Applications and Computational Experiments

2001 ◽  
pp. 207-222
Author(s):  
Stefan M. Stefanov
Keyword(s):  
Computational Experiments

MEASUREMENT AND COMPUTATIONAL EXPERIMENTS WITHIN NEWBORN'S BRAIN COOLING PROCESS

2018 ◽  
Author(s):  
Dominika Bandoła ◽  
Andrzej J. Nowak ◽  
Ziemowit Ostrowski ◽  
Marek Rojczyk ◽  
Wojciech Walas
Keyword(s):  
Computational Experiments ◽  
Brain Cooling ◽  
Cooling Process

Computational Experiments with Direct Simulation Quasi-Random Monte Carlo

2009 ◽  
Author(s):  
Jose Camberos ◽  
Robert Greendyke ◽  
Larry Lambe ◽  
Brook Bentley
Keyword(s):  
Monte Carlo ◽  
Computational Experiments ◽  
Direct Simulation

Researches and Applications of Computational Experiments

2014 ◽  
Vol 39 (8) ◽  
pp. 1157-1169 ◽  
Author(s):  
Kai-Nan CUI ◽  
Xiao-Long ZHENG ◽  
Ding WEN ◽  
Xue-Liang ZHAO
Keyword(s):  
Computational Experiments

scholarly journals Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach

2021 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Christos Bazinas ◽  
Eleni Vrochidou ◽  
Chris Lytridis ◽  
Vassilis Kaburlasos
Keyword(s):  
Time Series ◽  
Accurate Prediction ◽  
Computational Experiments ◽  
Stationary Time Series ◽  
Neural Architecture ◽  
Recursive Scheme ◽  
Stationary Time ◽  
Agricultural Applications ◽  
Data Statistics

This work represents any distribution of data by an Intervals’ Number (IN), hence it represents all-order data statistics, using a “small” number of L intervals. The INs considered are induced from images of grapes that ripen. The objective is the accurate prediction of grape maturity. Based on an established algebra of INs, an optimizable IN-regressor is proposed, implementable on a neural architecture, toward predicting future INs from past INs. A recursive scheme tests the capacity of the IN-regressor to learn the physical “law” that generates the non-stationary time-series of INs. Computational experiments demonstrate comparatively the effectiveness of the proposed techniques.

ABOUT IMPROVEMENT OF SETS OF REPLACEABLE GEARS GUITARS METAL CUTTING MACHINES

2021 ◽  
pp. 21-25
Author(s):  
A. P. Fot
Keyword(s):  
Quality Index ◽  
Metal Cutting ◽  
Average Density ◽  
Performance Characteristics ◽  
Total Weight ◽  
Computational Experiments ◽  
Resource Saving ◽  
Complex Products ◽  
Integral Quality ◽  
Practical Recommendations

The article is devoted to the problems of modernization of the existing fleet of metal-cutting machines. The purpose and objectives-to determine ways and practical recommendations for resource saving in the production of machine components for the manufacture of complex products. The methods used are comparative computational experiments to determine the performance characteristics of sets of replaceable gears. The presented material contains practical recommendations on the choice of evaluation indicators and improvement of sets of replaceable gears of two-pair guitars of various gear-processing machines (5B12, 5A308P, 5111 and 5K32) based on the results of research. Confirmed the possibility of improving the characteristics of kits while maintaining the number of wheels in the kits. In conclusion, it is noted that the proposed solutions contribute to an increase in the integral quality index by 1,2857 – 6,3742 times, a decrease in the total weight of the wheels of the set by 1,4824 – 3,3093 times, an increase in the number of realized gear ratios by 1,2622 – 3,7525 times, an increase in the average density of the values of the gear ratios by 1,2136 – 3,7781 times.

scholarly journals The inverse{0,1}-knapsack problem: Theory, algorithms and computational experiments

2013 ◽  
Vol 10 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Julien Roland ◽  
José Rui Figueira ◽  
Yves De Smet
Keyword(s):  
Knapsack Problem ◽  
Computational Experiments

scholarly journals Computational Experiments with Cross and Crooked Cross Cuts

2014 ◽  
Vol 26 (4) ◽  
pp. 780-797 ◽  
Author(s):  
Sanjeeb Dash ◽  
Oktay Günlük ◽  
Juan Pablo Vielma
Keyword(s):  
Computational Experiments

scholarly journals Examining Infant Relation Categorization Through Deep Neural Networks

2021 ◽  
Author(s):  
Guy Davidson ◽  
Brenden M. Lake
Keyword(s):  
Neural Networks ◽  
Spatial Cognition ◽  
Deep Neural Networks ◽  
Relational Learning ◽  
Spatial Relations ◽  
Computational Experiments ◽  
Visual Understanding

Categorizing spatial relations is central to the development of visual understanding and spatial cognition, with roots in the first few months of life. Quinn (2003) reviews two findings in infant relation categorization: categorizing one object as above/below another precedes categorizing an object as between other objects, and categorizing relations over specific objects predates abstract relations over varying objects. We model these phenomena with deep neural networks, including contemporary architectures specialized for relational learning and vision models pretrained on baby headcam footage (Sullivan et al., 2020). Across two computational experiments, we can account for most of the developmental findings, suggesting these models are useful for studying the computational mechanisms of infant categorization.

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