scholarly journals QUALITY CONTROL ON THE BASIS OF ENTROPY DETERMINATION

Transport ◽  
2004 ◽  
Vol 19 (2) ◽  
pp. 51-55 ◽  
Author(s):  
Yasaratne Bandara Dissanayake ◽  
Aleksandr Pankov ◽  
Vladimir Shestakov Shestakov
Keyword(s):  
Complex System ◽  
Quality Control ◽  
Statistical Methods ◽  
Statistical Information ◽  
System Control ◽  
Sources Of Risks ◽  
Adverse Factor

Statistical methods are extensively used in quality control. The method of the application of entropy conception for quality control is proposed. It is based on the use of statistical information about deviations in the operation of a complex system. In the context of this: the structure of the complex system control is considered; the conceptions of controllable and observable systems are introduced. The classification of adverse factor is given influencing upon the system. The example is presented, showing how the application of the proposed method permits to give the evaluation of sources of risks and quality reduction in the complex system.

scholarly journals Quality Control in Production Processes

2016 ◽  
Vol 19 (3) ◽  
pp. 77-83 ◽  
Author(s):  
Miroslav Prístavka ◽  
Martina Kotorová ◽  
Radovan Savov
Keyword(s):  
Quality Control ◽  
Quality Improvement ◽  
Quality Management ◽  
Statistical Methods ◽  
Developed Countries ◽  
Basic Knowledge ◽  
Root Cause ◽  
Production Processes ◽  
Basic Quality ◽  
Quality Tools

AbstractThe tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.

Evaluation of graphical and multivariate statistical methods for classification of water chemistry data

2002 ◽  
Vol 10 (4) ◽  
pp. 455-474 ◽  
Author(s):  
Cüneyt Güler ◽  
Geoffrey D. Thyne ◽  
John E. McCray ◽  
Keith A. Turner
Keyword(s):  
Water Chemistry ◽  
Statistical Methods ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Chemistry Data

scholarly journals STATISTICAL PROBABILITY EVALUATION OF OPERATING ITS

Transport ◽  
2010 ◽  
Vol 25 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Adolfas Baublys ◽  
Aldona Jarašūnienė
Keyword(s):  
Analytical Description ◽  
Statistical Information ◽  
Transport Systems ◽  
Statistical Probability ◽  
Intelligent Transport ◽  
Loop Detectors ◽  
Probability Evaluation ◽  
Control Technologies ◽  
And Control

Intelligent Transport Systems (ITS) work with information and control technologies providing the core of ITS functions. Some of these technologies like loop detectors are well known to transportation professionals. However, there are a number of less familiar technologies and system concepts that are keys to ITS functions. Although information and control technologies act as a technical core of ITS, human factors also remain vitally important and potentially very complex issues. The process of operating ITS is influenced by a number of random factors. Along with an assessment of dependence upon separate random factors, the classification of those in the whole hierarchical structure of operating ITS is presented. Statistical information on operating ITS is renewed and replenished in the course of time. With the growth of information amounts, the costs of storing them also increase. Therefore, the article presents relevant algorithms for obtaining required statistical assessments with the least statistical information. It is deduced that while modelling the process of operating ITS, an analytical description of random factors applying non‐parametric assessment is suitable.

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