A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

Optical and thermo-mechanical properties of fine-grained transparent yttria ceramics fabricated by hot-press sintering for infrared window applications

In this work, highly transparent yttria ceramics Φ?=?55?mm in size were fabricated by a hot-pressing method with 1 at.% ZrO₂ or 12 at.% La₂O₃ as a sintering additive. For a 4-mm-thick specimen doped with ZrO₂, the in-line transmittance reaches 71.1% at 400?nm and 80.9% at 1100?nm, and the transmittance of the La₂O₃-doped specimen is comparable to that of the ZrO₂-doped specimen. By means of the relatively low sintering temperature of 1600?°C, the present samples exhibited very fine microstructures (<2?μm), giving rise to excellent mechanical strength levels (～200?MPa). With regard to the 1 at.% ZrO₂-doped specimen, the combination of high strength and high thermal conductivity (～10 W/m?K) substantially improved parameters related to the thermal shock resistance. The results of this study indicate that the hot-pressed transparent yttria ceramic doped with 1 at.% ZrO₂ is optically, mechanically, and thermally suitable for high-temperature IR window applications.     

Supercharged Proteins and Polypeptides

Electrostatic interactions play a vital role in nature. Biomacromolecules such as proteins are orchestrated by electrostatics, among other intermolecular forces, to assemble and organize biochemistry. Natural proteins with a high net charge exist in a folded state or are unstructured and can be an inspiration for scientists to artificially supercharge other protein entities. Recent findings show that supercharging proteins allows for control of their properties such as temperature resistance and catalytic activity. One elegant method to transfer the favorable properties of supercharged proteins to other proteins is the fabrication of fusions. Genetically engineered, supercharged unstructured polypeptides (SUPs) are just one promising fusion tool. SUPs can also be complexed with artificial entities to yield thermotropic and lyotropic liquid crystals and liquids. These architectures represent novel bulk materials that are sensitive to external stimuli. Interestingly, SUPs undergo fluid–fluid phase separation to form coacervates. These coacervates can even be directly generated in living cells or can be combined with dissipative fiber assemblies that induce life-like features. Supercharged proteins and SUPs are developed into exciting classes of materials. Their synthesis, structures, and properties are summarized. Moreover, potential applications are highlighted and challenges are discussed.     

Novel fuzzy adaptive finite-time nonsmooth control for uncertain nonlinear systems

In this article, a novel fuzzy adaptive finite-time nonsmooth controller is developed to handle the finite-time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite-time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite-time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.     

Adaptive global stabilization of chained-form systems with multiple disturbance and strong nonlinear drifts

This article investigates the stabilization of chained-form nonholonomic systems with strong drifts, multidisturbances, and unknown constant parameters. The disturbances include the matched disturbance with bounded variation and the unknown time-varying unmatched disturbance. A nonlinear disturbance observer is skillfully constructed to evaluate the matched disturbance and a disturbance estimation is used in the virtual controls to compensate the unmatched disturbance. By using a new input-to-state scaling scheme, the original nonholonomic system is transformed into a strict feedback form. Combining back-stepping technique with disturbance observer-based control (DOBC), a composite DOBC and global adaptive stabilization controller is proposed. A switching strategy based on control input magnitude instead of time is proposed to avoid uncontrollability. By using Lyapunov tools, all the states in the system are proved to be uniformly ultimate bounded. Simulations demonstrate the effectiveness of the proposed strategies.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Harnessing adaptive capacity to close the pandora’s box of open innovation

ABSTRACT

This paper focuses on how companies deploy structural properties of adaptive capacity (multiplexity, redundancy and loose coupling) to cope with the phenomenon of internal challenges of Open Innovation (OI) implementation, i.e. organisational and cultural changes. We developed a single case study, which offered significant findings. First, the multiplexity of relationships in OI settings helps to face the challenges of organisational and cultural changes by triggering trust formation and interaction. Second, redundancy has twofold elements: institutional logics redundancy triggers cultural change challenges which can be overcome through management practices and communications; task redundancy facilitates knowledge sharing and collaboration, thus helping to deal with organisational change challenges. Finally, loose coupling in OI settings facilitates social interconnectedness among members through management practices, helping to face organisational and cultural change challenges. We provide managerial and theoretical implication to deal with the challenges of OI in relation to both organisational and cultural changes.     

基于机器视觉的手机电池表面缺陷检测

针对手机电池表面质量人工检测情况,开发了电池表面缺陷无损检测系统软件。首先电池表面经过倾斜矫正、感兴趣区域提取和字符灰度值修改等预处理操作,通过基于灰度密度分布和灰度差的自适应阈值亮度法对感兴趣区域进行子图像遍历,融合有重合区域的缺陷子图像并滤除没有明显缺陷的区域;然后采用支持向量机多种类分类法,提取二值图像像素分布规律作为训练特征,识别电池表面缺陷种类;最后设计了人机交互界面,确定最佳的可变参数,实验测试缺陷识别率达95%以上。     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.