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.     

Disappearance and recovery of colossal permittivity in (Nb+Mn) co-doped TiO2

We investigate the effects of doping and annealing on the dielectric properties of metal ions doped TiO₂ ceramics. Colossal permittivity (CP) above 10⁴ was observed in single Nb ion doped TiO₂, which was dominated by electron transport related interfacial polarization. Moreover, the CP can be dropped to 120 when simultaneously introducing Mn ion into the sample. The disappearance of CP behaviors maybe due to the multivalence of Mn which would inhibit the reduction of Ti⁴⁺ to Ti³⁺, and thus reduce delocalized electrons. Interestingly, the CP was recovered for the (Nb+Mn) co-doped TiO₂ after post-sintering heat treatment in N₂ atmosphere. The recovery of CP in the sample after annealing can be ascribed to the semiconducting grain and the insulating grain boundary, according to impedance spectroscopy. We therefore believe that this work can help us understand the mechanism of CP from a new perspective.     

Eu@C86 isomers: Calculated relative populations

Abstract

Relative populations of four energy-lowest IPR (isolated-pentagon-rule) isomers of Eu@C₈₆ are computed using the Gibbs energy based on characteristics from density functional theory calculations (M06-2X/3-21G?～?SDD entropy term, M06-2X/6-31G*～SDD or B2PLYP(D)/6-31G*～SDD energetics). The calculations confirm that the recently isolated Eu@C₁(7)-C₈₆ species is a major isomer in a relevant temperature region. Relationship to the empty C₈₆ cages is discussed, too.     

Effects of Cooling Rate and Component on the Microstructure and Mechanical Properties of Mg–Zn–Y Alloys

The microstructure and mechanical properties of Mg–6Zn–1Y and Mg–6Zn–3Y(wt%) alloys under different cooling rates were investigated. The results show that the second dendrite arm spacing(SDAS) of Mg–6Zn–1Y and Mg–6Zn–3Y is reduced by 32 and 30% with increasing cooling rates(Rc) from 10.2 to 23 K/s, which can be predicted using a empirical model of SDAS=68 R 0:45:45cand SDAS=73 R 0c, respectively. The compressive strength of both alloys increases with increasing the cooling rate, which is attributed to the increase of volume fraction(Vf) of secondary phases under high cooling rate. The interaction of the cooling rate and component with SDAS has been theoretically analyzed using interdependence theory.     

Multicellular Tumor Spheroids (MCTS) as a 3D In Vitro Evaluation Tool of Nanoparticles

Multicellular tumor spheroid models (MCTS) are often coined as 3D in vitro models that can mimic the microenvironment of tissues. MCTS have gained increasing interest in the nano‐biotechnology field as they can provide easily accessible information on the performance of nanoparticles without using animal models. Considering that many countries have put restrictions on animals testing, which will only tighten in the future as seen by the recent developments in the Netherlands, 3D models will become an even more valuable tool. Here, an overview on MCTS is provided, focusing on their use in cancer research as most nanoparticles are tested in MCTS for treatment of primary tumors. Thereafter, various types of nanoparticles—from self‐assembled block copolymers to inorganic nanoparticles, are discussed. A range of physicochemical parameters including the size, shape, surface chemistry, ligands attachment, stability, and stiffness are found to influence nanoparticles in MCTS. Some of these studies are complemented by animal studies confirming that lessons from MCTS can in part predict the behaviour in vivo. In summary, MCTS are suitable models to gain additional information on nanoparticles. While not being able to replace in vivo studies, they can bridge the gap between traditional 2D in vitro studies and in vivo models.     

General Decay Synchronization and H ∞ Synchronization of Multi-weighted Coupled Reaction-diffusion Neural Networks

This paper investigates the general decay synchronization and general decay H∞ synchronization problems of multi-weighted coupled reaction-diffusion     

海面油膜的偏振反射特性仿真实验

为了给海面溢油污染识别检测提供理论基础,根据菲涅尔反射公式的偏振反射系数,结合偏振双向反射率因子和粗糙海面的概率密度分布函数,建立了完善的pBRDF模型,仿真在不同太阳入射角度、不同探测器观测角度以及不同海面风速风向等条件下海水和油膜的偏振反射分布函数。结果表明:海水和油膜太阳天顶角为53°和56°时P偏振反射率分别为1.0×10^-5和2.5×10^-5,S偏振反射率随着太阳天顶角的增加而增加;海面风速越大偏振反射率峰值越小;海面风向只改变pBRDF的空间位置;海水和油膜线偏振度空间分布有明显差异。搭建实验平台相机以40°拍摄时,得出海水和油膜的线偏振度分别在0.2~0.4, 0.5~0.7之间,同时表明利用偏振探测获取目标场景的偏振度和偏振角图可提高图像质量。     

基于图像增强的去雾快速算法的FPGA实现

基于图像增强方法，本文提出了一种使用亮度映射的图像去雾快速算法。此算法通过调整室外多雾场景图像的对比度，提高了雾中物体的辨识度。算法的复杂度低、处理延迟小，实时性高，利于FPGA的实现。实现时不需外存储器，延时为ns级，并提供了强度调节接口，以适应较广的应用环境。