Robustness analysis

Robust analysis phase The construction of Catalysis business and requirements models covers more than in the more conventional style. In Catalysis, more of the important decisions are pinned down. As a result, there is less work later in the design stage and less work over the maintenance part of the life cycle, the part that accounts for most of a software system s cost. (To cope with any uncomfortable feeling of risk that this approach may generate, see the remarks in Sections 1.11.2 and 1.11.3.)... 

Verboven, S., Hubert, M. Chemom. Intell. Lab. Syst. 75, 2005, 127-136. LIBRA A MATLAB library for robust analysis. 

Before we discuss these two methods of robustness analysis, let us consider the general relationship between performance and robustness. 

Considering risk in terms of variations in both projected benefits and recourse variables provided a more robust analysis of the problem. As explained earlier, the problem will have a more robust solution as the results will remain close to optimal for all given scenarios through minimizing the variations of the projected benefit. On the other hand, the model will be more robust as minimizing the variations in the recourse variables leads to a model that is almost feasible for all the scenarios... 

Such data are shown in Table 3 and Fig. 6. Upper and lower control limits are calculated based upon n = 2 and A2 = 1.880. Thus, for 10 lots there will be 9 data points to plot, which results in a robust analysis of the quality control data for the product. Unlike a normal control chart, when you decide to use RSD values to create the quality control chart, the lower control limit (LCL) is more desirable than the upper control limit (UCL) simply because lower RSD values reflex a tighter dispersion around the mean. 

The most robust analysis methods involve direct comparison of the AI (q) term with theoretical computations. Agreement between data and prediction then validate the models used. The application of inversion approaches involves taking Fourier transforms of the data to yield the set of vectors connecting scattering particles. However, one must be cautious when interpreting the results of these inversions. Experiments are currently underway to measure the Fj2ons term independently, which will allow us to extract the pure ion—DNA cross-term which is more straightforward to interpret. 

Ramsey, M. H., Thompson, M., and Hale, M. (1992). Objective evaluation of precision requirements for geochemical analysis using robust analysis of variance. J. Geochem. Explor. 44, 23—36. 

MATLAB functions for all of the procedures mentioned in this chapter are part of LIBRA, Library for Robust Analysis [81], which can be downloaded from http //www.wis.kuleuven.be/stat/robust.html. 

Verboven, S. and Hubert, M., LIBRA A MATLAB Library for Robust Analysis, Chemometrics and Intelligent Lab. Syst., 75, 127-136, 2005. 

In a companion paper (Kleijnen et al., 2003), we changed the metamodel in (1) after the screening phase, as follows. For those controllable factors found to be important by sequential bifurcation, we augmented (1) with quadratic effects to form a predictive model for optimization. For those environmental or noise factors identified by sequential bifurcation as important, we created environmental scenarios through Latin hypercube sampling for robustness analysis. 

Because of the much larger molecular weights of ILs relative to common organic solvents, the use of mole fraction data to compare the solubility of H S in ILs relative to the solubility of H S in common organic (physical) solvents does not provide a sufficiently robust analysis [6, 15], A more appropriate comparison of H S solubility in organic solvents to ILs must take into account solubility of H S per volume of the solvent [6, 15], Equation 6.1 provides the most commonly used expression for volume-based solubility (S). 

EDXRF is well suited for the on-line measurement of sulfur content for less dense oils that are pumped at low pressures, typically at a maximum of 30 psig. Refineries, pipelines, and blending operations, however, require on-line analysis of thick, dense, viscous crude oil pumped at high pressure. These environments require a robust analysis technique with instrumentation capable of accurately measuring crude oil pumped at a pressure of 800 psig. XRF is not desirable for such an analysis. 

Human factors - A robust analysis is ultimately about how people deliver care and not necessarily just about how the tools behave. If you ask people to draw a business process they ll describe data flow, states and logical decision points. Ask someone to draw a storyboard and they show people, interactions and context. It s defining and capturing this latter information that makes for a good hazard description. 

Efficient frontiers also invariably place Treasury bills as the risk-free asset. T-bills may be risk-free from a creditworthiness point of view, bnt it is not tenable that a three-month nominal asset is a risk-free instrn-ment for someone with, say, a 30-year savings horizon. If you are investing for 30 years, over which time you are interested in your prospective real returns, then a 30-year linker (to be held to maturity) is your riskfree asset, almost by definition. 100% invested in that bond becomes the lowest risk portfolio on yonr frontier. Efficient frontier analysis starts to lose its impact once this premise is accepted, not least because you do not have a large data sample of consecutive, nonoverlapping 30-year periods (for any asset) to produce robust analysis. ... 

As ouflined earber, sample preparation is a key aspect for the robust analysis of NAs by MALDl-TOF-MS. The challenge of sample preparation can be subdivided into three categories ... 

Which enzymatic reaction generates NA products of a size (and molecular mass) suitable for robust analysis by MALDI-TOF-MS. ... 

The cornerstone of a reporting system is robust analysis of theme and detection of patterns within the data. After the sharp-end worker shares the narrative story about what happened, a case analysis is conducted and deidentified. The case analysis provides a window into the system to view error-producing conditions (Vincent, 2001). The analysis should provide a diagnosis of the problems in a system. A case analysis, performed for accidents, potential accidents, and near misses, accomplishes two goals ... 

The purpose of data analysis is to promote learning and offer preliminary recommendations. The goal is a robust analysis that focuses on detecting meaningful patterns and identifying new themes. As the system matures, data can be augmented with reports linked to a data warehouse. 

Zhang QR, Teng H, Sun YQ, Xiu ZL, Zeng AP. (2008). Metabolic flux and robustness analysis of glycerol metabolism in Klebsiella pneumoniae. Bioproc Biosyst Eng, 31, 127-135. 

The field of application of this method is not only robustness analysis but also controller design. In this case the set of stabilizing controller parameters is determined. All controllers from this set stabilize the plant, thus, allowing to incorporate further design criteria to select the final controller. The task is to determine a controller which robustly F-stabilizes the system for the entire operating domain. 

---