Optical/structural machine analysis of a material's microstructure as a basis for formation of constructional strength of details

1998 ◽  
Author(s):  
Eduard I. Ulianov ◽  
A. V. Liasnikov ◽  
Alexey A. Lavrov ◽  
D. P. Svetlichny ◽  
A. V. Ulianov
Keyword(s):  
Constructional Strength ◽  
Machine Analysis

Optical-structural machine analysis as a basis of forming constructional strength of details

2003 ◽  
Author(s):  
Eduard I. Ulianov ◽  
D. P. Svetlichny ◽  
Alexey A. Lavrov ◽  
A. V. Ulianov ◽  
A. I. Vorobjov
Keyword(s):  
Constructional Strength ◽  
Machine Analysis

scholarly journals Simulation Tests of a Cow Milking Machine—Analysis of Design Parameters

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1358
Author(s):  
Ewa Golisz ◽  
Adam Kupczyk ◽  
Maria Majkowska ◽  
Jędrzej Trajer
Keyword(s):  
Mathematical Model ◽  
Simplified Model ◽  
Design Parameters ◽  
Degree Polynomial ◽  
Fourth Degree ◽  
Local Loss ◽  
Linear Drag ◽  
Milking Machine ◽  
Machine Analysis ◽  
The Impact

The objective of this paper was to create a mathematical model of vacuum drops in a form that enables the testing of the impact of design parameters of a milking cluster on the values of vacuum drops in the claw. Simulation tests of the milking cluster were conducted, with the use of a simplified model of vacuum drops in the form of a fourth-degree polynomial. Sensitivity analysis and a simulation of a model with a simplified structure of vacuum drops in the claw were carried out. As a result, the impact of the milking machine’s design parameters on the milking process could be analysed. The results showed that a change in the local loss and linear drag coefficient in the long milk duct will have a lower impact on vacuum drops if a smaller flux of inlet air, a higher head of the air/liquid mix, and a higher diameter of the long milk tube are used.

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

Twelve-Phase Induction Machine Analysis With Harmonic Injection

2018 ◽  
Author(s):  
Isaac Soares de Freitas ◽  
Italo R. F. M. P. da Silva ◽  
Marcos L. Quirino ◽  
Elves S. e Silva ◽  
Zariff M. Gomes ◽  
...  
Keyword(s):  
Induction Machine ◽  
Harmonic Injection ◽  
Machine Analysis

scholarly journals Multi-machine analysis of termination scenarios with comparison to simulations of controlled shutdown of ITER discharges

2017 ◽  
Vol 58 (2) ◽  
pp. 026019 ◽  
Author(s):  
P.C. de Vries ◽  
T.C. Luce ◽  
Y.S. Bae ◽  
S. Gerhardt ◽  
X. Gong ◽  
...  
Keyword(s):  
Machine Analysis

scholarly journals ANALISIS DAN PERBANDINGAN QUALITY OF SERVICE VIDEO CONFERENCE JITSI DAN BIGBLUEBUTTON PADA VIRTUAL PRIVATE SERVER

2021 ◽  
Vol 4 (2) ◽  
pp. 192-203
Author(s):  
Ida Bagus Ary Indra Iswara ◽  
I Putu Pedro Kastika Yasa
Keyword(s):  
Quality Of Service ◽  
Open Source ◽  
Virtual Machine ◽  
Packet Loss ◽  
Video Conferencing ◽  
Video Conference ◽  
Machine Analysis ◽  
Test Scenarios

The use of video conferencing technology is increasing due to the COVID-19 pandemic. Bigbluebutton and jitsi are examples of open source video conferencing platforms that can be installed on their own servers. The server is created using a cloud-based virtual machine. Analysis of quality of service which includes delay, packet loss, throughput, and jitter is needed to determine the quality of service and the comparison of the two platforms. Observations were also made on the use of CPU, memory / RAM, and disk usage for each server. There are 3 test scenarios carried out. Each scenario is carried out on each existing VM specification. From this test, it is known that in the delay parameter, the highest bigbluebutton is obtained, which is 35,35 ms. And then the highest jitsi delay is 17,66 ms. In packet loss parameters, jitsi obtained the highest yield, namely 0,29%, while for bigbluebutton only 0,16% of packet loss was the highest. Throughput, bigbluebutton and jitsi all got very bad results. However, bigbluebutton obtained better results, namely, the highest throughput was 5.6%. While Jitsi obtained the highest throughput, namely 2,8%. Whereas for the jitter parameter, jitsi obtained 0,00 ms results on all tests in each VM. Meanwhile, bigbluebutton, get 0,1 ms on test 3 on VM 1

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