Errors generic system

FIGURE 2.5. Dynamics of Generic Error Modeling System (GEMS) (adapted from Reason, 1990). 

Reason, J.T. (1987). Generic Error-Modelling Systems (GEMS) a cognitive framework for locating human error forms. In J. Rasmussen, K. Duncan and J. Leplat (eds). New Technology and Human Error. Wiley. New York. 

Figure 13.6 Dynamics of generic error-modelling system (CEMS). (From Reason 0...

These classifications of human interactions can be related to a simple error classification system consisting of three categories (1) slips, (2) non-response, and (3) mistakes. This classification scheme can then be used to qualitatively incorporate human errors in accident scenarios. Table 9.1 provides generic human error probabilities for use in accident scenario... 

Finally, the MOS should also take into account the uncertainties in the estimated exposure. For predicted exposure estimates, this requires an uncertainty analysis (Section 8.2.3) involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters. General sources of variability and uncertainty in exposure assessments are measurement errors, sampling errors, variability in natural systems and human behavior, limitations in model description, limitations in generic or indirect data, and professional judgment. 

Figure 9-2 shows a generic diagram for the control of a chemical process. The controller will function to minimize or correct for any unexpected disturbances that may upset the process. A control system will measure one of the output variables that must be controlled, Y (e.g., temperature, concentration), and compare it to a desired value Fsp, called the set point. The difference, between the actual value, Y, and the desired value, Tsp, is called the error signal, e. That is,... 

JBPDS The Joint Biological Point Detection System (JBPDS) is in development to replace and outperform the Army BIDS and Navy IBAD systems. It is planned to enter development of Block II in FY 2004. JBPDS comprises trigger, sampler, detection, and identification subsystems to meet Joint Operational Requirements Document (JORD) specifications. It is designed to be able to identify multiple BW agents in less than 15 minutes, at 1 ACPLA sensitivity, and have less than 2 percent error in identification." Generic UV laser-induced fluorescence detection capability (BAWS) improves system performance while reducing operations and support costs. 

This simple example is very instructive and shows the basic key features of classical error-correction. First, one has to assume a particular and physically motivated error model, one cannot fight a completely unknown enemy Then, one applies the following generic scheme. First, one encodes information on well-chosen states of an extended and redundant system of bits. For instance, in the repetition code, the original bit of information is encoded on two particular states of a three bit system. The idea is clear redundancy prevents information from serious damage due to the errors and assures very likely recovery (let us emphasize that one uses the same kind of trick in everyday life when asking someone to repeat a sentence or a question to make sure of every word). Finally, after the transmission through the noisy channel, one decodes... 

Evidently, each of these approaches generates specific implementation problems in the treatment of the initialization of the EDA system and integration error control, but this was generically previewed in the package (3). 

In this special case, a criterion analogous to the one proposed in Buzzi-Ferraris and Tronconi (1993) and adopted in BzzNonLinearSystem class for a generic nonlinear system based on the decrease of a norm of the vector d from an iteration to the next one is adopted. Whereas in the general case this criterion is rarely used, in this special case where the error on the variables is known, the use of this criterion is quite spontaneous (Vol. 3, Buzzi-Ferraris and Manenti, 2014). 

Many of the data published today are either erroneous or apply to inadequately characterized materials. Unrecognized systematic errors are frequently present in the measurement process, and consequently the data do not represent the property purportedly measured within the stated uncertainty. In other instances, the property measured is especially sensitive to unreported characteristics of the material. For example, the transport properties of pure metals and crystalline dielectric solids at low temperatures are determined almost entirely by the physical and chemical defects in them, rather than by their chemical or generic identification, such as copper, aluminum, etc. Great care must be exercised to characterize such materials, if reported data are to have any usefulness. Both the study of systematic errors and specimen characterization are of the utmost importance to the reliability and usefulness of data. This paper only considers the use of standard reference materials (SRMs) to reduce systematic errors in the measuring system. The problem of insufficient material characterization is an extensive subject in itself and will not be discussed further here. 

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