scholarly journals The Michigan 10-Degree Blue Spectral Survey as a Basis for Future Deep Schmidt Surveys

1995 ◽  
Vol 148 ◽  
pp. 292-295
Author(s):  
N. Houk ◽  
T. von Hippel

AbstractThe Henry Draper stars are being systematically classified on the MK System, using the Curtis and Burrell Schmidt telescopes with photographic spectra having a dispersion of 108 Å/mm. Over 156,000 stars south of δ = +5°, have been classified leaving about 69,000 yet to do. The project is expected to be completed around the year 2004. This all-sky network of consistently classified spectra of very good quality should serve as a basis for future deep surveys. Such surveys will almost certainly be automated because of the huge number of stars to be dealt with. Von Hippel et al. at Cambridge plan to scan at least 150,000 of the spectra classified by Houk, using her plates to serve as a ‘training’ set for automatic classification using artificial neural networks. The same data can also be utilized for other methods of automatic classification including the metric-distance methods used by Kurtz and La Sala (Kurtz 1983). Even at lower dispersions, significantly more information can be obtained from Schmidt spectra than by doing Schmidt photometric colour surveys alone, though these are also valuable, especially when used in conjunction with spectra. We urge that large Schmidts not currently having prisms or other dispersive elements consider adding this equipment.

Metals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1569
Author(s):  
Nicolae Filipoiu ◽  
George Alexandru Nemnes

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.


2019 ◽  
Author(s):  
Ashat Sydikhov ◽  
Alexander Buevich ◽  
Alexander Sergeev ◽  
Andrey Shichkin ◽  
Irina Subbotina ◽  
...  

2018 ◽  
Vol 36 ◽  
pp. 207-215 ◽  
Author(s):  
Cormac Reale ◽  
Kenneth Gavin ◽  
Lovorka Librić ◽  
Danijela Jurić-Kaćunić

Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey

With the artificial neural networks which we have met so far, we must have a training set on which we already have the answers to the questions which we are going to pose to the network. Yet humans appear to be able to learn (indeed some would say can only learn) without explicit supervision. The aim of unsupervised learning is to mimic this aspect of human capabilities and hence this type of learning tends to use more biologically plausible methods than those using the error descent methods of the last two chapters. The network must self-organise and to do so, it must react to some aspect of the input data - typically either redundancy in the input data or clusters in the data; i.e. there must be some structure in the data to which it can respond.


Author(s):  
Apurva Patel ◽  
Patrick Andrews ◽  
Joshua D. Summers

Artificial Neural Networks (ANNs) have been used to predict assembly time and market value from assembly models. This was done by converting the assembly models into bipartite graphs and extracting 29 graph complexity metrics which were used to train the ANN prediction models. This paper presents the use of sub-assembly models instead of the entire assembly model to predict assembly quality defects at an automotive OEM. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection, and second order graph seeding, over 70% of the predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from sub-assemblies complexity data.


1996 ◽  
Vol 33 (1) ◽  
pp. 35-46 ◽  
Author(s):  
W. Wu ◽  
B. Walczak ◽  
D.L. Massart ◽  
S. Heuerding ◽  
F. Erni ◽  
...  

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