基于改进卷积神经网络的3D打印机激光点温度检测方法

3D打印过程中激光点温度对成型制品的精度和质量会产生直接影响。针对传统温度检测装置在激光点温度检测方面无法达到高检测率、低误差率的工业需求，文章提出一种基于卷积神经网络对激光点温度进行检测估计方法。采用深度学习方法，对收集到的激光温度训练样本运用卷积神经网络的方法进行模型训练，利用训练结果对测试集进行预测，估计出测试激光图像的激光温度。此外在传统卷积神经网络的基础上进行了改进，验证结果表明改进的卷积神经网络能够对激光点等温线进行更为准确的检测估计。     

谐波信号的闭环检测和估计算法及其应用

从联合检测估计的角度讨论了检测器和估计器之间的耦合机制,并就水声目标线谱特征提取问题研究了一种谐波信号的闭环检测和估计算法.该算法通过迭代形式在检测器和估计器之间不断交换信息,用以改善未知参数的先验分布,从而得到较好的检测和估计性能.对海试数据的仿真结果表明,该算法可以有效地提取目标的线谱特征.     

主动声纳宽带信号的一种高效参数估计算法

讨论在宽带信号体制下,对水中高速目标的检测与估计问题,给出了高斯包络线性调频信号必须采用宽带模糊函数进行检测时的带宽和目标速度范围。在仿真分析了信号宽带、窄带模糊函数主峰特性的基础上,提出了一种联合利用宽带模糊函数和窄带模糊函数的高效目标参数估计算法,该算法可以将二维相关运算简化到一维相关运算,降低了运算量,同时具有良好的估计精度。     

系统存在未知输入时的鲁棒故障检测

提出了系统存在未知输入时实现鲁棒故障检测的一种新方法。该方法以系统发生故障之前总有一段正常运行阶段的假设为前提,其基本思想是,在正常运行阶段用一般的观测器估计未知输入,估计稳定后把估计所得到的未知输入当作已知输入修正检测滤波器,这样可以以往的未知输入观测器方法丢失故障信息的缺点。     

水下无人航行器自主检测方法研究

自主检测技术是实现水下无人航行器(Underwater Unmanned Vehicle,UUV)智能化的关键技术,是无人航行器能够自主执行水下预警、目标跟踪等任务的前提。针对当前基于均值类和有序统计类恒虚惊(Constant False Alarm Rate,CFAR)技术的自主检测方法在背景起伏严重、多目标情况下,背景噪声统计特性估计不准确、自主检测性能下降的问题,文章提出了一种基于方位-时间二维参考窗联合有序截断平均算法的自主检测方法。首先,该方法设计了一种方位-时间二维参考窗,解决了一维参考窗检测参考样本过少、噪声统计量估计不准的问题;其次,采用有序截断平均算法估计背景噪声统计量,对起伏背景进行均衡;最后,利用背景噪声均值和方差构造恒虚警检测器,采用检测前跟踪技术,实现起伏背景下、多目标自动检测与跟踪。湖上试验结果表明,在水下无人航行器的自噪声干扰下,该方法对多目标依然具有较好的自主检测效果。     

一种基于广义互相关的水声直扩信号检测方法

直扩信号因其低功率谱密度特性使得检测十分困难,针对传统自相关法在低信噪比条件下检测性能急剧下降的问题,在分析了直扩信号自相关特性的前提下,提出一种基于广义互相关估计的直扩信号检测方法。首先对接收信号分段并对相邻信号段分别进行广义互相关估计,估计结果采用二阶矩非相干积累,提取相关峰获得检测统计量,将检测统计量与门限比较,判定信号是否存在。通过计算机仿真对比了改进自相关检测法与文中方法的检测性能,仿真结果表明：该方法相比于改进自相关检测法信噪比容限降低了5 dB,适用于低信噪比条件下的水声直扩信号检测。水池试验处理结果也验证了该方法的实用性。     

一种基于MMPF-TBD的机动弱目标检测方法

针对低信噪比条件下机动目标实时检测与跟踪问题,提出一种改进的基于多模型粒子滤波的检测前跟踪(MMPF-TBD)方法.通过滑窗平均法判断粒子集是否受上一时刻目标估计结果的影响,当判断值超过设定阈值时,则根据上一时刻的目标检测概率与状态估计分布添加新粒子集,再用扩展后的粒子集对目标进行检测与估计,与现有方法的仿真比较表明,本丈所提的改进方法能够有效地解决粒子退化问题,并在满足系统实时性的前提下,提高了对于机动微弱目标的检测概率和跟踪精度.     

初相未知信号联合检测与估计的序贯实现

在未知参数的充分统计量和再生概率密度函数的基础上，引入了联合检测与估计的序贯算法，给出了算法的实现框图，并将该算法应用于高斯噪声中初相位未知信号的联合检测与估计当中，仿真实验表明，新算法较传统算法有一定的性能改善。     

基于动态测量柔度矩阵的悬索桥吊索损伤检测

基于动态测量柔度矩阵的结构形变和变形曲率是一种有效的悬索桥吊索损伤检测指标。通过模拟仿真不同损伤程度和不同噪声影响下的8种吊索损伤情况,讨论了方法的可行性。仿真结果表明,动态测量柔度法能够定位5%噪声水平下的损伤程度达到90%及以上的单个和多个损伤。通过实验室模型测试来进一步验证这些损伤指标对检测和定位损伤的有效性,并在实验中分析了模态截断对损伤估计的影响。实验表明,与变形位移相比,变形曲率对于检测和定位吊索损伤更为有效。     

Bootstrap重采样Bayesian方法在白车身制造尺寸不合格率估计中的应用

白车身质量是汽车整车质量控制中的重要环节,针对白车身制造尺寸质量控制中检测数据属于小样本数据、数据处理分析不能采用一般大样本条件下统计分析方法的问题引入Bootstrap重采样Bayesian方法。通过对白车身尺寸质量的不合格率进行定义,分析简单计算、滑动计算、β分布Bayesian计算等3种估计不合格率的方法,引入Bootstrap重采样技术结合Bayesian方法进行不合格率的估计,并通过Matlab软件对4种算法进行仿真比较。仿真结果表明,Bootstrap重采样Bayesian方法的预测精度高于其余3种方法,适用于小样本情况下白车身制造尺寸不合格率的估计。最后通过一个实例演示了Bootstrap重采样Bayesian方法在白车身制造尺寸不合格率估计中的应用流程。     

A generalized approach to the reconstruction of a restricted class of digitized planar curves

Reconstruction of an original continuous curve and the estimation of its parameters from the digitized version of the curve is a challenging problem, as quantization always causes some loss of information. In this paper, we have developed a scheme for reconstruction which is applicable to a class of curves having at the most two parameters. The class of curves for which the scheme works has also been characterized. We have shown that for one-parameter curves the exact domain of values of the parameter can be obtained. But in the two-parameter case, only the smallest rectangle containing the domain can be realised. The distinctive feature of our scheme is that it provides a unified approach to solve the reconstruction and the domain-finding problem for a class of curves.     

Blind turbo-detection in the presence of phase noise

Blind coherent detection of convolutional turbo codes is a hard problem in the presence of strong phase noise. Since the operating signal-to-noise ratio is usually very low, phase synchronisation algorithms suffer from phase ambiguities and cycle slips. A possible remedy is to perform joint phase estimation and decoding on a combined state-space model for the time-varying phase and the component convolutional codes. We demonstrate that joint phase estimation and decoding is in fact mandatory only for one component code, while ordinary BCJR decoding can be used for the second component code. Monte Carlo simulations for the turbo code used in the DVB-RCS standard show that the performances of the proposed scheme are close to decoding with perfect knowledge of the phase.     

Recursive parametric tests for multichannel adaptive signal detection

The problem of detecting a multichannel signal in spatially and temporally coloured disturbances is considered. The parametric Rao and parametric generalised likelihood ratio test detectors, recently developed by modelling the disturbance as a multichannel autoregressive (AR) process, have been shown to perform well with limited or even no range training data. These parametric detectors, however, assume that the model order of the multichannel AR process is known a priori to the detector. In practice, the model order has to be estimated by some model order selection technique. Meanwhile, a standard non-recursive implementation of the parametric detectors is computationally intensive since the unknown parameters have to be estimated for all possible model orders before the best one is identified. To address these issues, herein the joint model order selection, parameter estimation and target detection are considered. We present recursive versions of the aforementioned parametric detectors by integrating the multichannel Levinson algorithm, which is employed for recursive and computationally efficient parameter estimation, with a generalised Akaike Information Criterion for model order selection. Numerical results show that the proposed recursive parametric detectors, assuming no knowledge of the model order, yield a detection performance nearly identical to that of their non-recursive counterparts at significantly reduced complexity.     

执行器受限空间机器人的模糊神经网络控制

空间机器人关节执行器输出力矩幅值及幅值变化率受限的情况,是其在太空应用中不可避免要面临的实际问题。为此该文讨论了关节执行器输出力矩幅值及幅值率受限情况下参数未知空间机器人系统协调运动的动力学控制问题。依据系统动量守恒关系和拉格朗日第二类方程,推导了漂浮基空间机器人系统的动力学方程。以此为基础,针对关节执行器输出力矩幅值及幅值率受限的情况,设计了一种自适应模糊神经网络控制器,以使空间机器人系统的本体姿态和机械臂关节铰协调地跟踪各自在关节空间的期望运动轨迹。该控制方案由自适应模糊神经网络控制器及抗饱和参数自适应律构成。首先利用有限差分法将幅值率受限条件转化为幅值受限条件,并与该文预设的力矩受限条件比较以确定每个采样时刻的力矩动态受限范围;然后再通过设计一个抗饱和参数自适应律来确保执行器的输出力矩在动态受限范围内。基于Lyapunov稳定性理论证明了该控制器可确保控制系统是渐近稳定的。仿真对比实验证明了该控制方案的有效性。     

A Novel Technique for Estimating the Numerical Error in Solving the Helmholtz Equation

In this study, we applied a defined auxiliary problem in a novel error estimation technique to estimate the numerical error in the method of fundamental solutions (MFS) for solving the Helmholtz equation. The defined auxiliary problem is substituted for the real problem, and its analytical solution is generated using the complementary solution set of the governing equation. By solving the auxiliary problem and comparing the solution with the quasianalytical solution, an error curve of the MFS versus the source location parameters can be obtained. Thus, the optimal location parameter can be identified. The convergent numerical solution can be obtained and applied to the case of an unavailable analytical solution condition in the real problem. Consequently, we developed a systematic error estimation scheme to identify an optimal parameter. Through numerical experiments, the optimal location parameter of the source points and the optimal number of source points in the MFS were studied and obtained using the error estimation technique.     

前后向时间序列模型联合估计的时变结构模态参数辨识

为提高时变结构模态参数辨识精度和抗噪声能力,提出一种前后向泛函向量时变自回归滑动平均（FS-VTARMA）时间序列模型联合估计的模态参数辨识方法。首先建立前后向FS-VTARMA模型联合估计的均方误差形式的费用函数,其次引入非平稳信号中前向模型和后向模型估计系数的近似共轭关系,再利用两步最小二乘法（2SLS）得到时变模型系数,最后把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明：能有效地克服前向模型估计中模态参数一步延迟以及起始时刻无法准确获得,以及后向模型估计中模态参数一步超前以及终止时刻无法准确获得的缺点,具有更高的模态参数辨识精度和更强的抗噪声能力。     

Reverse stream flow routing by using Muskingum models

Reverse stream flowrouting is a procedure that determines the upstream hydrograph given the downstream hydrograph. This paper presents the development of methodology for Muskingum models parameter estimation for reverse stream flow routing. The standard application of the Muskingum models involves calibration and prediction steps. The calibration step must be performed before the prediction step. The calibration step in a reverse stream flow routing system uses the outflow hydrograph and the inflow at the end period of the inflow hydrograph as the known inputs and Muskingum model parameters are determined by minimizing the error between the remaining portion of the predicted and observed inflow hydrographs. In the present study, methodology for parameter estimation is developed which is based on the concept of minimizing the sum of squares of normalized difference between observed and computed inflows subject to the satisfaction of the routing equation. The parameter estimation problems are formulated as constrained nonlinear optimization problem, and a computational scheme is developed to solve the resulting nonlinear problem. The performance evaluation tests indicate that a fresh calibration is necessary to use the Muskingum models for reverse stream flow routing.     

Generally weighted moving average monitoring schemes: Overview and perspectives

An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution-free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (ie, double GWMA, composite Shewhart-GWMA, mixed GWMA-CUSUM, and mixed CUSUM-GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA-related schemes (ie, 66 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.     

Robust Parameter Design With Computer Experiments Using Orthonormal Polynomials

Robust parameter design with computer experiments is becoming increasingly important for product design. Existing methodologies for this problem are mostly for finding optimal control factor settings. However, in some cases, the objective of the experimenter may be to understand how the noise and control factors contribute to variation in the response. The functional analysis of variance (ANOVA) and variance decompositions of the response, in addition to the mean and variance models, help achieve this objective. Estimation of these quantities is not easy and few methods are able to quantity the estimation uncertainty. In this article, we show that the use of an orthonormal polynomial model of the simulator leads to simple formulas for functional ANOVA and variance decompositions, and the mean and variance models. We show that estimation uncertainty can be taken into account in a simple way by first fitting a Gaussian process model to experiment data and then approximating it with the orthonormal polynomial model. This leads to a joint normal distribution for the polynomial coefficients that quantifies estimation uncertainty. Supplementary materials for this article are available online.