Automated digitization of radial charts

Vasilyeva, Natalia; Бойков, А. В.; Erokhina, O. O.; Trifonov, Andrei

doi:10.31897/pmi.2021.1.9

Cited by 35 publications

(27 citation statements)

References 14 publications

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“…Recall is another important metric that is defined as the proportion of samples from a class that are correctly predicted by the model. This metric is the proportion of class instances recognized by the algorithm as related to the total number of instances of the class in the sample [25]. Recall is calculated using Formula (2):…”

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confidence: 99%

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Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

et al. 2021

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The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

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confidence: 99%

“…where X-predicted pixel set, Y-true meaning. The Dice coefficient is primarily a statistical measure used to assess the similarity of two samples: the similarity coefficient [25].…”

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confidence: 99%

Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

et al. 2021

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“…Today’s trend in industrial companies is to improve automation systems in all areas of operation [ 1 , 2 ]. This is primarily due to the digitalization of production [ 3 ].…”

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confidence: 99%

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

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Korolev

Zhukovskiy

et al. 2021

Sensors

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This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

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“…Digital technologies are involved in all spheres of human activity and are a universal trigger for the creation of new approaches to solving ordinary issues [3,4]. Mining industry is not an exception [5,6]. The task of determining the rock fragmentation at the mining enterprises is one of the most important and constantly clarified issues [7,8].…”

Section: Introductionmentioning

confidence: 99%

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

2021

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Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, trommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass.

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Automated digitization of radial charts

Cited by 35 publications

References 14 publications

Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

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