Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Stable reduced-order models for discrete-time systems

The Routh-stability method is employed to reduce the order of discrete-time system transfer functions. It is shown that the Routh approximant is well suited to reduce both the denominator and the numerator polynomials, although alternative methods, such as PadÃ�Â(c)-Markov approximation, are also used to fit the model numerator coefficients.

A new approach to reduced-order modelling

An error polynomial is defined, the coefficients of which indicate the difference at any instant between a system and a model of lower order approximating the system. It is shown how Markov parameters and time series proportionals of the model can be matched with those of the system by setting error polynomial coefficients to zero. Also discussed is the way in which the error between system and model can be considered as being a filtered form of an error input function specified by means of model parameter selection.

Reduced complexity set-point following in self-tuning control

An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.

Origin of electronic absorption spectra of MLCT-excited and one-electron reduced 2,2′-bipyridine and 1,10-phenanthroline complexes

UV–Vis absorption spectra of one-electron reduction products and 3MLCT excited states of [ReICl(CO)3- (N,N)] (N,N = 2,20-bipyridine, bpy; 1,10-phenanthroline, phen) have been measured by low-temperature spectroelectrochemistry and UV–Vis transient absorption spectroscopy, respectively, and assigned by open-shell TD-DFT calculations. The characters of the electronic transitions are visualized and analyzed using electron density redistribution maps. It follows that reduced and excited states can be approximately formulated as [ReICl(CO)3(N,Nÿ)]ÿ and ⁄[ReIICl(CO)3(N,Nÿ)], respectively. UV–Vis spectra of the reduced complexes are dominated by IL transitions, plus weaker MLCT contributions. Excited-state spectra show an intense band in the UV region of 50% IL origin mixed with LMCT (bpy, 373 nm) or MLCT (phen, 307 nm) excitations. Because of the significant IL contribution, this spectral feature is akin to the principal IL band of the anions. In contrast, the excited-state visible spectral pattern arises from predominantly LMCT transitions, any resemblance with the reduced-state visible spectra being coincidental. The Re complexes studied herein are representatives of a broad class of metal a-diimines, for which similar spectroscopic behavior can be expected.

On rank, underdetermined systems and consistency

The concepts of rank, underdetermined systems and consistency in linear algebra are discussed in the context of a puzzle. The article begins with a specific example, moving on to a generalization of the example and then to the general n x n case. As well as providing a solution of the puzzle, the article aims to provide students with a greater understanding of these abstract ideas in linear algebra through the study of the puzzle.

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

Reduced height alleles (Rht) and Hagberg falling number of wheat

Near isogenic lines varying for alleles for reduced height (Rht) and photoperiod insensitivity (Ppd-D1) in cv. Mercia (2005/6 to 2010/11; rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht8c+Ppd-D1a, Rht-D1c, Rht12) and cvs Maris Huntsman and Maris Widgeon (2007/8 to 2010/11; rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht-B1b+Rht-D1b, Rht-D1b+Rht-B1c) were compared at one field site, but within different systems (‘organic’, O, 2005/6 to 2007/8 v ‘intensive’, I, 2005/6 to 2010/11). Further experiments at the site (2006/7 to 2008/9) compared 64 lines of a doubled haploid (DH) population [Savannah (Rht-D1b) × Renesansa (Rht-8c+Ppd-D1a)]. Gibberellin (GA) insensitive dwarfing alleles (Rht-B1b; Rht-B1c; Rht-D1b; Rht-D1c) could reduce α-amylase activity and/or increase Hagberg falling number (HFN) but effects depended greatly on system, background and season. Only Rht-B1c increased grain dormancy despite producing plants taller than Rht-D1c. The GA-sensitive Rht8c+Ppd-D1a in Mercia was associated with reduced HFN but analysis of the DH population suggested this was more closely linked with Ppd-D1a, rather than Rht8c. The severe GA-sensitive dwarfing allele Rht12 was associated with reduced HFN. Instability in HFN over season tended to increase with degree of dwarfing. There was a negative association between mean grain weight and HFN that was in addition to effects of Rht and Ppd-D1 allele.

Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.