Automated Systems Techniques for On-Line Quality and Production Control in the Manufacture of Textiles

2000 ◽  
Author(s):  
Stantcho Djiev ◽  
Luben Pavlov
Keyword(s):  
Production Control ◽  
Automated Systems ◽  
On Line

scholarly journals Booth Library On-Line Circulation System (BLOC)

1971 ◽  
Vol 4 (2) ◽  
pp. 86
Author(s):  
Paladugu V. Rao ◽  
B. Joseph Szerenyi
Keyword(s):  
Circulation System ◽  
Time Sharing ◽  
Automated Systems ◽  
Peripheral Equipment ◽  
University Library ◽  
On Line

<p class="p1">An <span class="s1">on-line </span>circulation <span class="s1">system </span>developed at a relatively small university library demonstrates that <span class="s1">academic </span>libraries <span class="s1">with </span>limit<span class="s2">e</span>d funds <span class="s1">can </span>develop automated systems utilizing parent institution's <span class="s1">computer </span>facilities in a time-sharing mode. In operation <span class="s1">since </span>September 1968, using an IBM 360/50 <span class="s4">computer and associated </span>peripheral equipment, it <span class="s4">provides </span>control over all stack books.</p>

On-line production control using a genetic algorithm

1999 ◽  
Vol 37 (1-2) ◽  
pp. 101-104
Author(s):  
T.K. Kurian ◽  
Ch.V.Krishna Reddy
Keyword(s):  
Genetic Algorithm ◽  
Production Control ◽  
Line Production ◽  
On Line

Production control in semi-automated systems: comparison between constant and stochastic speeds

1996 ◽  
Vol 7 (2) ◽  
pp. 176-183 ◽  
Author(s):  
LEA FRIEDMAN ◽  
DIMITRI GOLENKO-GINZBURG ◽  
ZILLA SINUANY-STERN
Keyword(s):  
Production Control ◽  
Automated Systems

Application of the GRA and SVM for Forecast of China Grain Production

2012 ◽  
Vol 433-440 ◽  
pp. 1106-1111
Author(s):  
Jin Fa Shi ◽  
He Jun Jiao
Keyword(s):  
Support Vector Machine Model ◽  
Production Control ◽  
Food System ◽  
Support Vector ◽  
Production Data ◽  
Grain Production ◽  
Machine Model ◽  
Factors Influencing ◽  
Svm Model ◽  
On Line

This paper proposes a new method: GRA-SVM model which is composed of GRA and SVM to predict grain production through annual production data. In view of the fact that the complexity and incomplete information of grain production system, the primary factors influencing the grain production is decided on the basis of the grey ralational analysis of the grain producing system, then, the grey ralational analysis and support vector machine model is established by the principle of the support vection machine regression. The application case proved that the proposed method can improve the feasibility of the program in grain production, and it is suitable for on-line grain production control for food system.

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