Results 111 to 120 of about 6,424,855 (311)

API-specific input parameters.

, 2018
API-specific input parameters.
Gert Storm (21109), Raymond M. Schiffelers (21110), Iris E. Allijn (1879780), Rik Oldenkamp (835113), Ad M. J. Ragas (835114)   +4 more
core   +1 more source

DЕTERMINATION OF THE DRUM MILLS’ ENGINE CAPACITY BY USING NEURAL NETWORK WITH SUBORDINATE INPUT PARAMETERS [PDF]

Fiabilitate şi Durabilitate, 2012
A successful experiment has been done to train the neural network to determine the drum mills’ engine capacity by using the program „QwikNet 2.23”. As a result we get a trained neural network with a maximum error of 1.00619.10-5 which can be used for ...
Teodora HRISTOVA, Ivan MININ
doaj  

Effect of Input Parameters on The Mechanical Properties of Green Sand Mould [PDF]

Archives of Foundry Engineering
Among all the methods of metal forming, green sand moulding is the most commonly used method due to its low cost and high speed of work. Main constituents of green sand mould are sand, water, coal dust and binder. Mechanical properties like permeability,
Arpit H. Modi, Shailee G. Acharya
doaj   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Model input parameters.

, 2018
Model input parameters.
Mickaël Hiligsmann (3331467), Robbin H. Ophuis (4872691), Silvia M. A. A. Evers (643477), Joran Lokkerbol (4872694)   +3 more
core   +1 more source

DЕTERMINATION OF THE DRUM MILLS’ ENGINE CAPACITY BYUSING NEURAL NETWORK WITH SUBORDINATE INPUT PARAMETERS [PDF]

Fiabilitate şi Durabilitate, 2013
A successful experiment has been done to train the neural network to determine the drum mills’engine capacity by using the program „QwikNet 2.23”. As a result we get a trained neural network with amaximum error of 1.00619.10-5 which can be used for ...
Teodora HRISTOVA, Ivan MININ
doaj  

Method of formulating input parameters of neural network for diagnosing gas-turbine engines

Aviation, 2013
A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine.
Mykola Kulyk, Sergiy Dmitriev, Oleksandr Yakushenko, Oleksandr Popov   +3 more
doaj   +1 more source

Optimizing photoactivation of PA‐mCherry for optical pooled CRISPR screens

FEBS Open Bio, EarlyView.
Photoactivatable PA‐mCherry finds widespread use to optically tag individual cells. However, confocal 405 nm UV laser‐scanning (normal scan) is much less efficient than widefield UV illumination, limiting the use of PA‐mCherry on confocal instruments. We remedy this limitation by reporting that rapid and repeated confocal scanning with a low‐intensity,
Sravasti Mukherjee, Giulia Zanetti, Bram van den Broek, Kees Jalink   +3 more
wiley   +1 more source

NUFT USNT Thermal Input Parameters [PDF]

, 2000
This document describes the thermal input parameters required to run the USNT module of the NUFT code. The USNT module handles multi-component transport of multiple fluid phases and heat through porous and fractured media.
Lee, K. H.
core   +1 more source

input

, 2021
All input files (INCAR, POSCAR, KPOINTS) needed to reproduce the MD results, and the trajectories (XDATCAR) from two-phase simulations are deposited here.
l y (8399214)
core   +1 more source