Difference between revisions of "Team:Edinburgh UG/Semantic Containment Modelling"
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<h1 class="brand-heading">Semantic Containment Failure Rate</h1> | <h1 class="brand-heading">Semantic Containment Failure Rate</h1> | ||
<h2 style="text-align:left">Introduction</h2> | <h2 style="text-align:left">Introduction</h2> | ||
| − | <p style="text-align:left"></a> | + | <p style="text-align:left">In any safety system it is vitally important to evaluate how likely failures are to occur both to allow the quantification of risk and to assess the fitness of the particular system, our <a href="https://2018.igem.org/Team:Edinburgh_UG/Design"> semantic containment system</a> is no different. We wanted to be able to assess our semantic containment system in a way that would be comparable to other safety systems in different disciplines. The performance level system (PL) was set in 2006 to assess the suitability of electronic parts used in control systems. The PL system ranks parts over 5 different categories defined by the probability of a dangerous failure within an hour and hence we produced a mathematical model to assess our semantic containment system in the same manner.</p> |
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Revision as of 15:28, 17 October 2018
Semantic Containment Modelling
Semantic Containment Failure Rate
Introduction
In any safety system it is vitally important to evaluate how likely failures are to occur both to allow the quantification of risk and to assess the fitness of the particular system, our semantic containment system is no different. We wanted to be able to assess our semantic containment system in a way that would be comparable to other safety systems in different disciplines. The performance level system (PL) was set in 2006 to assess the suitability of electronic parts used in control systems. The PL system ranks parts over 5 different categories defined by the probability of a dangerous failure within an hour and hence we produced a mathematical model to assess our semantic containment system in the same manner.
Mass Action Equations
Ordinary Differential Equations
Results
Failure Rate
Conclusion
Ordinal Logistic Regression Classifier
Introduction
Methodology
Results
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