Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Methodology for optimization of pouring variables using bottom pour for low masses of SS304 (<300 kg) in investment casting process (case study)

Bottom pour ladles with stopper rod systems are commonly used in the metal casting industry. However, stopper rod bottom-pouring systems have not yet been developed for the lower thermal masses of alloys typically used in the investment casting industry. Large thermal masses used with bottom pour systems are typically limited for ladles larger than 700 kg and to certain alloys with higher fluidity and longer solidification time like cast iron, aluminum alloys etc. In this study, bottom pour ladle designs and low thermal mass refractory systems have been developed and evaluated in production investment foundry trials with 300 kg pouring ladle. The ladles system and pouring practices used will be described along with the results from the pouring trials for SS304 that represents typical alloys used in Investment casting industries. Optimization of the variables used in an experimentation using Genetic algorithm is also explained.     

Temperature cycling and its effect on mechanical behaviours of highporosity chalks

Temperature history can have a significant effect on the strength of water-saturated chalk.In this study,hydrostatic stress cycles are applied to understand the mechanical response of chalk samples exposed to temperature cycling between each stress cycle,compared to the samples tested at a constant temperature.The total accumulated strain during a stress cycle and the irreversible strain are reported.Chalk samples from Kansas(USA)and Mons(Belgium),with different degrees of induration(i.e.amount of contact cementation),were used.The samples were saturated with equilibrated water(polar)and nonpolar Isopar H oil to quantify water weakening.All samples tested during 10 stress cycles with varying temperature(i.e.temperature cycled in between each stress cycle)accumulated more strain than those tested at constant temperatures.All the stress cycles were performed at 30℃.The two chalk types behaved similarly when saturated with Isopar H oil,but differently when saturated with water.When saturated with water,the stronger Kansas chalk accumulated more total strain and more irreversible strain within each stress cycle than the weaker Mons chalk.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Suitability,efficiency and microbiological safety of novel physical technologies for the processing of ready‐to‐eat meals,meats and pumpable products

Consumer studies and market reports show an increase in consumption of ready‐to‐eat (RTE) foods. Although conventional processing technologies can in most cases produce safe products, they can also lead to the degradation of nutritional compounds and negatively affect quality characteristics. Consumers strongly prefer food that is minimally processed with the maximum amount of health‐promoting substances. Novel processing technologies as pre‐ or post‐treatment decontamination methods or as substitutes of conventional technologies have the potential to produce foods that are safe, rich in nutrient content and with superior organoleptic properties. Combining novel with conventional processes can eliminate potential drawbacks of novel technologies. This review examines available scientific information and critically evaluates the suitability and efficiency of various novel thermal and nonthermal technologies in terms of microbial safety, quality as well as nutrient content on the production of RTE meals, meats and pumpable products.     

On‐the‐Spot Immobilization of Quantum Dots,Graphene Oxide,and Proteins via Hydrophobins

Class I hydrophobin Vmh2, a peculiar surface active and versatile fungal protein, is known to self‐assemble into chemically stable amphiphilic films, to be able to change wettability of surfaces, and to strongly adsorb other proteins. Herein, a fast, highly homogeneous and efficient glass functionalization by spontaneous self‐assembling of Vmh2 at liquid–solid interfaces is achieved (in 2 min). The Vmh2‐coated glass slides are proven to immobilize not only proteins but also nanomaterials such as graphene oxide (GO) and quantum dots (QDs). As models, bovine serum albumin labeled with Alexa 555 fluorophore, anti‐immunoglobulin G antibodies, and cadmium telluride QDs are patterned in a microarray fashion in order to demonstrate functionality, reproducibility, and versatility of the proposed substrate. Additionally, a GO layer is effectively and homogeneously self‐assembled onto the studied functionalized surface. This approach offers a quick and simple alternative to immobilize nanomaterials and proteins, which is appealing for new bioanalytical and nanobioenabled applications.     

A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

A Constitutive Equation of Thermoelasticity for Solid Materials

A new constitutive equation of thermoelasticity for crystals is presented based on the interatomic potential and solid mechanics at finite temperature. Using the new constitutive equation, the calculations for crystal copper and graphene are carried out under different loading paths at different temperatures. The calculated results are in good agreement with those of the previous thermoelasticity constitutive equation based on quantum mechanics, which clearly indicates that our new constitutive equation of thermoelasticity is correct. A lot of comparisons also show that the present theory is more concise and efficient than the previous thermal stress theory in the practical application.     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.