矢量语音传感器微阵列

人工智能的普及促进了语音交互技术的发展,语音传感器阵列作为智能语音交互的硬件前端,成为语音交互领域的前沿研究方向.矢量语音传声器自有的偶极子指向性、零点深度以及阵列体积小便于集成的特点特别符合语音交互技术对硬件设备的要求.基于此,通过采用两组矢量敏感单元"共点正交"形成矢量微阵列实现声源空间锐化波束指向,其不受瑞利限与空间采样率限制,与传统空间离散分布的声压麦克风阵列有着本质区别,是矢量微阵列的核心优势所在.矢量微阵列传声器弥补了现有双麦阵列的不足,具有更为广阔的应用前景,作为智能语音交互的硬件前端,对推动智能语音交互领域的发展具有重要意义.     

基于蜻蜓翅脉结构的连续碳纤维增强树脂基复合材料仿生设计与增材制造

以具有轻质高强优异性能的蜻蜓翅脉结构为设计灵感，在分析翅脉网格结构抗冲击原理的基础上，设计了传统和仿生两类对比结构。采用熔融挤出3D打印机成功制备了具有不同结构的连续碳纤维增强聚乳酸复合材料试样，并对不同结构复合材料试样的拉伸性能和抗冲击性能进行了测试和对比分析。研究分析结果表明：由于拉伸力方向上的连续碳纤维含量相对较少，限制了仿生结构复合材料抗拉强度的提高，但仿生结构的平均抗拉强度为传统结构的1.18倍；当仿生结构复合材料试样受到冲击力时，其内部六边形结构的连接角度会发生变化，从而极大消耗冲击能量，同时具有六边形网格结构的连续碳纤维可以有效阻碍裂纹的扩展，因此仿生结构的平均冲击韧性可以达到传统结构的2.46倍；仿生蜻蜓翅脉结构可以显著提高增材制造复合材料的综合力学性能，且对于抗冲击性能的提高具体突出效果。连续碳纤维增强树脂基复合材料的有效可行的仿生蜻蜓翅脉结构设计和增材制造，可极大扩展其在高冲击载荷领域中的相应应用。     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

P-doped CoSe2 nanoparticles embedded in 3D honeycomb-like carbon network for long cycle-life Na-ion batteries

We report for the first time a Na-ion battery anode material composed of P-doped CoSe2 nanoparticles(P-CoSe2)with the size of 5-20 nm that are uniformly embed in a 3D porous honeycomb-like carbon network.High rate capability and cycling stability are achieved simultaneously.The honeycomb-like carbon network is rationally designed to support high electrical conductivity,rapid Na-ion diffusion as well as the accommodation of the volume expansion from the active P-CoSe2 nanoparticles.In particular,heteroatom P-doping within CoSe2 introduces stronger P-Co bonds and additional P-Se bonds that signif-icantly improve the structure stability of P-CoSe2 for highly stable sodiation/desodiation over long-term cycling.P-doping also improves the electrical conductivity of the CoSe2 nanoparticles,leading to highly elevated electrochemical kinetics to deliver high specific capacities at high current densities.Benefiting from the unique nanostructure and atomic-level P-doping,the P-CoSe2(2∶1)/C anode delivers an excel-lent cycle stability with a specific capacity of 206.9 mA h g-1 achieved at 2000 mA g-1 after 1000 cycles.In addition,this material can be synthesized using a facile pyrolysis and selenization/phosphorization approach.This study provides new opportunities of heteroatom doping as an effective method to improve the cycling stability of Na-ion anode materials.     

Attitude dynamics of coupled spacecraft undergoing fuel transfer

Multibody System Dynamics - This paper represents an explicit analytical solution for attitude dynamics of spacecraft combination during on-orbit refueling. Due to the fuel transfer from the...     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.     

某进口固体废物“铁精粉”的鉴别方法探析

为了确保不进口污染环境的固体废物，针对某冒充铁精粉的进口固体废物，通过外观、成分、物相分析进行了系统的鉴别。确定样品主要为经研磨加工的氧化铁皮，属于禁止进口的固体废物——轧钢产生的氧化皮。     

小像元红外探测器读出电路设计研究

介绍了基于SMIC 0.18 μm 3.3 V工艺设计研究的第一款小像元红外探测器读出电路,间距10 μm,规模1024×1024。文章详细介绍了像素输入级以及列级、输出级运放的设计,为提高线性摆伏,设计选用了低阈值NMOS管nmvt 33,仿真分析证明低阈值管nmvt 33的噪声性能优于普通管n 33；版图设计对关键信号线和敏感点采取隔离处理措施,对像元间串扰进行了仿真分析,有效控制了信号串扰。电路经测试使用各项功能正常,最大电荷处理能力达到4.3 Me-,动态范围≥65 dB,读出速率达到10 MHz,性能指标满足设计要求。     

黑匣子高温测试仪的开发

采用物质的相变吸热原理，可以将高温环境传人保温箱的热量重新排回至高温环境，有效地提高了保温箱的抗高温能力，并对保温箱的隔热性能做了简单的理论推算和测温反向试验。温度测试仪选用超低功耗CPU，具有体积小、耗电少的优点，使黑匣子高温测试仪在运行过程中具有很好的安全可靠性。     

关于水电站建设工程保险

介绍了国内外水电站工程建设中应投保的主要保险险种及其理赔情况。通过对国内外的水电站保险状况对比,及对工程保险实例的论述,指出了我国水电站建设工程保险的现状,通过实施保险来减轻和转移水电站工程的风险和损失。