Feature sensitivity analysis

The maxEnt approach suffers from the scale dependence problem. Nevertheless, perhaps a distribution may be judged to be better than not assigning a distribution. Consequences of using uniform or maxEnt distributions for different scales can be explored in a sensitivity analysis. An additional difficulty is that in order to apply the maxEnt approach, particular features of a distribution may need to be assumed known when those features may actually be substantially uncertain. 

Fig. 8.11. The classifier development process. Clinical knowledge provides us with a set of classes for supervised classification (top, right). Large numbers of spectra from large sample numbers are reduced to a set of potentially useful features (top, left) or metrics. A modified Bayesian algorithm operates on the metrics to provide predictions that are compared to a gold standard. The end result of the training and validation process is an optimized algorithm, metric set, calibration and validation statistics, and sensitivity analysis of the data...

Doubtlessly, there are also other computational analyses to be performed in the future, such as stochastic simulations, stoichiometric network analysis, sensitivity analysis, etc. However, for this initial survey, we only take this absolute minimum into account, while listing additional features of the respective software tools. 

If the rate sensitivity analysis is carried out over a non-isothermal oscillatory trace, then it is possible to see how certain reactions, in particular radical termination reactions and the reactions of water, become important at high temperatures and high conversions. The highest number of reactions is selected at the peak maximum where the temperature rises above 2000 K. All reactions required by the non-isothermal analysis, reactions 2-10, 13, 14, 16, 22-29, 35 and 45, were featured in one of the isothermal mechanisms. Therefore, it is possible to produce a reduced scheme for a non-isothermal simulation from an isothermal analysis provided the full... 

Generally, local concentration sensitivities are used for finding the parameters that have to be known with high precision, and for the identification of rate-limiting steps. An important achievement in the field of sensitivity analysis has been the introduction of principal component analysis as a method for the interpretation of the large amount of information contained in the sensitivity matrix. In the future, a more wide-spread application of principal component analysis is expected for the interpretation of concentration sensitivity results. This would help the extraction of further mechanistic details, or could be used for the detection of redundant reactions. The calculation of initial concentration sensitivities is very useful in some cases, but most simulation packages cannot calculate these sensitivities and there is no sign that such a feature will be incorporated. 

Built in PK, PBPK, and PD blocks, and features such as Monte Carlo and sensitivity analysis... 

Figure 2 presents the log-log plot of a r) versus t t) usually applied for showing the violation of the DSE relation. The FDSE exponent A = 0.77 0.02 is determined from the linear regression analysis. The inset shows the derivative of data from the main part of the plot. This distortion-sensitive analysis of data, do not applied so far, revealed a secret feature of results in the main part of the Fig. 2 the value of the exponent S changes on approaching the isotropic - nematic (I-N) transition. 

A more advanced feature in flowsheeting is the use of analysis tools for design or operation. For example, a sensitivity analysis can capture interrelations between different variables in the simulation problem. A more elaborate research may involve case studies. The capacity of simulation to imagine virtual experiments is a real benefit from which the user should know to take full profit. 

Up to now we have dealt with the sensitivity analysis of finite systems. Because of our interest in inorganic structural chemistry and heterogeneous catalysis, we should be able to account for the entire external potential in infinite systems. It will be demonstrated that EEM is particularly suitable for investigating the properties of inorganic crystals. Special features with respect to molecules interacting with surfaces will be stressed. 

Sensitivity analysis is also a tool that can help to refine potential energy functions for (bio)molecular simulations. Sensitivity analysis can help one decide whether a specific feature needs to be included in a potential function for describing a specified set of properties of a given class of molecules. For example, because point charge models are commonly used in bio(molecular) modeling, it is useful to inquire whether a dispersed charge representation would improve the description of intra- and intermolecular electrostatic interactions. One study of this type was carried out by Zhu and Wong," ° who included in the force field a squared Lorentzian function f r - f/ ) of the form... 

Force fields must be relatively simple and computationally efficient for studying complex macromolecules such as proteins and DNAs. The force fields usually describe properties of certain types better than others, depending on how the force fields were developed. We have already learned from the sensitivity analysis studies of liquid water and a two-dimensional square lattice model of protein folding that different system properties can be determined by different features of a potential model. 

To check if any active constraint is to be changed into a passive one, we can exploit ( ) the aforementioned feature of performing a sensitivity analysis of the solution by means of Lagrange multipliers against small constraint variations. 

Based on the historical data and on the sensitivity analysis results, an alternative CO conversion control loop having the following features arises ... 

ABSTRACT Several EU PAMINA project partners have applied different Sensitivity Analysis (SA) techniques to analytical models as well as to a simplified, though representative, PA model. The aims of the exercise were to investigate the performance of different techniques in the presence of features such as (lack of) linearity, (lack of) monotony, interactions, etc. to check the importance of sample size to compare different options within a given S A technique to cross-compare the results obtained using different teehniques and to get a better understanding of the rationale behind every available technique and about their capabilities and shortcomings. This paper presents and discusses some results on the tests of global sensitivity analysis performed with variance-based sensitivity indicators. 

A summary of advantages, limitations and outstanding issues is presented in Table 9.1. In the short term, it is likely that the use of larger detectors will provide a wider range of Q and thus improved sensitivity to both very small (< 1 nm) and larger (> 50 nm) features. Data analysis will be supported by improved models of the scattering processes, and higher incident fluxes... 

When we compare our va/we-based approach with other methods of sensitivity analysis, the following important features of the value method are distinguishing ... 

Strucmral Analysis provides all relevant structural responses based on the analysis models and the current set of DV. The Sensitivity Analysis calculates the first derivatives of aU responses with respect to the independent DV. A very important feature of MSC NASTRAN is the External Server, which allows the integration of user-defined design criteria described by Fortran routines. It therefore can be used to integrate various detailed design constraints, which are dependent on NASTRAN responses (stresses, displacements etc.). All detailed wing buckling... 

In the following, reaction flow analysis, sensitivity analysis and the directed relation graph method will be presented as static and dynamic reduction procedures. Thereafter will the main features of ILDM (including extensions such as flamelet generated manifolds (FGM) and reaction-diffusion manifolds (REDIM)), CSP and the LOI be discussed, including the fundamentals of the quasi steady state elimination procedure and the rate-controlled constrained equilibria (RCCE) approach. 

The applied model predictive algorithm has a few special features that make it more effective operates with constraints on manipulated variables and controlled variables a nonlinear form of the MPC algorithm was used to obtain feasible control performance the NLMPC controller was tuned according to the dynamic sensitivity analysis. 

To reduce the estimation error caused by the temperature measurement and parametric uncertainties, a robust observer using the sliding mode technique by considering the NH3 dynamics is designed. As the ammonia sensor is not crosssensitive against NOx, such a feature can be beneficial for the observer design. Also, based on the sensitivity analysis of the observer, the observer is robust to NOx sensor uncertainty, which is preferable especially when the NOx sensor crosssensitivity is not completely compensated by the EKF correction approach. 

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