomass, the level of malondialdehyde, and the activity of superoxide dismutase, glutathione S-transferase and glutathione peroxidase were examined; the expression profile of the following heat shock proteins was also determined: HSP31, HSP40, HSP60, HSP70, and HSP104. It was demonstrated that at the tested concentrations, both AFB2+G1 and ZEA inhibited yeast biomass growth. OTA at a concentration of 8.4 [µg/L] raised the MDA level. Intensified lipoperoxidation and increased activity of SOD and GPx were observed, regardless of the level of contamination with ZEA (300 µg/L or 900 µg/L). Increased contamination with AFB2+G1 and OTA caused an increase in the production of most HSPs tested (HSP31, HSP40, HSP70, HSP104). ZEA contamination in the used concentration ranges reduced the production of HSP31. The response of yeast cells to the presence of mycotoxin as a stressor resulted in the expression of certain HSPs, but the response was not systematic, which was manifested in different profiles of protein expression depending on the mycotoxin used. The tested mycotoxins influenced the induction of oxidative stress in yeast cells to varying degrees, which resulted in the activation of mainly SOD without GST mobilization or with a small involvement of GPx. [ABSTRACT FROM AUTHOR]

This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.

The following paper presents a key role and potential of Industrial Internet of Things (IIoT) in industrial applications as a solution for monitoring and maintaining manufacturing assets. IIoT is particularly important due to progressing computerisation of hardware resources leading to development of a virtualised model of autonomous real-time production management. Adequately article presents case study of IIoT use in production environment – both methodical and analytic approach is presented.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents four selected methods of supervised machine learning, which can be successfully used in the tomography of flood embankments, walls, tanks, reactors and pipes. A comparison of the following methods was made: Artificial Neural Networks (ANN), Supported Vector Machine (SVM), K-Nearest Neighbour (KNN) and Multivariate Adaptive Regression Splines (MAR Splines). All analysed methods concerned regression problems. Thanks to performed analysis the differences expressed quantitatively were visualized with the use of indicators such as regression, error of mean square deviation, etc. Moreover, an innovative method of denoising tomographic output images with the use of convolutional auto-encoders was presented. Thanks to the use of a convolutional structure composed of two auto-encoders, a significant improvement in the quality of the output image from the ECT tomography was achieved.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wet foundations and walls of buildings significantly increase the energy consumption of buildings, and the drying of walls is one of the priority activities as part of thermal modernization, along with the insulation of the facades. This article discusses the research findings of detecting moisture decomposition within building walls utilizing electrical impedance tomography (EIT) and deep learning techniques. In particular, the focus was on algorithmic models whose task is transforming voltage measurements into spatial EIT images. Two homogeneous deep learning networks were used: CNN (Convolutional Neural Network) and LSTM (Long-Short Term Memory). In addition, a new heterogeneous (hybrid) network was built with LSTM and CNN layers. Based on the reference reconstructions' simulation data, three separate neural network algorithmic models: CNN, LSTM, and the hybrid model (CNN+LSTM), were trained. Then, based on popular measures such as mean square error or correlation coefficient, the quality of the models was assessed with the reference images. The obtained research results showed that hybrid deep neural networks have great potential for solving the tomographic inverse problem. Furthermore, it has been proven that the proper joining of CNN and LSTM layers can improve the effect of EIT reconstructions. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart buildings are becoming a new standard in construction, which allows for many possibilities to introduce ergonomics and energy savings. These contain simple improvements, such as controlling lights and optimizing heating or air conditioning systems in the building, but also more complex ones, such as indoor movement tracking of building users. One of the necessary components is an indoor localization system, especially without any device worn by the person being located. These types of solutions are important in locating people inside smart buildings, managing hospitals of the future and other similar institutions. The article presents a prototype of an innovative energy-efficient device for radio tomography, in which the hardware and software layers of the solution are presented. The presented example consists of 32 radio sensors based on a Bluetooth 5 protocol controlled by a central unit. The preciseness of the system was verified both visually and quantitatively by the image reconstruction as a result of solving the inverse tomographic problem using three neural networks. [ABSTRACT FROM AUTHOR]

Effective project management has contributed to successful operations and process management. The goal of this article is to look at the link between a project's success (PS) and the amount of intellectual capital (IC) an organization has. Instead of being reactive to measuring the cost, timeliness, and quality (customer requirements), a more predictive indicator of a project's success is needed. Nearly 300 people who work in the field of digital (information and communication) technology took part in the survey research. The survey contains 88 questions. Several statistical techniques are utilized for the data analysis. Based on the comprehensive surveys, the findings show the strong possibility for IC to be adapted as a predictor of the success of investment projects, especially for digital upgrade and improvement. IC plays a key role in assuring the effective (and successful) project management. The study highlights the impacts of effective project management on industrial and organizational operations. This highlight is based on the attempt to determine whether IC contributes to a PS. In this study, in addition to the three traditional factors of cost, timeliness, and quality (or requirements), IC should be considered as a prediction for the project management's success. The survey was addressed to selected companies from the ICT industry (IT projects). The sample selection is based on non-probability sampling. The author's method of converting the respondents' answers into binary form was adopted. [ABSTRACT FROM AUTHOR]

One of the key elements influencing the efficiency of cellulosic ethanol production is the effective pretreatment of lignocellulosic biomass. The aim of the study was to evaluate the effect of microwave-assisted pretreatment of wheat stillage in the presence of sodium cumene sulphonate (NaCS) hydrotrope used for the production of second-generation bioethanol. As a result of microwave pretreatment, the composition of the wheat stillage biomass changed significantly when compared with the raw material used, before treatment. Microwave-assisted pretreatment with NaCS effectively reduced the lignin content and hemicellulose, making cellulose the dominant component of biomass, which accounted for 42.91 ± 0.10%. In post pretreatment, changes in biomass composition were also visible on FTIR spectra. The peaks of functional groups and bonds characteristic of lignins (C–O vibration in the syringyl ring, asymmetric bending in CH3, and aromatic skeleton C–C stretching) decreased. The pretreatment of the analyzed lignocellulosic raw material with NaCS resulted in the complete conversion of glucose to ethanol after 48 h of the process, with yield (in relation to the theoretical one) of above 91%. The highest observed concentration of ethanol, 23.57 ± 0.10 g/L, indicated the high effectiveness of the method used for the pretreatment of wheat stillage that did not require additional nutrient supplementation. [ABSTRACT FROM AUTHOR]

According to the article, locating moisture within the walls of buildings using electrical impedance tomography is discussed in detail. The algorithmic approach, whose role is to convert the input measurements into images, received excellent attention during the development process. Numerous models have been trained to generate tomographic images based on individual pixels in a given image based on machine learning methods. An array of categorisation data was then generated, which enabled the development of a classification model to solve the problem of optimal model selection for a given point on the screen. It was achieved in this manner by developing a pixel-oriented ensemble model (POE), the goal of which is to provide tomographic reconstructions of at least the same quality as homogeneous algorithmic approaches. Artificial neural networks (ANN), linear regression (LR), and the long short-term memory network (LSTM) were employed in the current research to get homogeneous machine learning results. An image reconstruction algorithm such as the ANN or the LR reconstructs the image pixel by pixel, which means that a different prediction model is trained for each image pixel. In the case of LSTM, a single network is responsible for creating the entire image. Then, using the POE algorithm, the best reconstruction method was fitted to each pixel of the output image while considering the measurement scenario provided to the program. As a result, each measurement consequences in a unique assignment of reconstructive procedures to individual pixels, which is different for each measurement. It is the capacity to maximise the selection of a prediction model while considering both a given pixel and a specific measurement vector that distinguishes the provided POE concept from other approaches. [ABSTRACT FROM AUTHOR]

The research aimed to develop an optimal way of using known machine learning techniques in electrical impedance tomography (EIT) of flood embankments. The innovative approach is based on the smart use of many machine learning techniques to allow the optimal selection of one of these techniques for each pixel of the tomographic image. An additional advantage of the presented concept is that selecting the optimal method for each pixel depends on the measurement set of a given case. This fact makes the method flexible and enables the automation of dyke monitoring using cyber-physical systems. Several machine learning methods were used during the research, including Elastic Net, Support Vector Machine, and Artificial Neural Networks. The comparison of the new concept with popular methods showed that thanks to pixel-oriented ensemble learning, the reconstructions obtained with the new approach are much better than those obtained with typical machine learning methods. [ABSTRACT FROM AUTHOR]

This paper refers to a new resilient cyber-physical machine learning-based system that enables the generation of high-resolution tomographic images. The research object was a model of a tank filled with tap water. Using electrical impedance tomography (EIT) with 16 electrodes, the possibility of identifying inclusions inside the reservoir was investigated. A two-stage hybrid approach was proposed. In the first stage, three independent models were trained for the Elastic Net, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) methods. In the second stage, a k-Nearest Neighbors (kNN) classification model was trained, that optimizes tomographic reconstructions by selecting the best method for each pixel, taking into account the specificity of a given measurement vector. Research has shown that applying the new concept results in a higher reconstruction quality than other methods used singly. It should be emphasized that our research is not intended to develop a new homogenous machine learning method. Instead, the goal is to invent an innovative, original, and flexible way to simultaneously use multiple machine learning methods for image optimization in industrial electrical impedance tomography. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)