Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: part II--Optimization of structural sensor placement

The work proposed an optimization approach for structural sensor placement to improve the performance of vibro-acoustic virtual sensor for active noise control applications. The vibro-acoustic virtual sensor was designed to estimate the interior sound pressure of an acoustic-structural coupled enclosure using structural sensors. A spectral-spatial performance metric was proposed, which was used to quantify the averaged structural sensor output energy of a vibro-acoustic system excited by a spatially varying point source. It was shown that (i) the overall virtual sensing error energy was contributed additively by the modal virtual sensing error and the measurement noise energy; (ii) each of the modal virtual sensing error system was contributed by both the modal observability levels for the structural sensing and the target acoustic virtual sensing; and further (iii) the strength of each modal observability level was influenced by the modal coupling and resonance frequencies of the associated uncoupled structural/cavity modes. An optimal design of structural sensor placement was proposed to achieve sufficiently high modal observability levels for certain important panel- and cavity-controlled modes. Numerical analysis on a panel-cavity system demonstrated the importance of structural sensor placement on virtual sensing and active noise control performance, particularly for cavity-controlled modes.     

大型粮仓温度分布反演数学模型的研究

采用有限元数值模拟方法,以响应面模型RSM(Response surface model)理论为基础对大型粮仓冬、夏两季的温度分布进行了深入分析,推导了粮仓温度反演数学模型。将采集的温度值导入RSM模型中,并结合单因素轮换法、正交实验和统计方法,通过构造一个具有明确表达形式的多项式来预测粮仓温度分布反演数学模型。在数学模型的基础上给出各因子交互作用下的设计空间、各因素对粮温影响的主效应及交互效应。实验证明该数学模型误差小,精度高,具有良好的计算性能,在大型粮仓温度监控系统中采用该模型能够解决布线少、预测空间范围广的问题。     

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.     

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.     

尖楔前体飞行器FADS系统的径向基网络建模及验证

文章研究了针对一种用于尖楔外形的嵌入式大气数据传感（flush air data sensing，FADS）系统的解算模型及精度.首先基于飞行包络及CFD数据建立了FADS系统的测压孔选取标准；然后基于径向基函数（radial basis function，RBF）的人工神经网络建模技术构建了FADS系统的网络解算模型；最后给出了模型的测试误差，分析了气动延时效应、位置误差等误差源模型对算法精度的影响，并给出了网络模型的预测精度.结果表明，针对尖楔外形测压孔配置特征，基于RBF的人工神经网络算法解算精度较好，攻角、侧滑角、Mach数及静压的网络输出预测值与真实值吻合较好，输出的测试误差（绝对值）分别小于0.25°，0.5°，0.05及250 Pa.结果同时表明神经网络建模技术在尖楔前体飞行器FADS系统中的有效性.      

On optimal solution error covariances in variational data assimilation problems

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find unknown parameters such as distributed model coefficients or boundary conditions. The equation for the optimal solution error is derived through the errors of the input data (background and observation errors), and the optimal solution error covariance operator through the input data error covariance operators, respectively. The quasi-Newton BFGS algorithm is adapted to construct the covariance matrix of the optimal solution error using the inverse Hessian of an auxiliary data assimilation problem based on the tangent linear model constraints. Preconditioning is applied to reduce the number of iterations required by the BFGS algorithm to build a quasi-Newton approximation of the inverse Hessian. Numerical examples are presented for the one-dimensional convection–diffusion model.     

空间遥感器中窗口的热光学特性研究

光学窗口是空间光学遥感器应用系统中的重要组成部分 ,其热光学特性是其热控设计的指南。拟定某种温度边界条件后利用有限元法计算其稳态温度场分布 ,再将温度载荷和其它有关广义力载荷作为载荷 ,计算光学玻璃的变形。将变形值拟合为 Zemike多项式 ,输入到光学设计程序 Zmax TM中 ,计算出光学玻璃的变形所引入的波差。最终根据光学遥感器许用的系统波像差来给定光学玻璃的许用温度梯度     

Wavelength-modulation spectroscopy near 2.5 μm for H2O and temperature in high-pressure and -temperature gases

The design and validation of a tunable diode laser (TDL) sensor for temperature and H₂O in high-pressure and -temperature gases are presented. High-fidelity measurements are enabled through the use of: (1) strong H₂O fundamental-band absorption near 2.5 μm, (2) calibration-free first-harmonic-normalized wavelength-modulation spectroscopy with second-harmonic detection (WMS-2f/1f), (3) an experimentally derived and validated spectroscopic database, and (4) a new approach to selecting the optimal wavelength and modulation depth of each laser. This sensor uses two TDLs near 2,474 and 2,482 nm that were fiber coupled in free space and frequency multiplexed to enable measurements along a single line-of-sight. The lasers were modulated at 35 and 45.5 kHz, respectively, to achieve a sensor bandwidth of 4.5 kHz. This sensor was validated in a shock tube at temperatures and pressures ranging from 1,000 to 2,700 K and 8 to 50 bar. There the sensor resolved transients and recovered the known steady-state temperature and H₂O mole fraction with a precision of 3.2 and 2.6 % RMS, respectively.     

Active control of high-frequency vibration: Optimisation using the hybrid modelling method

This work presents active control of high-frequency vibration using skyhook dampers. The choice of the damper gain and its optimal location is crucial for the effective implementation of active vibration control. In vibration control, certain sensor/actuator locations are preferable for reducing structural vibration while using minimum control effort. In order to perform optimisation on a general built-up structure to control vibration, it is necessary to have a good modelling technique to predict the performance of the controller. The present work exploits the hybrid modelling approach, which combines the finite element method (FEM) and statistical energy analysis (SEA) to provide efficient response predictions at medium to high frequencies. The hybrid method is implemented here for a general network of plates, coupled via springs, to allow study of a variety of generic control design problems. By combining the hybrid method with numerical optimisation using a genetic algorithm, optimal skyhook damper gains and locations are obtained. The optimal controller gain and location found from the hybrid method are compared with results from a deterministic modelling method. Good agreement between the results is observed, whereas results from the hybrid method are found in a significantly reduced amount of time.     

Exploration of SDSS stellar database by AutoClass

AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as...     

空间光学遥感器主镜加工过程中疲劳寿命研究

针对空间光学遥感器主镜镜面加工过程中,磨盘与主镜间磨削动作往复运行引起的主镜柔性支撑结构疲劳寿命问题,通过建立主镜组件的有限元模型,利用MSC.Fatigue软件按应力-寿命(S-N)法对主镜组件进行了疲劳寿命分析,确定了支撑结构的薄弱部位,并对仿真过程进行了误差分析,讨论了影响仿真结果的各个因素.对比热真空试验和动力学试验前后主镜镜面面型数据,验证了支撑结构加工、设计参量的合理性.通过疲劳寿命仿真分析,可以有效预示光学结构在加工过程中的疲劳情况,为空间光学遥感器结构的设计、加工提供理论依据和参考.     

核磁共振测井仪探头设计中的数值方法

核磁共振测井仪探头的优化设计能够增强仪器的探测特性,提高仪器的信噪比, 而探头设计中的数值方法对设计结果至关重要.本文利用电磁场有限元方法对贴井壁型核磁共振测井仪探头静磁场和射频场进行了2D和3D的数值模拟,深入分析了数值模型形状、模型尺寸、 单元形状对数值模拟结果的影响,并将有限元数值模拟结果与实测数据做了对比. 结果显示:数值模拟结果与实测数据符合.在设计核磁共振测井仪探头结构时, 选取与井眼形状一致的圆形模型,模型尺寸范围在10—15倍探头外径, 并采用三角形单元可以有效提高数值模拟方法精度,增强优化设计结果的可靠性.     

小口径高精度折射式光学系统装调公差的分析与控制

采用小口径高精度折射式光学系统的对地观测遥感器对成像质量要求严格,随之对装调公差要求苛刻。传统装调公差分配完全以光学设计给定公差为标准,在实际装调过程中与精度和周期的需求产生矛盾。提出将光学设计和装调实践相结合,用全过程仿真计算进行成像质量预估,对公差进行再分配,并利用光学系统各分离误差相互间的补偿,实时调整公差。然后举例说明在某小口径高精度折射式光学系统装调过程中对公差的具体分析,并详细阐述了针对公差的再分配和补偿方法,实现对偏心误差的控制精度为2,对镜间距的控制精度为1 m。镜头装调完成后成像性能良好,证明公差分配及控制方法的改进使装调效率显著提高。     

探空温度传感器的计算流体动力学分析与实验研究

伴随着数值天气预报和气候变化研究精细化程度的不断提高, 希望探空温度传感器的观测精度达到0.1 K数量级. 为了实现此目标, 运用计算流体动力学方法对珠状热敏电阻从海平面上升至20 km高空的整个过程进行数值仿真分析. 并在此基础上, 针对影响测温精度的太阳辐射强度和传感器倾斜角度两个因素进行分析. 仿真结果表明, 太阳辐射强度和海拔高度是辐射误差的重要影响因子. 当传感器倾斜角度为90°时, 珠状热敏电阻的辐射误差最小. 通过麦夸特法和通用全局优化法对仿真数据进行拟合, 获得不同海拔高度和太阳辐射强度下的辐射误差修正方程; 为验证方程的准确性, 设计和搭建太阳辐射误差模拟系统. 实验结果表明, 辐射误差实验测量值与修正方程修正值之间的平均偏移量为0.017 K, 均方根值RMS误差为0.023 K, 验证了计算流体动力学方法、麦夸特法和通用全局优化法获得辐射误差数据的准确性.     

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).     

基于FPGA的半导体激光器温度控制系统设计

介绍了半导体激光器的温度控制原理,设计了基于FPGA的半导体激光器温度控制系统。系统采用数字温度传感器DS18820作为温度测量器件,TEC作为控温执行元件,并采用模糊控制算法、PWM机制来控制TEC的驱动电流从而实现温控。基于QuartusⅡ开发平台采用Verilog HDL设计了软件部分,最后进行了功能仿真。整个系统对硬件要求不高,实现简单。从仿真结果可以看出,控制系统能够满足半导体激光器温度控制的精度和稳定度的要求。     

Determination of quantum toric error correction code threshold using convolutional neural network decoders

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.     

Improved phase-contrast flow quantification by three-dimensional vessel localization

In this paper, a method of three-dimensional (3D) vessel localization is presented to allow the identification of a vessel of interest, the selection of a vessel segment, and the determination of a slice orientation to improve the accuracy of phase-contrast magnetic resonance (PCMR) angiography. A marching-cube surface-rendering algorithm was used to reconstruct the 3D vasculature. Surface-rendering was obtained using an iso-surface value determined from a maximum intensity projection (MIP) image. This 3D vasculature was used to find a vessel of interest, select a vessel segment, and to determine the slice orientation perpendicular to the vessel axis. Volumetric flow rate (VFR) was obtained in a phantom model and in vivo using 3D localization with double oblique cine PCMR scanning. PCMR flow measurements in the phantom showed 5. 2% maximum error and a standard deviation of 9 mL/min during steady flow, 7.9% maximum error and a standard deviation of 13 mL/min during pulsatile flow compared with measurements using an ultrasonic transit-time flowmeter. PCMR VFR measurement error increased with misalignment at 10, 20, and 30 degrees oblique to the perpendicular slice in vitro and in vivo. The 3D localization technique allowed precise localization of the vessel of interest and optimal placement of the slice orientation for minimum error in flow measurements.     

非对称空间光学遥感器主动热控系统多目标优化设计

针对大口径、离轴、非对称结构的空间光学遥感器主动热控功率最小分配的难题,提出一种基于多目标遗传算法的功率优化方法。首先根据空间相机结构建立有限元模型。然后,凭借设计者的经验,根据相机结构特点及大致热分布规律,初步划分热控区域,规划设计变量和目标变量。之后,将设计变量和目标变量代入多目标遗传算法求出Pareto最优解集。最后,在最优解集中选出合适的功率分配代入到仿真模型中进行计算,得到优化后的功率分配及温度场。对某离轴三反空间相机进行了功率优化和地面热平衡试验。经TMG仿真计算,优化后整机波动范围在低温工况和高温工况分别降低了4.76%和35.7%,并且总功耗降低了6.85%。经地面热平衡试验表明,整机温度场温差控制在±0.5℃以内,满足±2℃的指标要求。     

Holographic optical element for laser time-of-flight flow sensor

A novel laser time-of-flight flow sensor has been developed. A specially designed holographic optical element (HOE) constitutes the basis of the optical front end of the sensor. The multifunctional HOE maintains both the transmitting and receiving functions of the optical front end and is designed so that the alignment of the optical system is independent of small changes in the wavelength and temperature. This makes it possible to use a laser diode and makes the optical front end very compact and robust. The HOE is produced as a high-efficiency surface relief grating, thus making a large-scale production through polymer replication possible. The design and fabrication of the HOE is described and the stability of the sensor system is discussed with respect to wavelength variations. Direct calibration measurements on the sensor and test velocity measurements are presented. The sensor is intended for water flow measurements in pipes and can also be used for velocity measurements of solid surfaces.