scholarly journals Applicability of neural network in rock classification of mountain tunnel considering rock types

2021 ◽  
Vol 16 (3) ◽  
pp. 221-234
Author(s):  
Takafumi KITAOKA ◽  
Yukitsugu MASUDA ◽  
Nobusuke HASEGAWA ◽  
Thirapong PIPATPONGSA ◽  
Hiroyasu OHTSU
Keyword(s):  
Neural Network ◽  
Rock Classification ◽  
Rock Types ◽  
Mountain Tunnel

scholarly journals Applicability of Neural Network in Rock Classification of Mountain Tunnel

2018 ◽  
Vol 67 (3) ◽  
pp. 354-359 ◽  
Author(s):  
Nobusuke HASEGAWA ◽  
Shingo HASEGAWA ◽  
Takafumi KITAOKA ◽  
Hiroyasu OHTSU
Keyword(s):  
Neural Network ◽  
Rock Classification ◽  
Mountain Tunnel

scholarly journals Applicability of Neural Network in Rock Classification of Mountain Tunnel

2019 ◽  
Vol 60 (5) ◽  
pp. 758-764 ◽  
Author(s):  
Nobusuke Hasegawa ◽  
Shingo Hasegawa ◽  
Takafumi Kitaoka ◽  
Hiroyasu Ohtsu
Keyword(s):  
Neural Network ◽  
Rock Classification ◽  
Mountain Tunnel

scholarly journals Automated Classification of High-resolution Rock Image Based on Residual Neural Network

2021 ◽  
Vol 2095 (1) ◽  
pp. 012051
Author(s):  
Weibo Cai ◽  
Juncan Deng ◽  
Qirong Lu ◽  
Kengdong Lu ◽  
Kaiqing Luo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Oil And Gas ◽  
Automated Classification ◽  
Maximum Probability ◽  
Rock Classification ◽  
Oil And Gas Exploration ◽  
Local Image

Abstract The identification and classification of high-resolution rock images are significant for oil and gas exploration. In recent years, deep learning has been applied in various fields and achieved satisfactory results. This paper presents a rock classification method based on deep learning. Firstly, the high-resolution rock images are randomly divided into several small images as a training set. According to the characteristics of the datasets, the ResNet (Residual Neural Network) is optimized and trained. The local images obtained by random segmentation are predicted by using the model obtained by training. Finally, all probability values corresponding to each category of the local image are combined for statistics and voting. The maximum probability value and the corresponding category are taken as the final classification result of the classified image. Experimental results show that the classification accuracy of this method is 99.6%, which proves the algorithm’s effectiveness in high-resolution rock images classification.

scholarly journals Mineral Rock Classification Using Convolutional Neural Network

2021 ◽  
Author(s):  
Shanmuk Srinivas Amiripalli ◽  
Grandhi Nageshwara Rao ◽  
Jahnavi Behara ◽  
K Sanjay Krishna ◽  
Mathurthi pavan venkat durga ram
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Image Recognition ◽  
Negative Impact ◽  
Rock Classification ◽  
Rock Types ◽  
Image Dataset ◽  
Neural Network Algorithm

The main aim of the research is to build a model that can effectively predict the type of mineral rocks. Rocks can be predicted by observing it is colour, shape and chemical composition. On-site technicians need to apply different techniques on rock sample in order to predict rock type. Technicians need to apply different techniques on rock samples, so it is a time-consuming process, and sometimes the predictions may be accurate, and sometimes predictions may be false. When predictions are false, it might show a negative impact in several ways for workers and organization as well. We considered an image dataset of rock types, namely Biotite, Bornite, Chrysocolla, Malachite, Muscovite, Pyrite, and Quartz. We applied CNN (Convolutional Neural Network) Algorithm to get a better prediction of different mineral rocks. Nowadays, CNN is mainly used for image classification and image recognition tasks.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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