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.     

埋地管道泄漏内封堵装置设计与研究

针对流体输送埋地管道泄漏问题,设计了一种利用管道机器人携带封堵气囊进行快速应急封堵修复的埋地管道泄漏内封堵装置。采用矩阵变换方法建立了牵引系统驱动轮过弯方程,利用MATLAB软件对过弯方程进行了验证,同时利用ADAMS软件仿真分析过弯路径与驱动轮转角对牵引系统行走速度的影响。研究结果表明:由两个串联封堵器组成的应急封堵系统可满足复杂工况下的管道泄漏封堵要求;牵引系统驱动轮在弯管内部行走时,单轮速度呈周期性变化,但三个驱动轮整体周期运动特性一致;驱动轮转角在25°~40°时,牵引系统行走速度与驱动轮转角成正比,且转角为30°时驱动效果最好。该内封堵装置的结构设计可为管道泄漏应急封堵领域装备的研发提供重要参考。     

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.     

Modified embedded-atom method interatomic potential for the Mg–Zn–Ca ternary system

Mg–Zn–Ca alloys are representative Mg alloys with high formability at room temperature. Their high formability is thought to be related to slip, twinning, and recrystallization of the alloys, but the detailed mechanisms have not yet been clarified. To enable atomistic simulations for investigating those behaviors, an interatomic potential for the Mg–Zn–Ca ternary system was developed. The development was based on the second nearest-neighbor modified embedded-atom method formalism, combining previously developed Mg–Zn and Mg–Ca potentials with the newly developed Zn–Ca binary potential. The Zn–Ca and Mg–Zn–Ca potentials reproduce structural, elastic, and thermodynamic properties of compounds and solution phases of relevant alloy systems in reasonable agreement with experimental data, first-principles and CALPHAD calculations. The applicability of the developed potentials is demonstrated through calculations of the effects of Zn and Ca solutes on the generalized stacking fault energy for various slip systems, segregation energy on twin boundaries, and volumetric misfit strain.     

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.