scholarly journals END-TO-END CLASSIFICATION OF LIGHT-COLORED OBJECT-SPACE ENVIRONMENT SYSTEMS IN THE STRUCTURE OF STOP SPACES

2020 ◽  
Vol 0 (72) ◽  
pp. 252-272
Author(s):  
Valerii Tovbych ◽  
Nataliya Kulichenko ◽  
Olga Kondratska ◽  
Nikolay Sysojlov
Keyword(s):  
Space Environment ◽  
Object Space ◽  
End To End

scholarly journals THE ONTOLOGICAL TRIANGLE OF THE RELATIONAL PARADIGM

Metaphysics ◽  
2021 ◽  
pp. 24-38
Author(s):  
M. G Godarev-Lozovsky
Keyword(s):  
Space Environment ◽  
Ideal Space ◽  
Physical Knowledge ◽  
Space And Time ◽  
Electromagnetic Interactions ◽  
Quantum Particles ◽  
Field Information ◽  
Relational Space ◽  
Relational Paradigm

The philosophical analysis of three main paradigms in the basis of physical knowledge is carried out. It is permissible to conclude that in the case of electromagnetic interaction between the emitter and the absorber: 1) the process of interaction of the photon with the medium in space and time can occur; 2) in the case when the photon “teleports” - there is only a relation outside of space and time. The following classification of fundamental concepts, with which the relational paradigm deals, is revealed. The ideal: space and time, field, information, a set of movements of quantum particles. The material: interactions, environment. Nothing more than countable: time, electromagnetic interactions. Uncountable: space, environment, interactions with the environment, a set of movements of quantum particles. Substantial: environment, interactions, information, a set of movements of quantum particles. Relational: space, time, field - as a means of description.

scholarly journals End-to-End Event Classification of High-Energy Physics Data

2018 ◽  
Vol 1085 ◽  
pp. 042022 ◽  
Author(s):  
M Andrews ◽  
M Paulini ◽  
S Gleyzer ◽  
B Poczos
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Event Classification ◽  
End To End ◽  
Energy Physics

Classification of ALS Point Clouds Using End-to-End Deep Learning

2019 ◽  
Vol 87 (3) ◽  
pp. 75-90 ◽  
Author(s):  
Lukas Winiwarter ◽  
Gottfried Mandlburger ◽  
Stefan Schmohl ◽  
Norbert Pfeifer
Keyword(s):  
Deep Learning ◽  
Point Clouds ◽  
End To End

SmartEEG: An End-to-End Framework for the Analysis and Classification of EEG signals

2021 ◽  
Author(s):  
Alexe Ciurea ◽  
Cristina-Petruta Manoila ◽  
Bogdan Ionescu
Keyword(s):  
Eeg Signals ◽  
End To End

Multiple CNN Variants and Ensemble Learning for Sunspot Group Classification by Magnetic Type

2021 ◽  
Vol 257 (2) ◽  
pp. 38
Author(s):  
Rongxin Tang ◽  
Xunwen Zeng ◽  
Zhou Chen ◽  
Wenti Liao ◽  
Jingsong Wang ◽  
...  
Keyword(s):  
Active Region ◽  
Ensemble Learning ◽  
Space Weather ◽  
Sunspot Group ◽  
Geomagnetic Storms ◽  
Automatic Classification ◽  
Space Environment ◽  
Data Set ◽  
Magnetic Type

Abstract A solar active region is a source of disturbance for the Sun–terrestrial space environment and usually causes extreme space weather, such as geomagnetic storms. The main indicator of an active region is sunspots. Certain types of sunspots are related to extreme space weather caused by eruptive events such as coronal mass ejections or solar flares. Thus, the automatic classification of sunspot groups is helpful to predict solar activity quickly and accurately. This paper completed the automatic classification of a sunspot group data set based on the Mount Wilson classification scheme, which contains continuum and magnetogram images provided by the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager SHARP data from 2010 May 1 to 2017 December 12. After applying some data preprocessing steps such as image cropping and data standardization, the features of magnetic type in the data are more obvious, and the amount of data is increased. The processed data are spliced into two frames of single-channel data for the neural network to perform 3D convolution operations. This paper constructs a variety of convolutional neural networks with different structures and numbers of layers, selects 10 models as representatives, and chooses XGBoost, which is commonly used in ensemble-learning algorithms, to fuse the results of independent classification models. We found that XGBoost is an effective way to fuse models, which is proved by the relatively balanced high scores in the three magnetic types. The accuracy of the ensemble model is above 92%. The F1 scores of the magnetic types of Alpha, Beta, and Beta-x reached 0.95, 0.91, and 0.82 respectively.

scholarly journals End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery

2020 ◽  
Vol 10 (7) ◽  
pp. 2501
Author(s):  
Yiheng Cai ◽  
Shaobin Hu ◽  
Shinan Lang ◽  
Yajun Guo ◽  
Jiaqi Liu
Keyword(s):  
Ice Sheet ◽  
Future Evolution ◽  
Radar Images ◽  
Learning Techniques ◽  
Testing Data ◽  
Radar Imagery ◽  
Spatial Pyramid Pooling ◽  
End To End ◽  
Comprehensive Study

Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram.

scholarly journals NEURAL NETWORKS FOR THE CLASSIFICATION OF BUILDING USE FROM STREET-VIEW IMAGERY

2018 ◽  
Vol IV-2 ◽  
pp. 177-184
Author(s):  
D. Laupheimer ◽  
P. Tutzauer ◽  
N. Haala ◽  
M. Spicker
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Abstract Representations ◽  
Street View ◽  
Building Facades ◽  
City Model ◽  
End To End

Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (<i>commercial, hybrid, residential, specialUse, underConstruction</i>) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.

Sign in / Sign up

Export Citation Format

Share Document