INDEX robust performance

The chapter is organized as follows In section 2, we first review basic results on I/O-controllability of linear systems. In section 3, a new t)fpe of I/O-controllability index is introduced, the Robust Performance Number. In section 4, I/O-controllability analysis by optimization is presented. Sections 5 and 6 contain two case studies an air separation plant and a reactive distillation column where these tools are applied to select the best control structure and quantify the process I/O-controllability. The evaluations of the control structures are validated by simulations with low order controllers which can easily be obtained from the analysis, in particular from the computed or estimated attainable performance of the chosen structure, using the procedure described in [9, 42, 29]. So the construction of practically relevant controllers of minimal complexity is seamlessly integrated with the analysis. 

The WDMSPR sensor approach offers the benefit of multichannel performance without increasing complexity and costs of the sensor system. In addition, the WDMSPR sensors make it possible to discriminate effects occurring in the proximity of sensor surface (specific binding, non-specific adsorption) from those occurring in the whole medium (interfering background refractive index fluctuations) which is a prerequisite for robust referencing ". ... 

Before discussing SCS, we give a brief review of related research on the implementation of Costa s scheme. Chen and Womell developed in 1998 quantization index modulation (QIM) which provides good performance for low channel noise, but is not robust for channel conditions with a 1 > cr [10], [1], In 1999, they improved the QIM idea using Costa s approach and named the new scheme QIM with distortion compensation (DC-QIM) [11], Most of the work of Chen and Womell concentrates on high... 

In general, it is desired to make the pilot sequence as short as possible, however, very short pilot sequences lead to an inaccurate PDF estimation, and thus to incorrect estimations of Ar and r0nS(H. Fig. 19 shows the estimation performance for Lpii0t = 250,500,1000, and 2000. Fig. 19.(a) depicts 5at which describes the relative estimation error of Ar. For Lpoot = 2000, 8at decreases monotonically with increasing WNR, and is lower than 1% for WNR > —3 dB. Shorter pilot sequences lead to an increased relative estimation error. However, for some WNR, robust estimation is no longer possible at all. Lowering the WNR further introduces so much noise into the PDF estimation that the largest component of the computed DFT spectrum appears at any random frequency index 0 < l < Ldft — 1. 

Properties of Porous Silicon, and Refractive Index of Porous Silicon ). Following the very first studies (Barla et al 1984 Drory et al. 1990), there have been continued but infrequent assessments (Da Fonesca et al 1995 Fang et al 2008 Rahmoun et al 2009 Aliev et al 2011 ). Nonetheless, there is a growing realization that robust porous silicon device and system performance will ultimately rely on achieving the appropriate mechanical properties, in conjunction with the novel functionality achieved through nanostructuring. 

In the last twenty years, various non-deterministic methods have been developed to deal with optimum design under environmental uncertainties. These methods can be classified into two main branches, namely reliability-based methods and robust-based methods. The reliability methods, based on the known probabiUty distribution of the random parameters, estimate the probability distribution of the system s response, and are predominantly used for risk analysis by computing the probability of system failure. However, variation is not minimized in reliability approaches (Siddall, 1984) because they concentrate on rare events at the tail of the probability distribution (Doltsinis and Kang, 2004). The robust design methods are commonly based on multiobjective minimization problems. The are commonly indicated as Multiple Objective Robust Optimization (MORO) and find a set of optimal solutions that optimise a performance index in terms of mean value and, at the same time, minimize its resulting dispersion due to input parameters uncertainty. The final solution is less sensitive to the parameters variation but eventually maintains feasibility with regards probabilistic constraints. This is achieved by the optimization of the design vector in order to make the performance minimally sensitive to the various causes of variation. 

The analytical methods proposed for acesulfame-K, cyciamate, and saccharin determination in foods, drinks, dietary products, and pharmaceuticals can be grouped into methods for the determination of an individual artificial sweetener [21-27] and multianalyte approaches [28-38], sometimes also including other sweeteners and/or other food additives, such as colorants or preservatives [39-43]. High-performance liquid chromatography (HPLC) is the most frequently used technique for the determination of these sweeteners, and this is selected by international standard methods because of its multianalyte capability, compatibility with the physicochemical properties of sweeteners, high sensitivity, and robustness [44-47]. However, cyciamate requires chemical derivatization to make it detectable by the most commonly employed UV-absorption detector due to a lack of a chromophore, by conversion to dichlorohexylamine for UV detection or to a fluorescence derivative for fluorimetric detection. Another alternative for cyciamate detection is the postcolumn ion-pair extraction where the eluted sweetener is mixed with an appropriate dye (methyl violet or crystal violet), being detected by visible absorption. Furthermore, cyciamate can be detected directly by refractive index [4]. For this, few HPLC methods for the concurrent determination of these sweeteners exist and... 

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