Errors classes

Accuracy Current error at Phase displacement Composite error class rated primary angle S (Figure at rated... 

Fig. 2.5. A quasi-species-type mutant distribution around a master sequence. The quasi-species is an ordered distribution of polynucleotide sequences (RNA or DNA) in sequence space. A fittest genotype or master sequence /m, which is commonly present at highest frequency, is surrounded in sequence space by a cloud of closely related sequences. Relatedness of sequences is expressed (in terms of error classes) by the number of mutations which are required to produce them as mutants of the master sequence. In case of point mutations the distance between sequences is the Hamming distance. In precise terms, the quasi-species is defined as the stable stationary solution of Eq. (2) [16,19, 20], In reality, such a stationary solution exists only if the error rate of replication lies below a maximal value called the error threshold. In this region, i.e. below...

In these expressions Wn always refers to the individual / in the corresponding error class under consideration, while the fs in the sums refer to the corresponding precursors of Ii, for example, into all d one-error precursors or in 1, (to all d of the (d — 1 )-error precursors of /(. Note that due to the iterative nature, the first sum term in contains d d-fold products of terms. 

Wiegmann, D.A. Rantanen, E. 2002. Defining the relationship between human error classes and technology intervention strategies. Technical Report ARL-02-l/NASA-02-1. Illinois Aviation Research Lab Institute of Aviation. 

In doing so, the system itself realizes with its components functions, which then in combination or through the interaction with other systems or components in the vehicle performs the vehicle functions. A brake system is always dependent on the wheels of a vehicle without a correct functioning of the wheels a brake cannot function correctly. This is why the functions according VDA FMEA are allocated to each error class (also type of failure). This means there is one function for each error class. Generally, the failure level is seen as the level in which a product (system, component, and element or object to be analyzed) is specified. Therefore, it is also possible in this context to test against the requirements, which have to be founded through the implemented functions. 

The philosophy of the TAO and JacORB projects aims for the developer communities to provide solutions to detected problems in these products. This is an alternative to commercial support but it is not always easy depending on the error class detected. Table 1 shows some code metrics taken from the latest ACE+TAO distribution (6.3.1) (TAO is distributed together with ACE, an object oriented framework). 

The hierarchical decomposition of the errors associated with the analysis of a structure by the Finite Element Method requires an examination of the analysis process itself and the error classes. The association with the analysis process implies that the errors relate to the two basic stages of a finite element analysis. These two stages are concerned with the overall consistency of the method which... 

This left 20 X 20 X 8 = 3200 classes, with some classes being very sparsely populated. For such classes, the error term is unacceptably large,... 

The described method can generate a first-order backward or a first-order forward difference scheme depending whether 0 = 0 or 0 = 1 is used. For 9 = 0.5, the method yields a second order accurate central difference scheme, however, other considerations such as the stability of numerical calculations should be taken into account. Stability analysis for this class of time stepping methods can only be carried out for simple cases where the coefficient matrix in Equation (2.106) is symmetric and positive-definite (i.e. self-adjoint problems Zienkiewicz and Taylor, 1994). Obviously, this will not be the case in most types of engineering flow problems. In practice, therefore, selection of appropriate values of 6 and time increment At is usually based on trial and error. Factors such as the nature of non-linearity of physical parameters and the type of elements used in the spatial discretization usually influence the selection of the values of 0 and At in a problem. 

The production of both an alcohol and the sodium salt of an acid might easily be confused with the hydrolysis products of an ester (in the above instance benzyl benzoate). Such an error would soon be discovered (e.g., by reference to the b.p. and other physical properties), but it would lead to an unnecessary expenditure of time and energy. The above example, however, emphasises the importance of conducting the class reactions of neutral oxygen-containing compounds in the proper order, viz., (1) aldehydes and ketones, (2) esters and anhydrides, (3) alcohols, and (4) ethers. 

One class of Al-based computational chemistry programs are de novo programs. These programs generally try to efficiently automate tedious tasks by using some rational criteria to guide a trial-and-error process. For example. 

Once you are experienced at finding transition structures for a particular class of reactions, you will probably go directly to the technique that has been most reliable for those reactions. Until that time, the checklist above is our best advice for finding a transition structure with the least amount of work for the researcher and the computer. Regardless of experience, it is common to experience quite a bit of trial and error in finding transition structures. Even experienced researchers find that the way they have been regarding a reaction is often much more simplistic than the molecular motions actually involved. 

The second class, indeterminate or random errors, is brought about by the effects of uncontrolled variables. Truly random errors are as likely to cause high as low results, and a small random error is much more probable than a large one. By making the observation coarse enough, random errors would cease to exist. Every observation would give the same result, but the result would be less precise than the average of a number of finer observations with random scatter. 

Measurement Errors for Class A Glassware Class B Glassware... 

Vitha, M. F. Carr, P. W. A Laboratory Exercise in Statistical Analysis of Data, /. Chem. Educ. 1997, 74, 998-1000. Students determine the average weight of vitamin E pills using several different methods (one at a time, in sets of ten pills, and in sets of 100 pills). The data collected by the class are pooled together, plotted as histograms, and compared with results predicted by a normal distribution. The histograms and standard deviations for the pooled data also show the effect of sample size on the standard error of the mean. 

Table 1 is condensed from Handbook 44. It Hsts the number of divisions allowed for each class, eg, a Class III scale must have between 100 and 1,200 divisions. Also, for each class it Hsts the acceptance tolerances appHcable to test load ranges expressed in divisions (d) for example, for test loads from 0 to 5,000 d, a Class II scale has an acceptance tolerance of 0.5 d. The least ambiguous way to specify the accuracy for an industrial or retail scale is to specify an accuracy class and the number of divisions, eg. Class III, 5,000 divisions. It must be noted that this is not the same as 1 part in 5,000, which is another method commonly used to specify accuracy eg, a Class III 5,000 d scale is allowed a tolerance which varies from 0.5 d at zero to 2.5 d at 5,000 divisions. CaHbration curves are typically plotted as in Figure 12, which shows a typical 5,000-division Class III scale. The error tunnel (stepped lines, top and bottom) is defined by the acceptance tolerances Hsted in Table 1. The three caHbration curves belong to the same scale tested at three different temperatures. Performance must remain within the error tunnel under the combined effect of nonlinearity, hysteresis, and temperature effect on span. Other specifications, including those for temperature effect on zero, nonrepeatabiHty, shift error, and creep may be found in Handbook 44 (5). The acceptance tolerances in Table 1 apply to new or reconditioned equipment tested within 30 days of being put into service. After that, maintenance tolerances apply they ate twice the values Hsted in Table 1.

Values of a, h, c, and m for various compound classes are found in Table 2-399. Average errors are about 8 percent. 

These errors are valid only when the CTs are loaded by a minimum 25% of the rated VA burden, for CTs of class 1 and 50% for CTs of classes 3 and 5 and a primary currenl of not less than 5% or more than 120% of the rated current. The measuring CTs may not transform correcllv unless the above conditions are met. 

This defines the maximum permissible current error at the rated current for a particular accuracy class. The standard accuracy classes for the measuring CTs may be one of 0.1, 0.2, 0.5, I, 3 and 5. The limits of error in magnitude of the secondary current and the phase error, as discussed in Section 15.6.1 and shown in Figure 15.18,... 

The objective of a proper sampling program is to take a sufficient number of samples to obtain a representative estimate of exposure. Contaminant concentrations vary seasonally, with weather, with production levels, and in a single location or job class. The number of samples taken depends on the error of measurement and differences in results. It is important also that if the employer has conducted air sampling and monitoring in the past, a thorough review of the records should be made. 

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