Errors violations

Violation Error/Failure A violation error occurs when an intended action is made which deliberately ignores known operational rules, restrictions, or procedures. However, this definition excludes actions that are deliberately intended to harm the system, which come within the category of sabotage. 

Compatibility with Personnel Expectations Compatibility refers to the degree of similarity between the direction of physical movement of a control or an instrument indicator and the worker s expectations. Many errors are due to the fact that the operation of the controls or the layout of the displays is incompatible with population stereotypes. For instance, on a control panel it is customary to increase the value of a parameter by turning the appropriate switch clockwise and reduce its value by turning it coimterclockwise. (Note that this stereotype is the opposite for controls which control flow directly, e.g., valves.) If such a stereotype is violated, errors may occur. Although such errors may be recoverable in the short run, under the stress of a process transient they may lead to serious consequences. 

Violational Errors Intentional Violation Unintentional Violation... 

The general value of human error classifications is that they allow a more structured and focused approach to rmderstanding both the potential causes of the error and the best route to error reduction. For example, the cause of a slip error (which is by defirtition unintentional) is likely to be very different from those of a violation error (which is by defirtition intentional). Similarly while refresher or additional trairting may be of value in reducing ntistake errors it will be of no value whatsoever in attempting to reduce slip errors. 

There is little doubt from the re-interpretation of the Bentley errors given in Table 10.1, that pre-emptive consideration of the potential for hiunan error in the system would have identified most of the errors which occiured and could have indicated the route to solution for most. That said, the preponderance of violation errors (and the nature of those violations) indicates that the most critical latent failure was the poor safety culture that acted as a major predisposing factor for... 

The overlap-sensitivity of EDA conclusions can be easily tested by reformulation in terms of NAOs or other overlap-free orbitals. Indeed, the recent ETS-NOCV ( Extended Transition State - Natural Orbital for Chemical Valence ) method of Ziegler and coworkers (M. P. Mitoraj, A. M. Michalak, and T. Ziegler, J. Chem. Theory Comput. 5, 962, 2009) corrects for normahzation and Pauli-violation errors by a simple AO symmetric orthogo-nalization procedure, and its conclusions closely resemble those of NAO/NBO-hased methods. 

There is a fundamental difference between such scientific controversies and what simply can be called scientific fraud, i.e., deliberate falsification or fudging of data. Sloppy experimental work or data keeping can also lead to questionable or incorrect conclusions, and, although these violate established scientific standards and must be corrected (as they will), they do not necessarily represent deliberate fraud. In all this, the professor has a strict personal responsibility. As he/she is getting most of the recognition for the accomplishment of the research, it is only natural that he/she must also shoulder the responsibility for any mistakes, errors, or even falsifications. It is not accepta-... 

Overview Reconciliation adjusts the measurements to close constraints subject to their uncertainty. The numerical methods for reconciliation are based on the restriction that the measurements are only subject to random errors. Since all measurements have some unknown bias, this restriction is violated. The resultant adjusted measurements propagate these biases. Since troubleshooting, model development, ana parameter estimation will ultimately be based on these adjusted measurements, the biases will be incorporated into the conclusions, models, and parameter estimates. This potentially leads to errors in operation, control, and design. 

Restraints due to artifacts may, by chance, be completely consistent with the correct structure of the molecule. However, the majority of incorrect restraints will be inconsistent with the correct structural data (i.e., the correct restraints and information from the force field). Inconsistencies in the data produce distortions in the structure and violations in some restraints. Structural consistency is often taken as the final criterion to identify problematic restraints. It is, for example, the central idea in the bound-smoothing part of distance geometry algorithms, and it is intimately related to the way distance data are usually specified The error bounds are set wide enough that all data are geometrically consistent. 

Assumption 3 The variance of the random error term is constant over the ranges of the operating variables used to collect the data. When the variance of the random error term varies over the operating range, then either weighted least squares must be used or a transformation of the data must be made. However, this may be violated by certain transformations of the model. 

Errors also occur because people deliberately decide not to carry out instructions that they consider unnecessary or incorrect. These are called violations. For example, they may not wear all the protective clothing or take the other precautions specified on a permit-to-w ork, as discussed in Section 1.4.2. We should ask the following questions both before and after accidents of this type ... 

Strehlow (1975) achieved a solution by conducting a mass balance over the flow field. Such a balance can be drawn up under the assumptions of similarity and a constant density between shock and flame. The assumption of constant density violates the momentum-conservation equation, and is a drastic simplification. The maximum overpressure is, therefore, substantially underestimated over the entire flame speed range. An additional drawback is that the relationship of overpressure to flame speed is not produced in the form of a tractable analytical expression, but must be found by trial and error. 

This section is concerned with errors that are often classified as "violations," that is, situations where established operating procedures appear to have been deliberately disregarded. Such violations sometimes arise because the prescribed way of performing the task is extremely difficult or is incompatible with the demands of production. Another cause is lack of knowledge of the... 

For the sake of completeness, it is also useful to define at this stage the category of errors known as violations. Violations occur when a worker carries out actions that are either prohibited or are different from those which are prescribed by the organization and carry some associated risks. Since violations are deliberate acts, they are not, strictly speaking, errors. However, the violations category is useful when classifying human caused failures. 

Motivational campaigns are one way of dealing with routine violations (see Section 2.5.1.1). They are not directly applicable to those human errors which are caused by design errors and mismatches between the human and the task. These categories of errors will be discussed in more detail in later sections. 

This factor refers to the spatial organization of the information displays. In general, instruments displaying process parameters that are functionally related should also be physically close. In this way, it is likely that a given fault will lead to a symptom pattern that is easier to interpret than a random distribution of information. Although violation of this principle may not induce errors in a direct manner, it may hinder human performance. The following example illustrates this point. 

Violation An error that occurs when an action is taken that contravenes known operational rules, restrictions, and/or procedures. The definition of violations excludes actions taken to intentionally harm the system (i.e., sabotage). 

As is seen, the results of BFCP and SCS are practically identical when ji,jf ji — jf but for the low-frequency transitions SCS yields a halved relative error. In the high-frequency region, the approximate theory provides only the order of magnitude since the basic criterion of SCS for semiclassical relative motion is violated. 

Figure 23.8 shows the readings of a glass electrode [the measured values of of a cell of the type (23.5)] as a function of solution pH. In the range from acidic to neutral solutions, this curve perfectly obeys Eq. (23.7) (i.e., the potential varies linearly by 0.06 V per unit of pH). However, in alkaline solutions the curve departs from this function ( alkali error of the glass electrode ) in strongly alkaline solutions the readings of the electrode are practically independent of solution pH. This is due to violation of the selectivity conditions. At a pH value of 10 and a sodium ion... 

There are several things known about the exact behavior of Vxc(r) and it should be noted that the presently used functionals violate many, if not most, of these conditions. Two of the most dramatic failures are (a) in HF theory, the exchange terms exactly cancel the self-interaction of electrons contained in the Coulomb term. In exact DFT, this must also be so, but in approximate DFT, there is a sizeable self-repulsion error (b) the correct KS potential must decay as 1/r for long distances but in approximate DFT it does not, and it decays much too quickly. As a consequence, weak interactions are not well described by DFT and orbital energies are much too high (5-6 eV) compared to the exact values. 

A Monte Carlo simulation (Fig. 3) can be made as usual (that is, without constraints on the output age), in which case only about 24% of the trials will yield ratios corresponding to a finite age, and a younger limit of >821 ka (95% confidence) or >531 ka (68% conf) is indicated. If, however, the a priori assumption of a closed system with no initial °Th is made, the failed trials can be ignored (since they violate the a priori constraints), and solution of both age and age-error (630 +370/-210 ka at 95% conf., or +150/-140 ka at 68% conf) can be obtained from the Monte Carlo simulations. 

If the assumption of normality is grossly violated, ML estimates of the parameters can only be obtained using the error-in-variables" method where besides the parameters, we also estimate the true (error-free) value of the measured variables. In particular, assuming that Ey i is known, the parameters are obtained by minimizing the following objective function... 

Here xik is an estimated value of a variable at a given point in time. Given that the estimate is calculated based on a model of variability, i.e., PCA, then Qi can reflect error relative to principal components for known data. A given pattern of data, x, can be classified based on a threshold value of Qi determined from analyzing the variability of the known data patterns. In this way, the -statistic will detect changes that violate the model used to estimate x. The 0-statistic threshold for methods based on linear projection such as PCA and PLS for Gaussian distributed data can be determined from the eigenvalues of the components not included in the model (Jack-son, 1992). 

The inverse calibration regresses the analytical values (concentrations), x, on the measured values, y. Although with it a prerequisite of the GAussian least squares minimization is violated because the y-values are not error-free, it has been proved that predictions with inverse calibration are more precise than those with the classical calibration (Centner et al. [1998]). This holds true particularly for multivariate inverse calibration. 

When an accident report says that an accident was due to human error, the writer usually means an error by an operator or other front line worker. But designers and managers also make errors, not slips or lapses of attention as they usually have time to check their work, as well as mistakes or, less often, violations. 

Certain assumptions underly least squares computations such as the independence of the unobservable errors ef, a constant error variance, and lack of error in the jc s (Draper and Smith, 1998). If the model represents the data adequately, the residuals should possess characteristics that agree with these basic assumptions. The analysis of residuals is thus a way of checking that one or more of the assumptions underlying least squares optimization is not violated. For example, if the model fits well, the residuals should be randomly distributed about the value of y predicted by the model. Systematic departures from randomness indicate that the model is unsatisfactory examination of the patterns formed by the residuals can provide clues about how the model can be improved (Box and Hill, 1967 Draper and Hunter, 1967). 

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