Success/failure classification

In the previous chapters the purposes of near miss reporting have been outlined and a framework of designing such a safety management tool has been presented. The importance of human behaviour as a dominant factor in incident sequences was stressed by developing a system failure classification scheme largely based on a theoretical model of operator behaviour. Also an overview was given of the organisational factors necessary for a successful implementation of a NMMS. 

In summary, we have seen that if parameters (connectivity degree and firing threshold) are chosen wisely, fully random connections allow an almost always (in the loose sense of a very small failure probability) one-to-one projection of activity patterns from the AL to the MB, a necessary requirement for successful odor classification. At the same time, the activity level in the MB can remain reasonably low even though the absolute minimum for the confusion probability is attained at very high activity levels. 

Classification, on the other hand, aims at predicting a binary label (two-class classification) or at most an integer label (multiclass classification) with a ratio of true versus false positives as a typical measure of success and failure (separation rate). A data set is called separable if a classifier can do the classification without error. Among a broad range of different methods for classification, there are a few frequently used ones ... 

II, Chapters 4, 5, and 6 give all the information regarding the theoretical aspects involved in designing a complete NMMS and ensuring its successful implementation and maintenance. A complete system to detect, describe, analyse and follow-up near misses is outlined (Chapter 4), with special emphasis on a model-based classification of system failure (Chapter 5) a number of key issues relating to organisational aspects like acceptance by employees, and safety cultures are discussed in Chapter 6,... 

In this chapter we have discussed the foundations of molecular similarity analysis that are largely responsible for success or failure of similarity-based methods in the context of virtual compound screening. Furthermore, we have described how crucial underlying SAR characteristics are for molecular similarity analysis and presented a comprehensive methodological framework for the qualitative and quantitative analysis of SARs and the classification of different SAR types. 

Such scaling of d as shown in Fig. 12.24 would give two possible overall results success or failure. At best, this can be used for classification of the experimental conditions as being useful or useless. Thus, a discrete scaling like this cannot be used for optimization. 

The word "unrelated above is key. API s RP521 (10) defines causes to be unrelated if "no process, mechanical or electrical linkages exist among them, or if the length of time that elapses between possible successive occurrences of these causes is sufficient to make their classification unrelated. When one failure causes another, the events are related. For instance, if reboiler heat is controlled by a differential pressure controller, a cooling water failm-e will cause the steam controller to open fully. In this case, cooling water failure and failure of... 

Human error seems responsible for most of the causes of mishaps. The most commonly expressed hope is to isolate the unreliable categories of behavior and to distinguish them from normal, successful performance. It is very doubtful, however, if this may ever come true. We will discuss the reasons, dealing first with the nature of human error. Further, classification schemes are outlined for structuring various types of errors. Surprisingly, errors are not the same at different levels of skill, which is important for the elaboration of preventive measures. Finally, fallacies and failures as regards the decision-making of operators in process industries are reported and decision aids outlined. 

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