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1

Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Elkateb Sherien, Métwalli Ahmed, Shendy Abdelrahman, Moussa Karim, Abu-Elanien Ahmed E. B.

Fibres & Textiles in Eastern Europe

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2023

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Vol. 31, no. 4

46--52

EN

Recently, Industry 4.0 introduced a breakthrough in the textile industry to meet customer demands. This study aimed to accurately estimate the production rate of a knitting machine through an online monitoring system using the Internet of Things (IoT) and machine learning (ML) concepts. Experimentally, a double knitting machine was attached with sensors for gathering data of the machine speed, yarn feeder speed and stitch length while other production variables remained constant. Two prediction models were introduced since correlation results revealed multicollinearity issues among the parameters measured. The second model achieved a prediction accuracy of 100 %. Thus, it presents a novel formula of production calculation.

2

Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms

Puchala Dariusz, Stokfiszewski Kamil, Wieloch Kamil

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 1

art. no. e139393

EN

Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.

3

Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites

Fan Chengkai, Zhang Na, Jiang Bei, Liu Wei Victor

Archives of Mining Sciences

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2022

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Vol. 67, no. 4

661--680

EN

The historical datasets at operating mine sites are usually large. Directly applying large datasets to build prediction models may lead to inaccurate results. To overcome the real-world challenges, this study aimed to handle these large datasets using Gaussian mixture modelling (GMM) for developing a novel and accurate prediction model of truck productivity. A large dataset of truck haulage collected at operating mine sites was clustered by GMM into three latent classes before the prediction model was built. The labels of these latent classes generated a latent variable. Two multiple linear regression (MLR) models were then constructed, including the ordinary-MLR (O-MLR) and the hybrid GMM-MLR models. The GMM-MLR model incorporated the observed input variables and a latent variable in the form of interaction terms. The O-MLR model was the baseline model and did not involve the latent variable. The GMM-MLR model performed considerably better than the O-MLR model in predicting truck productivity. The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity). The haul distance was the most crucial input variable in the GMM-MLR model. This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.

4

Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task

Trostianchyn A.M., Izonin I.V., Duriagina Z.A., Tkachenko R.O., Kulyk V.V., Havrysh B.M.

Archives of Materials Science and Engineering

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2022

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Vol. 116, nr 2

71--80

EN

Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.

5

Bankruptcy models : verifying their validity as a predictor of corporate failure

Kovacova M., Kliestik T., Kubala P., Valaskova K., Radišić M., Borocki J.

Polish Journal of Management Studies

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2018

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Vol. 18, No. 1

167--179

EN

Although the issue of corporate failure analysis is a hot topic for business research since the last century, even nowadays there are numerous researches focusing on assessing the financial health of companies. Within increasing internationalization and globalization the demand for bankruptcy prediction is important not only for owners of the companies, but also for other interested groups. We aim to test the validity of prediction models developed as partial results of our research project. Bankruptcy prediction models were constructed on the data set of Slovak companies covering the year 2015 and based on the various statistical methodologies. We provided the validity of these models and their prediction accuracy on the data set of Slovak companies covering the following year 2016.

PL

Chociaż kwestia analizy niepowodzenia korporacyjnego jest gorącym tematem badań biznesowych od zeszłego wieku, nawet obecnie prowadzone są liczne badania skupiające się na ocenie kondycji finansowej firm. W warunkach rosnącej internacjonalizacji i globalizacji zapotrzebowanie na prognozy bankructwa jest ważne nie tylko dla właścicieli firm, ale także dla innych zainteresowanych grup. Celem artykułu jest sprawdzenie ważności modeli prognostycznych opracowanych jako częściowe wyniki projektu badawczego przez autorów. Modele przewidywania bankructwa zostały zbudowane na zbiorze danych słowackich firm w roku 2015 na podstawie różnych metodologii statystycznych. Zapewniona została poprawność tych modeli i dokładność ich prognozowania na zbiorze danych słowackich firm obejmująca rok 2016.

6

Studies on additive properties of some processing operations

Malewski J.

Mining Science

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2013

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Vol. 20

57--69

EN

The accuracy of prediction of mineral processing operation by partial effects summation is analyzed. Methodological and practical aspects of the problem are discussed using laboratory studies of selected comminution and classifying operations as examples. Laboratory experiments show that the effects of examined mineral processing operation depend on simultaneously running processes of classification and comminution. The influence rate of interaction between both processes on the final results is significant, but in some cases it may be neglected. The obtained results have preliminary character and needed a further verification.

PL

W artykule przedstawiono zagadnienie nieaddytywności procesów przeróbczych, jakie występuje w realnych procesach i które powinno być brane pod uwagę w przypadku konstruowania matematycznych modeli efektywności operacji uwzględniających wpływ składu ziarnowego. W warunkach laboratoryjnych wykonano testy i pomiary wydajności i składu ziarnowego na sześciu różnych maszynach: kruszarkach, młynach, przesiewaczu wibracyjnym i klasyfikatorze powietrznym. Badania pokazały, że w przypadku wydajności maszyn rozdrabiających i sprawności przesiewania modele tych operacji powinny uwzględniać nieaddytywność procesu.. Natomiast różnice (dP) w składzie ziarnowym produktów rozdrabiania były niewielkie i nie przekraczały trzech procent.