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.     

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.     

Quantitative image quality analysis of a nonlinear spatio-temporalfilter

Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.     

Point-matching method for reduction of anomalous radiation of log-periodic dipole array

A point matching method is developed for the evaluation of the performance of the log-periodic dipole antenna (LPDA. Based on this method, the anomalous behaviour of the LPDA is studied and found to be improved at an optimum spacing factor and by selecting a small dipole length to diameter ratio; that is, an LPDA, operating over a given frequency band, must possess an optimum dimension that gives a pattern with eliminated anomalous backlobe peaks.<>     

Predicting the number of contacts and dimensions of full-customintegrated circuit blocks using neural network techniques

Block layout dimension prediction is an important activity in many very large scale integration computer-aided design tasks, among them structural synthesis, floor planning and physical synthesis. Block layout dimension prediction is harder than block area prediction and has been previously considered to be intractable. The authors present a solution to this problem using a neural network machine learning approach. The method uses a neural network to predict first the number of contacts; then another neural network uses this prediction and other circuit features to predict the width and the height of its layout. The approach has produced much better results than those published-a dimension (aspect ratio) prediction average error of less than 18% with a corresponding area prediction average error of less than 15%. Furthermore, the technique predicts the number of contacts in a circuit with less than 4% error on average.     

Image analysis study of the perimysial connective network,and its relationship with tenderness and composition of bovine meat

Image processing method was developed to predict beef tenderness, collagen and lipids contents. The study was carried out on the semimembranosus muscle (SM). Images of SM slices were acquired under visible and ultraviolet lighting. In this work statistical technique was implemented as a method to relate the distribution of intramuscular connective tissue (IMCT), characterized by image analysis, to sensory tenderness evaluated by a trained panel and collagen and total lipids contents assessed chemically. Using Multiple Linear Regression (MLR) combining visible and ultraviolet lighting, IMCT image parameters were found to be good predictors of beef tenderness (R² = 0.89), collagen and lipids contents (respectively R² = 0.82 and R² = 0.91).     

Misalignment controller in wireless battery charger for electric vehicle based on MPPT method and metaheuristic algorithm

This paper focuses on applying a wireless power transfer WPT technology in an electric vehicle battery charger. The wireless charger system is based on the principle of resonance inductive coupling power through a coreless transformer. The WPT system is considered as a multi parameter and multi constrained nonlinear system. The main contribution in this paper is the use of PSO and GA metaheuristic algorithms in the optimization of a transformer design regarding the impact of a lateral misalignment and the separation distance between the primary and secondary coils. To find the best global solution which is considered as the maximum efficiency in the complex system, both algorithms are compared. A perturbation-and-observation-based tracking system is developed through an efficiency sensing system to act on the misalignment issue and the car position. An additional PSO controller is performed to control the duty cycle of the boost converter in order to follows the maximum efficiency operating points of a WPT system. The discrepancy is the use of the resonant inductive coupling as a source of the MPPT so as to perturb the car position and observe the transferred. Furthermore, the modeling of a contactless transformer is optimized using metaheuristic algorithm.     

Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH_0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH_{0 PPC} had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH_{0 PPC} and CY or coagulation traits were weak (?0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.