Cancellation of bulging effect on mould level in continuous casting: Experimental validation

During the continuous casting of steel, several disturbances occur and affect all the parts of the caster, including the mould where the molten steel has to be stable for good quality of the final product. Especially at high casting velocities, the bulging generates important level fluctuations which cannot be efficiently rejected by the classical controllers. To address these problems, this paper proposes an architecture combining disturbances estimation and compensation techniques. It operates as an additional module and does not need any change of the main controller currently implemented. The entire structure has been tested successfully through several simulations and by means of intensive water model experiments.     

A predictive bottom-up parser

Despite all advances in parsing, parser size, conflict resolution and error recovery are still of important consideration. In this research, we propose a predictive bottom-up parser. The parser is implemented in two versions. Both versions constitute an algorithm that simulates the run of a shift–reduce automaton, defined and constructed in a way that integrates its parsing actions with reduction prediction, conflict resolution and error recovery. However, the first implementation version performs explicit shift–reduce parsing actions based on implicit prediction of the reduction sequences. The second one performs parsing actions based on explicit prediction of the reduction sequences with implied shift–reduce actions. The proposed parser has been experimented against the ones based on similar approaches. 10–20% reduction of the parser size has been achieved, with a parsing behaviour proportional to a factor reflecting the grammar ambiguity.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Osmotic Dehydration Kinetics of Pomegranate Seeds Using Date Juice as an Immersion Solution Base

Pomegranate seeds were osmodehydrated using date juice added with sucrose (final °Brix, 55) as immersion solution. The kinetics of osmotic dehydration showed that the most significant changes of mass transfer took place during the first 20 min of the process, regardless of date juice varieties. During this time, seed water loss and solid gain were estimated to be ∼39% and ∼6%, respectively. After 20 min of the process, the percentage of water loss and solid gain varied slightly and ranged on average close to ∼40% and ∼9%, respectively. During osmotic dehydration, there was a leaching of natural solutes from seeds into the solution, which is quantitatively not negligible, and might have an important impact on the sensorial and nutritional value of seeds and date juices. Both scanning electron microscopy and texture (compression) analysis revealed that osmotic dehydration process induced modifications of seed texture and cell structure. Sucrose was found to be the essential element which influences the texture of seed and the viscosity of date juice. Additionally, natural sugar present in date juice permits substituting 35% of the total quantity of sucrose added to the osmotic solution.     

A new convolution approach for the time-delay identification of systems with arbitrary entries

In this paper, an online delay identification approach of continuous-time linear systems with unstructured entries is achieved via a new algebraic technique. The arbitrary input and output trajectories are chosen with close and abundant crossing zero. Initial conditions and static disturbances are taken into account in the design of the identification approach. The proposed method is based on a distributional algebraic technique and a convolution approach. A proposed theorem is hence enounced to identify a single time-delay of such systems. The effectiveness of the proposed approach is demonstrated by an illustrative example. The obtained results show the high performances of the proposed time-delay identification approach in severe operation conditions of the considered system.     

Utilisation of attenuated total reflectance MIR and front-face fluorescence spectroscopies for the identification of Saint-Nectaire cheeses varying by manufacturing conditions

Twelve samples of cheeses (three types of Saint-Nectaire PDO cheeses and Savaron cheeses) differing by manufacturing and ripening conditions, from 12 different producers, were characterised by attenuated total reflectance mid-infrared (MIR) and front-face fluorescence spectroscopies, dynamic testing rheology and physico-chemical analysis. Fluorescence spectra (tryptophan residues, vitamin A and riboflavin) and MIR (3,000–2,800 (fat region), 1,700–1,500 (protein region) and 1,500–900 cm⁻¹ (fingerprint region)) spectra were recorded on cheese samples. The potential of the data tables was investigated for discriminating the four different groups of cheeses. The results of factorial discriminant analysis (FDA) performed on the fluorescence and mid-infrared spectra showed a good discrimination of the four cheese groups. Considering cross-validation results, the best classifications (100%) were achieved from mid-infrared and fluorescence spectra, while only 91.7 and 72.2% of correct classification were obtained by applying FDA to rheology and physico-chemical data, respectively. Spectroscopic techniques could provide useful fingerprints and allow the identification of investigated cheeses according to manufacturing conditions. Simple and rapid spectroscopic methods offer a promising approach to the authentication of cheeses.     

Perception of temporally filtered X-ray fluoroscopy images

For noisy X-ray fluoroscopy image sequences we quantitatively evaluated image quality after digital temporal filtering to reduce noise. Using an experimental paradigm called a reference/test adaptive forced-choice method we compared detectability of stationary low-contrast disks in filtered and unfiltered, computer-generated image sequences. In the first experiment, a low-pass first-order recursive filter used in X-ray fluoroscopy was found to be much less effective at enhancing detectability than predicted from the reduction of display noise variance, a common measurement of filter effectiveness. Detectability was reasonably predicted by a nonprewhitening human-observer model (NPW-HVS) that included an independently determined human temporal-contrast-sensitivity function. In another experiment, designed to test models over a range of temporal frequencies, we used paired high-pass and low-pass temporal filters that both reduced noise variance by 25%. The high-pass filter was artificially applied to the noise only and greatly improved detectability, while the low-pass filter had little effect. The human-observer model quantitatively described the measurements, but classical prewhitening and nonprewhitening signal detectors did not. As compared to the nonprewhitening, spatio-temporal matched filter, human-observer efficiency was low and variable at 2.1%, 2.9%, and 0.06% for 60 frames of unfiltered low-pass and high-pass noise, respectively. As compared to this detector, humans were not very effective at combining information across frames. On the other hand, signal to noise ratios (SNR's) from the human-observer model were comparable to human performance, and efficiencies were reasonably constant at 40%, 52%, and 32%, respectively. We conclude that it is imperative to include human-observer models and experiments in the analysis of noise-reduction filtering of noisy image sequences, such as X-ray fluoroscopy.     

Detection improvement in spatially filtered x-ray fluoroscopy image sequences

The effect of spatial noise-reduction filtering on human observer detection of stationary cylinders mimicking arteries, catheters, and guide wires in x-ray fluoroscopy was investigated in both single image frames and image sequences. Ideal edge-preserving spatial filtering was simulated by filtering of the noise before addition of the target cylinder. This allowed us to separate the effect of edge blurring from those of noise reduction and spatial noise correlation. We used three different center-weighted averagers that reduced pixel noise variance by factors of 0.75, 0.50, and 0.25. As compared with no filtering, the effect of filtering on detection in single images was statistically insignificant. This indicated an adverse effect of spatial noise correlation on detection that countered the effect of noise reduction. By comparison, spatial filtering significantly improved detection in image sequences and yielded potential x-ray dose savings of 26-34%. Comparison of results with two observer models suggested that human observers have an improved detection efficiency in spatially filtered image sequences as compared with white-noise sequences. Pixel noise reduction, a measure commonly used to assess filter performance, overestimated the effect of filtering on detection and was not a good indicator of image quality. We conclude that edge-preserving spatial filtering is more effective in sequences than in single images and that such filtering can be used to improve image quality in noisy image sequences such as x-ray fluoroscopy.     

Prediction of the rheology parameters of ripened semi-hard cheeses using fluorescence spectra in the UV and visible ranges recorded at a young stage

Storage modulus (G′), loss modulus (G″), strain, tan (δ) and complex viscosity (η*) of 20 semi-hard cheeses were measured by dynamic oscillatory analysis after 2, 30 and 60 days of ripening. On the same cheeses and at the same ages, tryptophan and riboflavin fluorescence spectra were recorded. The aim was to predict the rheology parameters of ripened cheeses from spectra recorded on these cheeses at a young stage. Using partial least square, tryptophan fluorescence spectra recorded at 20 °C on 2-days-old cheeses predicted G′, G″, strain, tan (δ) and η* measured at 80 °C on the 60-days-old cheeses with correlation coefficients (R) of 0.98, 0.97, 0.98, 0.98 and 0.97, respectively. Riboflavin fluorescence spectra gave slightly lower correlation coefficients of 0.88, 0.88, 0.92, 0.87 and 0.88, respectively. Dependent only on visible light, the riboflavin fluorescence spectra potentially provide viable and economic prediction of the rheology of ripe cheese.     

Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study

The present work evaluated the ability of near infrared (NIR) spectroscopy in predicting some sensory attributes of 20 Emmental cheeses originating from different European regions. For the purpose of this study four appearance and texture attributes, namely, adhesivity, friability, elasticity and firmness and six olfacto-gustatory attributes namely, aroma intensity, odour intensity, bitterness, saltiness, acidity and sweetness were selected by the sensory panel. Calibration models between sensory properties and NIR spectra were developed using partial least squares (PLS) regression. The squared correlation coefficients (R²) were greater than 0.5 for adhesivity, elasticity, firmness, aroma, bitterness, saltiness, acidity and sweetness. In addition, a good correlation between sensory attributes and NIR spectra was found using canonical correlation analysis (CCA). Therefore, this work demonstrates the feasibility of NIR to predict some sensory attributes since a relatively high correlation between sensory data and NIR spectra was found. However, further research with a large data bases will be needed in order to validate the method.