scholarly journals Structure Preserving Exemplar-Based 3D Texture Synthesis

2021 ◽  
Author(s):  
Andrew Babichev ◽  
Vladimir Alexandrovich Frolov
Keyword(s):  
Neural Network ◽  
3D Model ◽  
Texture Synthesis ◽  
Error Function ◽  
Synthesis Method ◽  
Structural Elements ◽  
The Third ◽  
3D Texture ◽  
Additional Image ◽  
Network Approaches

In this paper we propose exemplar-based 3D texture synthesis method which unlike existing neural network approaches preserve structural elements in texture. The proposed approach does this by accounting additional image properties which stand for the preservation of the structure with the help of a specially constructed error function used for training neural networks. Thanks to the proposed solution we can apply 2D texture to any 3D model (even without texture coordinates) by synthesizing high quality 3D texture and using local or world space position of surface instead 2D texture coordinates (fig. 1). Our solution is based on introducing 3 different error components in to the process of neural network fitting which helps to preserve desired properties of generated texture. The first component is for structuredness of the generated texture and the sample, the second component increases the diversity of the generated textures and the third one prevents abrupt transitions between individual pixels.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

Fullbore Image Construction of Imaging Logging Based on the Improved Texture Inpainting Method

2014 ◽  
Vol 571-572 ◽  
pp. 825-828
Author(s):  
Xiang Zhang ◽  
Jun Hua Wang ◽  
Xiao Ling Xiao
Keyword(s):  
Local Search ◽  
Texture Synthesis ◽  
Synthesis Method ◽  
Image Inpainting ◽  
Search Method ◽  
Local Search Method ◽  
Synthesis Algorithm ◽  
Image Construction ◽  
Tv Model ◽  
Improved Algorithm

The image inpainting method based on CriminiciA’s algorithm is slowly complete the image for large blank area. An improved algorithm based on the classic texture synthesis algorithm for image inpainting is proposed for imaging logging inpainting, which is used to generate the fullbore image. Two schemes, the local search method and priority calculation with TV model, are employed in the improved texture synthesis method. Some examples were given to demonstrate the effectiveness of the proposed algorithm on dealing with fullbore image construction with large blank area and raising efficiency obviously.

scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

scholarly journals Outlier detection in scatterometer data: neural network approaches

2003 ◽  
Vol 16 (3-4) ◽  
pp. 419-426 ◽  
Author(s):  
Robert J. Bullen ◽  
Dan Cornford ◽  
Ian T. Nabney
Keyword(s):  
Neural Network ◽  
Outlier Detection ◽  
Network Approaches

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

The Structure of a Free Speech Right

2021 ◽  
pp. 212-230
Author(s):  
Stephen Gardbaum
Keyword(s):  
Free Speech ◽  
Freedom Of Expression ◽  
Common Law ◽  
Structural Elements ◽  
The Third ◽  
The Subject ◽  
The Right ◽  
Free Speech Rights

This chapter describes the structural elements or components of a free speech right. The nature and extent of a free speech right depends upon a number of legal components. The first is the legal source of the right (in common law, statute, or a constitution) and the force of the right having regard to how it is enforced, and whether and how it can be superseded. The second component is the ‘subject’ of free speech rights, or who are the rights-holders: citizens, natural or legal persons. The third is the ‘scope’ of a free speech right, while the fourth is the kind of obligation it imposes on others: a negative prohibition or a positive obligation. The fifth component is the ‘object’ of a free speech right: who is bound to respect a right of freedom of expression and against whom the right may be asserted. Finally, there is the ‘limitation’ of a free speech right.

scholarly journals Ancient Coastal Changes Due to Ground Movements and Human Interventions in the Roman Portus Julius (Pozzuoli Gulf, Italy): Results from Photogrammetric and Direct Surveys

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 658 ◽  
Author(s):  
Pietro P. C. Aucelli ◽  
Gaia Mattei ◽  
Claudia Caporizzo ◽  
Aldo Cinque ◽  
Salvatore Troisi ◽  
...  
Keyword(s):  
Sea Level ◽  
3D Model ◽  
First Century ◽  
Structural Elements ◽  
Sea Levels ◽  
Ground Movements ◽  
Vertical Ground ◽  
Coastal Changes ◽  
Fish Tank ◽  
The Military

This research aims to evaluate the amount of vertical ground movements during Roman times inside the archaeological area of Portus Julius (Gulf of Pozzuoli) using high-precision surveys on the most reliable archaeological sea-level markers. Measuring the submersion of ancient floors, structural elements belonging to a former fish tank, and several roman pilae, two different relative sea levels (RSLs), related to the beginning and the end of the first century BCE, respectively, −4.7/−5.20 m and −3.10 m MSL (mean sea level), were detected. A photogrammetric survey was carried out in order to produce a 3D model of the fish tank. The results in terms of the RSL variations have enabled us to reconstruct a morpho-evolution of the ancient coastal sector during the last 2.1 kyBP. At the beginning of the first century BCE, the area was characterized by a sheltered gulf with numerous maritime villae located along the coast. In 37 BCE, the construction of the military harbour of Portus Julius strongly modified the paleogeography of the sector, which was also affected by a prevailing subsidence at least until the end of the first century BCE (year 12 BCE), when the port was converted into a commercial hub.

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