Artificial Intelligence Platform Proposal for Paint Structure Quality Prediction within the Industry 4.0 Concept

Kebísek, Michal; Tanuška, Pavol; Spendla, Lukas; Kotianová, Janette; Střelec, Peter

doi:10.1016/j.ifacol.2020.12.299

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Stepwise regression analysis (SWR) was conducted in order to determine the relative contributions of independent variables and to create a prediction model for the DW of WT and WC [35]. Equation ( 3) was used to generate the SWR model using the same data as the ANN model.…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Dry Weight Prediction of Wedelia trilobata and Wedelia chinensis by Using Artificial Neural Network and MultipleLinear Regression Models

Abdolzadeh

Mai

Chen

et al. 2023

Water

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In China, Wedelia trilobata (WT) is among the top most invasive plant species. The prediction of its growth, using different efficient methods under different environmental conditions, is the optimal objective of ecological research. For this purpose, Wedelia trilobata and its native plant species Wedelia chinensis (WC) were grown in mixed cultures under different levels of submergence and eutrophication. The multiple linear regression (MLR) and artificial neural network (ANN) models were constructed, with different morphological traits as the input in order to predict dry weight as the output for both plant species. Correlation and stepwise regression analysis (SWR) were used to find the best input variables for the ANN and MLR models. Plant height, number of nodes, chlorophyll content, leaf nitrogen, number of leaves, photosynthesis, and stomatal conductance were the input variables for WC. The same variables were used for WT, with the addition of root length. A network with the Levenberg–Marquart learning algorithm, back propagation training algorithm, Sigmoid Axon transfer function, and one hidden layer, with four and six neurons for WC and WT, respectively, was created. The best ANN model for WC (7-4-1) has a coefficient of determination (R2) of 0.98, root mean square error (RMSE) of 0.003, and mean absolute error (MAE) of 0.001. On the other hand, the ANN model for WT (8-6-1) has R2 0.98, RMSE 0.018, and MAE 0.004. According to errors and coefficient of determination values, the ANN model was more accurate than the MLR one. According to the sensitivity analysis, plant height and number of nodes are the most important variables that support WT and WC growth under submergence and eutrophication conditions. This study provides us with a new method to control invasive plant species’ spread in different habitats.

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Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Dry Weight Prediction of Wedelia trilobata and Wedelia chinensis by Using Artificial Neural Network and MultipleLinear Regression Models

Abdolzadeh

Mai

Chen

et al. 2023

Water

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“…Calabrese et al [ 17 ] emphasized that the adoption of Industry 4.0 technologies can be seen worldwide, with robots and cybersecurity among the technologies most often reported in scientific papers. Kebisek et al [ 18 ] indicated the necessity to incorporate technologies like Big Data and analytics, when a company wants to adapt for Industry 4.0 concepts, so that enough information can be collected to observe and analyze the ongoing manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Recent Development of Air Gauging in Industry 4.0 Context

Rucki

2023

Sensors

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The paper presents a review of the research reports published in 2012–2022, dedicated to air gauging. Since most of the results are somehow related to Industry 4.0 concept, the review put the air gauging to the context of fourth industrial revolution. It was found that despite substantial decrease of the number of published papers in recent years, the investigations are still performed to improve air gauges, both in static and in non-steady states. Researchers paid attention to the digitization of the results, models and simulations, uncertainty estimation, calibration, and linearization. Specific applications covered real-time monitoring and in-process control, as well as form and surface topography measurements. Proposed solutions for integration with computer systems seem suitable for the air gauges be included to the sensor networks built according to the Industry 4.0 concept.

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