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 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

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.

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.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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