In this study a Hybrid Ground Coupled Heat Pump (HyGCHP) system with MPC is investigated, for which state estimation and disturbance prediction are highly uncertain, moreover, the system performance is highly sensitive to errors at these points.
The current paper provides analysis of these situations, and shows that the performance of a mount with a symmetric stiffness displacement relationship is highly sensitive to errors in the static loading.
In a first experiment, using the EAT, O'Connell and colleagues were able to demonstrate that the skin-conductance response (SCR) to subjects' reactions, another correlate of (mostly sympathetic) ANS activity, is highly sensitive to subjective error awareness: The SCR was enlarged towards errors (in comparison to corrects), yet this only held true for consciously perceived errors.
This approach is known to be highly sensitive to genotyping error (Mitchell et al. 2003; Paterson et al. 2009).
For the satellite system considered here, the orthogonality calculation is highly sensitive to experimental errors, so a set of noisy mode shapes has a small probability of passing the orthogonality criteria for some of the TAMs.
Since a single FRF estimate is highly sensitive to measurement errors of input/output signals, the mean averaging of repeatedly observed FRF estimates is employed in most of the practical applications.
Measurements were affected by the presence of diffusive boundary layers and fouling, and procedures designed to eliminate the effects of diffusive boundary layers and fouling were highly sensitive to analytical errors and outlying data points.
Moreover, the interference suppression is highly sensitive to small errors in the weights applied to each of the antenna elements [30].
However, a complete mathematical description of the model is usually extremely complex to arrive at, and generally the systems are highly sensitive to modeling errors.
Noise in the experimental data makes perfect multicollinearity unlikely in practice and usually an inverse can be computed, though it may be inaccurate and highly sensitive to small errors in the data and small changes in the model.
CL-rules are theoretically stable for x0 = 0, but ISO learning is highly sensitive to numerical errors which can easily destroy convergence.
