Cluster analysis methods for recognition of mineral rocks in the mining industry

Baklanova, Olga E.; Shvets, Olga

doi:10.1109/ipta.2014.7001972

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Thus, the hybrid information system that has been developed allows timely solving of complex problems of controlling heating networks operation, and regional heat stations in particular, without involving supercomputers but using a computer cluster developed on the basis of available technical support at JSC "Heating Networks". Also, this approach can be used in image processing of minerals [24,25].…”

Section: Resultsmentioning

confidence: 99%

The Development of Hybrid IP Architecture for Solving the Problems of Heating Networks

Baklanov¹,

Baklanova²,

Grigoryeva³

et al. 2020

ACTA POLYTECH HUNG

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The paper covers organisation of pipelined processing of data received from heat stations. New software architecture was developed for processing data received from the heating network. Development of a new analytical information system based on pipeline data processing has allowed increased efficiency of work in heat supply systems. The mechanisms for data storage have been established, as well as work sequence and monitoring of heating networks. This architecture is based on a parallel-pipeline data processing system. The idea of such an approach of data processing was transferred from the system of organization of work of the central processor of a personal computer with processes and streams. A distinctive feature of our system is the ability to work with different databases. It can be adapted to various modern systems of data storage and communication in heating systems. The timely operational monitoring described in the article made it possible to change the modes of operation of heat points and the central heat point in real time, which affects the increase in the reduction of heat consumption. The real reduction made was approximately 9 percent. Hopefully, this allows that with the implementation of this approach at the industrial level heat gain and, respectively, energy will increase even more.

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Section: Resultsmentioning

confidence: 99%

The Development of Hybrid IP Architecture for Solving the Problems of Heating Networks

Baklanov¹,

Baklanova²,

Grigoryeva³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

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“…The metric model is simple and scalable but the classification features need to be highly distinguishable. Baklanova et al (2014) [51] classified the dataset into categories based on similarity through the clustering analysis of the K-means algorithm used for mineral identification, which is calculated by a distance, such as the Euclidean distance.…”

Section: Algorithm Pros Consmentioning

confidence: 99%

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

Long

Zhou

Hancke

et al. 2022

JSAN

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Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.

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“…A system is proposed in [26] to learn to break rocks with a rock hammer using Deep Double Deep-Q Networks (DDDQNs). In [27], they analyze different clustering algorithms to recognize minerals in rocks. In this study, they explore various color spaces and conclude that these are useful, especially the HSV space, for segmenting rocks using the k-means algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

Rodriguez-Guillen,

Kern,

Urrea

2024

Applied Sciences

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Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.

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Cluster analysis methods for recognition of mineral rocks in the mining industry

Cited by 7 publications

References 4 publications

The Development of Hybrid IP Architecture for Solving the Problems of Heating Networks

The Development of Hybrid IP Architecture for Solving the Problems of Heating Networks

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

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