Sensitivity analysis of shape memory alloy shells

This paper presents procedures for efficient design sensitivity analysis for shape memory alloy (SMA) structures modeled with shell elements. Availability of sensitivity information at low computational cost can dramatically improve the efficiency of the optimization process, as it enables use of efficient gradient-based optimization algorithms. The formulation and computation of design sensitivities of SMA shell structures using the direct differentiation method is considered, in a steady state electro-thermo-mechanical finite element context. Finite difference, semi-analytical and refined semi-analytical sensitivity analysis approaches are considered and compared in terms of efficiency, accuracy and implementation effort, based on a representative finite element model of a miniature SMA gripper.     

La0.5Sr0.5CoO3−δ nanotubes sensor for room temperature detection of ammonia

La_0.5Sr_0.5CoO_3−δ (LSCO) nanotubes were synthesized by using a porous anodic aluminum oxide (AAO) template from a sol–gel solution. Based on the achievement of synthesis of LSCO nanotubes, a nanotube gas sensor was fabricated with microelectromechanical system technology and its NH₃ sensing characteristics were investigated. Capacitance of LSCO nanotubes was changed by two orders of magnitude within several seconds of exposure to NH₃ molecules at room temperature. The detection limit of the LSCO nanotube sensor was several ppm, and the typical response and recovery time of the sensor at room temperature was only several seconds. Our results demonstrate the potential application of LSCO nanotubes for fabricating a highly sensitive and fast response gas sensor.     

一种快速映射Isomap算法

传统的Isomap算法仅侧重于当前数据的分析,不能提供由高维空间到低维空间的快速直接映射,因此无法用于特征提取和高维数据检索.针对这一问题,文中提出一种基于Isornap的快速数据检索算法.该算法能够快速得到新样本的低维嵌入坐标,并基于此坐标检索与输入样本最相似的参考样本.在典型测试集上的实验结果表明,该算法在实现新样本到低维流形快速映射的同时,能较好保留样本的近邻关系.     

Combining the Hanning windowed interpolated FFT in both directions

The interpolated fast Fourier transform (IFFT) has been proposed as a way to eliminate the picket fence effect (PFE) of the fast Fourier transform. The modulus based IFFT, cited in most relevant references, makes use of only the 1st and 2nd highest spectral lines. An approach using three principal spectral lines is proposed. This new approach combines both directions of the complex spectrum based IFFT with the Hanning window. The optimal weight to minimize the estimation variance is established on the first order Taylor series expansion of noise interference. A numerical simulation is carried out, and the results are compared with the Cramer-Rao bound. It is demonstrated that the proposed approach has a lower estimation variance than the two-spectral-line approach. The improvement depends on the extent of sampling deviating from the coherent condition, and the best is decreasing variance by 2/7. However, it is also shown that the estimation variance of the windowed IFFT with the Hanning is significantly higher than that of without windowing.     

基于FPGA硬件实现的谐波检测方法

针对现有系统对谐波检测实时性差和精度低的问题,介绍一种基于傅立叶变换和FPGA硬件实现的谐波检测方法.分析了谐波检测中影响测量精度的关键因素,采用数字锁相环来同步被测信号,以减小由非同步采样所产生的误差.基-4FFT 处理器的硬件设计采用全并行的乘法运算单元结构和并行的存储分配方法,最大限度地提高谐波检测的速度.数字锁相环和基-4 FFT 算法用VHDL语言设计实现,并用MAX plus Ⅱ软件进行仿真,仿真结果表明,所设计的数字锁相环可以很好地跟踪被测信号,在180ms时,误差仅为0.01Hz,很好地消除了非同步采样所引起的测量误差;采用所设计的基-4FFT运算器对给定的谐波数据进行运算,得到的谐波幅值和相位误差小于0.05%,运算时间仅为8μs.     

光稳系统中视轴漂移的数值模拟

基于坐标变换公式和矢量旋转公式,分析了视轴稳定系统中各光学器件对于光线矢量传输的影响,并对光线矢量传输进行追踪,建立了视轴稳定系统的光学传输模型,数值模拟了光学平台角度振动对探测视轴漂移的影响,从而获到了基于快速倾斜反射镜的视轴校正系统的角度补偿量.模拟结果表明,对该光学系统的视轴校正,快速倾斜反射镜绕轴旋转的角度补偿量为0,绕轴旋转的角度补偿量与位敏传感器上沿方向的投影是一种简单的线性关系.     

一类欠驱动系统的分层FTSMC控制

研究了一类具有扰动的欠驱动非线性系统的控制问题及其渐近稳定的判定.提出一种基于快速终端滑模(FTSMC)的分层控制方法:分层快速终端滑模控制(HFTSMC).方法将系统按状态变量的耦合关系分成多个子系统,应用快速终端滑模控制分别构造滑动面.在此基础上采用Lyapunov方法设计控制量,给出了一种新的滑模而渐近稳定性判据,并提出了两种构造第二层滑模面的方法.对旋转式倒立摆的稳定控制进行的MATLAB仿真与实物实验,均得到较好的控制效果.实验结果证明了控制方法及判据的有效性.     

国产平台下可靠文件传输软件的设计及实现

在国产自主可控平台加速推广的当下,为实现文件在测控数据传输网不同软硬件平台之间的可靠、快速传输,本文根据相关技术规范,基于文件交换协议(File Exchange Protocol,简称FXP协议),设计并实现功能完备、运行稳定的可靠文件传输软件。软件具备模块化、可移植、基于TCP协议满足用户可靠性需求、基于UDP协议满足用户传输效率需求、兼容不同软硬件平台等的功能特点,并具备满足安全编程需求的软件特性,立足核心代码设计、验证,对软件功能和稳定运行两个方面做了进一步的改良和提升,以满足跨不同软硬件平台稳定运行、恶劣网络环境中可靠传输、畅通网络环境中尽力快速传输的新要求。     

Fast k most similar neighbor classifier for mixed data (tree k-MSN)

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. For this reason, many fast k-NN classifiers have been developed, some of them are based on a tree structure, which is created during a preprocessing phase using the prototypes in T. Then, in a search phase, the tree is traversed to find the nearest neighbor. The speed up is obtained, while the exploration of some parts of the tree is avoided using pruning rules which are usually based on the triangle inequality. However, in soft sciences as Medicine, Geology, Sociology, etc., the prototypes are usually described by numerical and categorical attributes (mixed data), and sometimes the comparison function for computing the similarity between prototypes does not satisfy metric properties. Therefore, in this work an approximate fast k most similar neighbor classifier, for mixed data and similarity functions that do not satisfy metric properties, based on a tree structure (Tree k-MSN) is proposed. Some experiments with synthetic and real data are presented.     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.