Using the differential mean value theorem, we transform the nonlinear error dynamics descriptor system into a linear parameter varying descriptor system.
It presents a memory fault simulator called random access memory simulator for error screening (RAMSES), which consists of a simulation engine and numerous fault descriptors.
The sensitivity of R-histogram to affine transformations is evaluated by the matching error between descriptors of the corresponding template and input shape pairs.
The matching error between descriptors, which is measured via the normalized L1 distance, is given by e r r ( R M i M j, R T k T s ) = ∑ n = 0 N R T k T s n − R M i M j n R M i M j n × 100%%, i, j ∈ [ 1, m ], i ≠ j ; k, s ∈ [ 1, t ], k ≠ s (9).
This diagnostic approach consist of 6 steps; 1 design 6 tasks of speaking diagnostic test 2 define attributes and survey student's speaking errors 3 develop descriptors and Q-matrix 4 diagnose English communicative competence of students 5) analyze data 6 provide feedback to teachers and students.
To reduce the likelihood of errors, precise descriptors were written for each code.
It is also important when constructing the model in the first place to examine the distribution of predictive error in the descriptor space.
Examination of the distribution of predictive errors across the descriptor space makes it clear that errors are consistently larger in some regions than in others – i.e., the predictive error is heteroskedastic (Fig. 4).
Doing so based on some analytical estimator would be preferable, but is impractical in most real-world situations because it requires detailed a priori knowledge of the global dependence of error on the descriptors.
Previous studies report the effect of location error on other descriptors of animal movement: fractal dimension [14], linear feature use [32] and resource selection [40], [41].
