Sensor placement method for dynamic response reconstruction

Structural dynamic response reconstruction can be conducted by transforming the measured responses into responses at other selected locations of a structure. The quality of the reconstructed responses strongly depends on the quality of the measured response data, which further depends on the number and location of sensors in the structure. A new sensor placement method is proposed in this paper for better prediction of the dynamic response reconstruction.     

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).     

四旋翼惯性导航姿态解算算法的改进与仿真

为了实现四旋翼飞行器的高精度导航,提出了互补滤波法和四元数算法对传感器获得的数据进行修正,最大限度的抑制干扰误差并提高姿态角解算的准确度。首先简单推导了捷联式惯性导航系统的算法基本原理并利用互补滤波算法进行改进,然后给出了惯性导航系统的力学编排模型分析旋翼飞行器的运动姿态。最后仿真验证数据选用惯性仪表MPU6050和HMC5883所得到实测数据采集并进行仿真分析,平台处理器选用STM32来仿真惯性仪表的测量速度,最终得到实验结果证明算法可行性。     

Optimal sensor placement for spatial lattice structure based on genetic algorithms

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.     

机载位移传感器故障检测与信号重构

为了避免机载系统故障诊断与重构中单纯增加余度数目所带来的问题,针对电传飞控系统中应用较多的位移传感器,提出了一种基于特征的检测电路、检测方法以及信号重构方案。方案基于位移传感器工作时两次级线圈回路电压和值恒定的特征,实现了对初级、次级回路的一次断线检测,在检测到次级线圈一次断线时,又利用正常工作特征并根据另一次级线圈所获得的信息对断线信号进行重构。分析和测试表明,所提出的位移传感器故障检测以及重构算法仅需增加极少的硬件电路,且检测及重构方法实时性高,在不增加传感器余度数目的条件下,能大大提高系统的可靠性。     

Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.     

可穿戴下肢外骨骼足底压力检测系统设计与研究

可穿戴下肢外骨骼能够柔性跟随人体运动的前提是足底压力检测数据符合人体步态特征,因此研究外骨骼足底压力检测系统具有重要意义。针对现有穿戴式下肢外骨骼系统压力传感器输入输出存在非线性误差,不能准确检测足底压力变化的缺点,选择电阻式压力传感器,通过理论计算确定曲线拟合方法可行性,并将曲线拟合方法引入足底压力检测系统设计中。研究结果表明,该方式采集数据特征明显,曲线拟合的系统线性度较小,可靠性高,适用足底压力检测系统。     

GF-2 PMS2与ZY-3 MUX多光谱传感器数据的交互对比

近年来随着我国新型国产高分影像的相继问世以及相关应用的逐步展开，不少研究涉及了不同国产高分影像多光谱数据之间的交互对比，但两种国产分辨率最高的GF-2 PMS2与ZY-3 MUX传感器多光谱数据之间的对比仍未见报道。为了使这两种国产主力高分辨率传感器的多光谱数据能够在实际应用中相互补充使用，发挥更大的作用，基于它们的3对同日过空影像，采用两种方法对其进行交互对比。第一种方法是对整个试验区采用逐像元光谱比较法进行对比，第二种是采用样区光谱均值比较法进行对比，即在试验影像上选择一系列的样区，然后以各样区的均值进行对比。通过对两种传感器同步影像对的表观反射率进行回归分析，获得各对应波段的回归散点图，查明它们之间的定量关系，并据此提出相互转换的关系方程。研究结果表明，两种对比方法得出的结果相一致，但使用样区光谱均值比较法进行交互对比的结果的准确性更高。GF-2 PMS2与ZY-3 MUX各对应波段具有很强的相关性，其线性回归方程的决定系数(R2)都大于0.9，但其值在蓝绿波段较高，在红光和近红外波段有所下降，表明两种传感器的表观反射率在蓝绿波段的一致性好于红光和近红外波段。总体上看，GF-2 PMS2的信号强于ZY-3 MUX，二者的信号差异在蓝、绿光波段较大，在红光和近红外波段较小，但却明显受到地物类型的影响。对于以裸土为主的影像，两种传感器之间的差异随着波长的增大而逐渐减小，而对于以植被为主的影像，二者之间的差异却随着波长的增大而逐渐增大。将纯植被与纯裸土的样区单独提取出来做进一步分析，结果表明，两种传感器的信号差异程度在红光波段主要受裸土影响，而在近红外波段则主要是受植被影响，且植被长势越旺盛，两种传感器的表观反射率差异越大。通过研究获得了两种传感器多光谱波段数据之间的相互转换方程，并对其进行验证，结果表明：经过转换后的GF-2 PMS2数据与ZY-3 MUX数据之间的差异大大减小，各波段均方根误差的均值降幅可达64.79%，平均相对偏差率也有明显的降低。这表明，所查明的两种传感器的定量关系是有效的，其对应波段的转换方程可以用于两种传感器数据的相互转换，经转换后的数据更有利于这两种传感器数据的协同使用。分析两种传感器数据的差异原因表明：二者数据的差异主要是由于它们的光谱响应函数的差异和空间分辨率的差异引起的。ZY-3 MUX的光谱响应函数曲线相对平缓，没有明显的起伏波动，而GF-2 PMS2则较不稳定，在四个波段呈现出程度不同的起伏变化，从而影响了二者表观反射率信号的一致性；而GF-2 PMS2具有的4 m空间分辨率明显高于ZY-3 MUX的6m空间分辨率，因此更容易捕捉到细小地物的光谱信息，这也使得二者信号出现不一致。     

基于衍射微透镜的光学加速度传感器设计

提出了一种基于菲涅耳衍射微透镜的新型光学加速度传感器,它能够解决导航系统中惯性传感器普遍存在的抗电磁干扰和电磁冲击能力差等缺陷。其传感原理是把一个反光膜平行地置于衍射微透镜的后方,加速度的变化会改变反光膜的位置,根据微透镜前方会聚点处光强的变化,感知加速度的大小。介绍了传感器及其动力学系统的工作原理,并对衍射微透镜和动力学系统的微弹性机械结构进行了设计和加工,最后对传感器的性能和误差进行了分析。原理验证性实验的结果表明这种新型光学加速度传感器的原理正确,并且结构简单,灵敏度高。     

基于主成分分析的光谱重建训练样本选择方法研究

训练样本构成是影响光谱重建精度的一个重要因素,针对学习型光谱重建算法中训练样本选择问题,提出了一种基于主成分分析的训练样本选择方法。为了保证训练样本与重建样本的相似度,首先根据欧式距离最小原则从待选样本集中选择与重建样本相机响应值相似的样本,并去掉其中的重复样本;然后进行主成分分析;设定阈值筛选各主成分系数较大的样本作为训练样本,最后得到与主成分个数相同的训练样本子集。为验证该方法的有效性,通过在镜头前加载宽带滤色片搭建多通道图像获取系统采集多通道图像信息,将得到的各样本子集用作训练样本,利用伪逆法重建光谱信息,最后将重建的光谱精度与常用的训练样本及训练样本选择方法得到的重建光谱精度进行比较。实验结果表明：提出的方法显著提高了光谱重建的色度精度和光谱精度,优于常用的样本选择方法,能较大程度满足高精度颜色复制要求。     

基于石墨烯/石墨烯卷轴复合薄膜高灵敏度柔性压力传感器

一维导电材料例如纳米线,大量应用于柔性压力传感器中. 但是一维材料和基底之间接触时相互作用力较弱,使得传感器灵敏度、响应时间、和循环寿命等性能指标有待进一步提高. 针对这些问题,设计了石墨烯/石墨烯卷轴多分子层复合薄膜作为传感器导电层. 石墨烯卷轴具有一维结构,而石墨烯的二维结构可以牢固地固定卷轴,以确保高导电性复合薄膜与基底之间的粘附性,同时整体结构的导电通道得到了增加. 由于一维和二维结构的协同效应,实现了应变灵敏度系数3.5 kPa^-1、 响应时间小于50 ms、能够稳定工作1000次以上的压阻传感器.     

基于数据协方差矩阵重构的MIMO声纳DOA估计

实际多输入多输出(MIMO)声纳系统由于环境或人为因素,可能出现部分阵元失效,从而导致阵列自由度减少、方位估计精度下降。本文提出了一种数据协方差矩阵重构方法,该方法基于差分阵列性质,利用正常工作阵元的协方差矩阵元素来恢复失效阵元的矩阵元素,获得满秩的数据协方差矩阵,从而恢复到全阵元MIMO声纳的阵列自由度。与已有方法相比,降低了计算复杂度。仿真及海试实验数据处理结果表明,本文所提的数据协方差矩阵重构方法能够恢复因部分阵元失效而丢失的阵列自由度,应用于方位估计中,所能分辨的最大目标数与全阵元相同。     

Input reconstruction of chaos sensors

Although the sensitivity of sensors can be significantly enhanced using chaotic dynamics due to its extremely sensitive dependence on initial conditions and parameters, how to reconstruct the measured signal from the distorted sensor response becomes challenging. In this paper we suggest an effective method to reconstruct the measured signal from the distorted (chaotic) response of chaos sensors. This measurement signal reconstruction method applies the neural network techniques for system structure identification and therefore does not require the precise information of the sensor's dynamics. We discuss also how to improve the robustness of reconstruction. Some examples are presented to illustrate the measurement signal reconstruction method suggested.     

光敏传感器实验课程改革探索

针对目前光敏传感器实验课教学中存在的问题,我们以传感器应用创新为导向,从教学方法和教学内容两个方面对此课程进行了改革,充分的激发了学生的学习兴趣,提高了学生的综合能力和创新能力。     

正交匹配追踪算法的近红外光谱定量分析

压缩感知(CS)是一种新兴的信号压缩和采样技术,正交匹配追踪(OMP)是一种贪婪追踪算法,广泛用于压缩感知领域中的稀疏信号重构。针对近红外光谱信号高维小样本以及信号稀疏先验的特点,为进一步提高小样本近红外光谱变量选择的灵活性和可靠性,基于压缩感知理论,提出了一种新颖的光谱变量选择方法正交匹配追踪变量选择(OMPBVS)。OMPBVS算法通过对原始光谱信号的稀疏重构,将绝大部分变量的回归系数压缩为0,进而间接实现光谱变量选择。具体过程为以光谱矩阵为传感矩阵,预测变量为观测变量,迭代地计算残差与原子的内积,选择内积最大的原子,在每一步迭代过程中将信号投影到由所有已经被选择原子张成的子空间上,然后对所有被选择原子的系数进行更新,使得产生的残差与已被选择的所有原子都正交,其残差计算的实质是进行Gram-Schmidt正交化,正交投影能够在保证信号重构精度的情况下减小迭代次数。OMPBVS具有将光谱维度降低至样本大小规模的能力,其变量选择能力与LASSO相当,但与LASSO相比,由于OMPBVS损失函数的优化方法是前向选择算法,减少了迭代次数,并且可以精确控制选择变量的数量。分别在beer数据集和Wheat kernels数据集上进行变量选择实验,比较PLS,MCUVE-PLS,CARS-PLS,WMSCVS,LASSOLarsCV和OMPBVS六种变量选择方法的性能。其中beer数据集共60个样本,采用Kennard Stone (KS)方法划分训练集样本36个,测试集样本24个,预测变量为Original extract concentration。Wheat kernels数据集共523个样本,训练集样本415个,测试集样本108个,预测值为蛋白质含量。OMPBVS方法在beer数据集上选择变量个数、RMSEC和RMSEP分别为2,0.205 2和0.159 8,在Wheat kernels数据集上选择变量个数、RMSEC和RMSEP分别为9,0.450 2和0.412 5,其变量选择能力和模型性能均好于其他五种方法,这说明OMPBVS是一种有效的近红外光谱变量选择和定量分析方法。OMPBVS变量选择方法在小样本情况下具有良好的泛化能力,能够减少选择变量的数量,提高变量选择的稳健性。此外,基于SNV和MSC等光谱预处理方法,能够在一定程度上减少选择变量的个数,提高模型的可解释性。     

Capacitive position measurement for high-precision space inertial sensor

Low noise position measurement is fundamental for space inertial sensors, and at present the capacitive position sensor is widely employed for space inertial sensors. The design for the possible suppression of the front-end electric noises for a capacitive sensor is presented. A prototype capacitive sensor with 2×10⁻⁶pF/Hz1/2 at frequency above 0.04 Hz is achieved and further improvements are discussed.      

基于一组Kalman滤波器信息融合的故障诊断

为了能及时准确地诊断发动机的传感器和执行机构故障,本文提出了基于一组Kalman滤波器信息融合的方法进行故障诊断。首先根据传感器特性设计了一组滤波器用于传感器故障诊断、隔离,每个滤波器针对一个传感器进行设计;其次根据执行机构故障特性设计了一组Kalman滤波器进行执行机构偏差估计,从而对执行机构进行故障诊断、隔离;接着给出了传感器、执行机构信息融合的诊断方案;最后分别给传感器、执行机构添加故障进行方案验证,仿真结果得出在传感器或者执行机构任意部件出故障的情况下,该融合方法可以有效地诊断并隔离出有故障的传感器或者执行机构。     

实时运动结构重建在自主导航系统中的应用

实时运动结构重建是自主车辆、机器人导航、空间探测器自主降落、智能监控等领域中的重要研究课题。目前实时运动结构重建主要存在着特征匹配困难、鲁棒性差、系统无法自动获取初始参数和需要大量人工干预等诸多问题。利用高速CMOS摄像机与惯性传感数据融合提高了运动结构重建算法的精度及其鲁棒性。该算法在扩展卡尔曼滤波框架下是通过融合惯性与视觉传感器的数据来进行运动估计的。对场景中的每一个待估计结构的特征点建立对应的卡尔曼滤波器,以估计其空间三维结构信息。运动估计模块与结构估计模块交替运行,减小了系统运算的复杂度,提高了实时性能。通过对真实场景图像序列的实验验证结果表明,惯性传感器的额外信息能够有效地提高运动结构估计的精度,能够增强算法的鲁棒性。     

传感器光谱指标对植被光谱模拟精度的影响

高光谱卫星数据模拟是卫星遥感数据模拟的重点研究方向,基于星载多光谱数据和地物光谱先验知识是一种快速模拟高光谱数据的方法,但数据模拟精度受传感器光谱指标的限制。文章针对EO-1/ALI的可见光/近红外波长范围进行实验,研究了波段数量、半波宽度和波长位置等光谱指标与植被光谱模拟精度的关系,分析了两者之间的变化规律。研究表明,光谱指标决定了植被光谱特征提取,是影响光谱模拟精度的直接原因。文章总结了适于光谱重构模型的光谱参数范围,实验结果有利于提高植被光谱模拟精度。该结论可用于多光谱传感器的性能评价及其分光滤色结构的改进。     

A Facile Strategy for Low Young's Modulus PDMS Microbeads Enhanced Flexible Capacitive Pressure Sensors

Flexible pressure sensors are widely demanded in human care systems. A simple and effective strategy for sensor fabrication can markedly promote its application. Herein, a facile strategy is employed to prepare a flexible capacitive pressure sensor with a polydimethylsiloxane microbeads-modified dielectric layer. Owing to the microbeads structure, the proposed sensor achieves a sensitivity of 0.048 kPa⁻¹ in the range of 0–10 kPa with a wide dynamic range (up to 100 kPa). The sensitivity is nine times higher than that of the planar structure. Moreover, the microbeads structured sensor obtains a low limit of detection (0.2 kPa), fast response time (120 ms), and good stability (variation lower than 3.30% after 1000 loading/unloading cycles at 20 kPa). The finite-elemental analysis reveals that the microbeads structure is critical to enhance the performance of sensors. Finally, the pressure sensor is successfully applied to detect touch signal, joints movement, and breathing, exhibiting its promising prospects as smart wearable devices. Furthermore, the strategy may provide a new idea for the microstructural design of capacitive pressure sensors.