Design of high performance linear feedback laws for operation that extends into the nonlinear region of AMB systems

Existing active magnetic bearings (AMBs) operate in the linear region of the magnetic material flux density, which limits the utilization of the bearing capacity. In order to increase the utilization of the bearing capacity and enhance the performance of the AMB system, this paper develops a method for designing high performance linear feedback laws. The resulting feedback laws allow the AMB to operate in its nonlinear region and hence improve the closed-loop performance. We first establish an approximate nonlinear AMB current force response model, and place this nonlinear curve inside a sector formed by two piecewise linear lines. Based on the linear line segments in these two piecewise linear lines, we determine the maximum disturbance that can be tolerated by solving an optimization problem with linear matrix inequality (LMI) constraints. For a given level of disturbance under the maximum tolerable disturbance, we formulate and solve the problem of designing the linear feedback that achieves the highest level of disturbance rejection as another LMI problem. Both $L_2$ disturbances and $L_\infty$ disturbances are considered. Finally, we illustrate our design by both simulation and experimental results.     

Nanoscale origins of small hysteresis and remnant strain in Bi0.5Na0.5TiO3-based lead-free ceramics

BiAlO₃-doped Bi_0.5Na_0.5TiO₃-Bi_0.5K_0.5TiO₃ (BA-doped BNT-BKT) ceramics are greatly concerned due to their sufficient electric-field-induced strain with small hysteresis and remnant strain for high precision positioning devices and other actuators. In this paper, the structural analysis especially the high-resolution transmission electron microscope (HRTEM) is used to reveal the origin of excellent properties obtained in 0.96(0.75BNT-0.25BKT)-0.04BA, which exhibits a large strain of 0.21% at ～70 kV/cm, a small strain hysteresis of only 24% and a near-zero remnant strain. Using HRTEM, the antiferroelectric nano-domains composited by three variants of in-phase a⁰a⁰c⁺ octahedral tilting coexisted with the remnant ferroelectric nano-domains of anti-phase a^?a^?a^? octahedral tilting are directly identified. Then a continuous tilting model is proposed to interpret the gradually transitional tilting involving nano-domains leading to the small hysteresis and near-zero remnant strain. The findings may pave a way for further optimizing the properties through creating stable antiferroelectric nano-domains in BNT-based ceramics and the analogues.     

Development of Solar Spectroradiometer for Meteorological Observation

A newgeneration of solar spectroradiometer has been developed by CUST/JRSI to improve solarirradiance observation data under hyperspectral resolution. It is bas...     

Effects of synthesis temperature on the microstructures and basic dyes adsorption of titanate nanotubes

The adsorption of two basic dyes (Basic Green 5 (BG5) and Basic Violet 10 (BV10)) onto titanate nanotubes (TNT) that were prepared via a hydrothermal method with different synthesis temperatures was studied to examine the potential of TNT for the removal of basic dyes from aqueous solution. Effects of synthesis temperature on the microstructures of TNT were characterized with transmission electron microscopy (TEM), X-ray diffraction (XRD), and nitrogen adsorption–desorption isotherms. For synthesis temperature greater than 160 °C, the microstructure of titanate might transform from nanotube into nanorod accompanying with the sharp decrease in the titanate interlayer spacing, BET surface area, and pore volume. Effects of the pore structure variation on the basic dyes adsorption of TNT were discussed. Moreover, the adsorption mechanisms of basic dyes from aqueous solution onto TNT were examined with the aid of model analyses of the adsorption equilibrium and kinetic data of BG5 and BV10. The regeneration of TNT was also briefly discussed.     

Integrating the Missing Information Estimation into Multi-frame Super-Resolution

Multi-frame super-resolution image reconstruction aims to restore a high-resolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback–Leibler divergence. In this way, the missing information is estimated simultaneously with the high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.     

基于改进卷积注意力模块与残差结构的SSD网络

SSD(Single Shot Multibox Detector)是一种基于卷积神经网络的单阶检测算法,相比双阶检测算法,它在保证一定精度的同时显著提高了检测速度,但仍难以满足很多实际应用,尤其是在小目标检测任务中,检测精度更是难以满足需求.针对该不足,文中提出了一种基于改进残差结构与卷积注意力模块的特征提取网络Re...     

改进YOLOv5s的复杂交通场景下目标检测算法

针对在实际的交通道路目标检测中,存在着小目标检测精度低,遮挡目标容易出现漏检误检等问题,提出了一种改进的YOLOv5s道路目标检测算法YOLOv5s-OEAG。将YOLOv5s的标签分配策略更换为效率更高的OTA标签分配策略,提高模型的检测精度与泛化能力;提出了一种轻量化的解耦预测头对不同尺寸的特征层进行分类任务与回归任务的解耦,提高模型对道路中小目标的检测能力;将原始模型中的最近邻插值上采样模块替换为轻量级通用上采样CARAFE模块,有助于更好地保留图像中的细节信息,提高模型的精度;提出了一种新的C3模块GMC3,在减小模型计算量的同时提高模型捕获特征的能力;为了提高模型的泛化能力,对KITTI数据集进行了扩充,增加了小目标的数量。实验结果表明,改进后的模型在经过扩充后的KITTI数据集的mAP达到了90.4%,比原始模型的精度提高了2.8%;FPS为75,满足实时性的要求,在一定程度上提高了对复杂交通场景的适应能力。     

融合实体与关系交互信息的知识感知推荐模型

由于知识图谱包含了丰富的项目属性及其关联信息,因此在推荐系统中引入知识图谱能在一定程度上解决数据稀疏和冷启动问题。如基于传播的推荐系统就利用了知识图谱的图结构学习用户及项目表示等相关特征。但在传播过程中,往往忽略了实体与关系之间的交互信息对特征表示的贡献,由此提出一种融合实体与关系交互信息的知识感知推荐模型。首先,将协同信息和知识关联整合,采用异构传播方式传播并扩展用户和项目的表示。其次,在传播过程中用注意力机制强化实体与关系之间的交互信息,增强语义关联,保证用户和项目基于知识的高阶交互的有效性。然后采用知识感知注意力机制来区分每层中实体邻居的重要性,更精确地生成用户和项目的表示。最后通过聚合器将多个表示结合得到用户和项目的最终表示,从而预测用户与项目进行交互的概率。通过添加KL散度损失函数对模型进行优化,以对齐模型的预测分布和真实分布之间的差异。在Last.FM、Book-Crossing和MovieLens-20M 3个数据集上进行的实验结果表明该模型在CTR预测性能中比其他基线模型有较大提升。