Journal ArticleDOI
The Shewhart Control Chart—Tests for Special Causes
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The Shewhart Control Chart for Special Causes (Shewhart control chart) as mentioned in this paper is a popular test chart for special causes and has been used extensively in the medical field.Abstract:
(1984). The Shewhart Control Chart—Tests for Special Causes. Journal of Quality Technology: Vol. 16, No. 4, pp. 237-239.read more
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Risks and decisions for conservation and environmental management
TL;DR: In this paper, the authors present an overview of the risk management cycle in the context of value, history, perception, values, history and perception, and perception of uncertainty in risk management.
Journal ArticleDOI
The Effect of Sample Size on Estimated Limits for and X Control Charts
TL;DR: In this article, the authors show that charts with subgroups of size n require about 400/(n-1) samples, and X charts require about 300 values to estimate control limits that perform like known limits.
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SPC Q charts for start-up processes and short or long runs
TL;DR: In this article, a control chart is designed for processes where data to estimate the process parameters and compute the control limits are available before a production run, and for many processes, especially in a job shop setting, production runs are not necessa..
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A Synthetic Control Chart for Detecting Small Shifts in the Process Mean
Zhang Wu,Trevor A Spedding +1 more
TL;DR: In this article, a synthetic control chart that is an integration of the Shewhart X chart and the conforming run length chart is presented, which is used to detect anomalies.
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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
Oludare Isaac Abiodun,Muhammad Ubale Kiru,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Abubakar Malah Umar,Okafor Uchenwa Linus,Humaira Arshad,Abdullahi Aminu Kazaure,Usman M. Gana +9 more
TL;DR: There is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems and the research focus on current models and the development of new models concurrently for more successes in the field.
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