Tracking method based on separation and combination of the measurements for radar and IR fusion system

A new distributed fusion method of radar/infrared （IR） tracking system based on separation and combination of the measurements is proposed by analyzing the influence of rate measurement. The rate information separated from the radar measurements together with measurements of IR form a pseudo vector of IR, and the corresponding filter is designed. The results indicate that the method not only makes a great improvement to the local tracker＇s performance, but also improves the global tracking precision efficiently.

In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.

INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Data Fusion Method for Manufacturing Measurement

A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.

Risk factors and care of early surgical site infection after primary posterior lumbar interbody fusion

Objectives:To explore the risk factors and nursing measures of early surgical site infection(SSI)after posterior lumbar interbody fusion(PLIF).Methods:A total of 468 patients who received PLIF in our hospital from January 2017 to June 2020 were enrolled into this study.According to the occurrence of early SSI,the patients were divided into two groups,and the general data were analyzed by univariate analysis.Multivariate logistic regression analysis was conducted with the dichotomous variable of whether early SSI occurred and other factors as independent variables to identify the risk factors of early SSI and put forward targeted prevention and nursing measures.Results:Among 468 patients with PLIF,18 patients developed early SSI(3.85%).The proportion of female,age,diabetes mellitus and urinary tract infection(UTI),operation segment,operation time,post-operative drainage volume,and drainage time were significantly higher than those in the uninfected group,with statistical significance(P<0.05),whereas the preoperative albumin and hemoglobin in the infected group were significantly lower than those in the uninfected group,with statistical significance(P<0.05).There was no significant difference between the two groups in the American Society of Anesthesiologists(ASA)grading,body mass index(BMI),complications including cardiovascular and cerebrovascular diseases or hypertension(P>0.05).Logistic regression analysis showed that preoperative diabetes mellitus(OR=2.109,P=0.012)/UTI(OR=1.526,P=0.035),prolonged drainage time(OR=1.639,P=0.029)were risk factors for early SSI.Men(OR=0.736,P=0.027)and albumin level(OR=0.526,P=0.004)were protective factors in reducing early SSI.Conclusions:Women,preoperative diabetes/UTI,hypoproteinemia,and prolonged drainage time are risk factors for early SSI after PLIF.Clinical effective preventive measures should be taken in combination with targeted nursing intervention to reduce the risk of early SSI.

Study on Posture Estimation Using Delayed Measurements for Mobile Robots

When associating data from various sensors to estimate the posture of mobile robots, a crucial problem to be solved is that there may be some delayed measurements. Furthermore, the general multi-sensor data fusion algorithm is a Kalman filter. In order to handle the problem concerning delayed measurements, this paper investigates a Kalman filter modified to account for the delays. Based on the interpolating measurement, a fusion system is applied to estimate the posture of a mobile robot which fuses the data from the encoder and laser global position system using the extended Kalman filter algorithm. Finally, the posture estimation experiment of the mobile robot is given whose result verifies the feasibility and efficiency of the algorithm.

VirtualLab Fusion在光偏振教学改革中的应用探索

偏振是光的基本特性之一,在工业生产和科学研究领域应用广泛。在大学物理实验和物理光学课程中,光的偏振都是重要内容之一。本文将VirtualLab Fusion仿真软件应用于光的偏振教学过程中,结合科研项目对旋光度的测量和矢量光场的产生进行了理论分析和VirtualLab Fusion建模仿真。该仿真立体直观,有利于激发学生的学习热情、培养学生的创新意识和创新能力。该研究能够对大学物理实验教学和物理光学课程教学模式改革提供一定的借鉴作用。

A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

基于PCNN图像融合的ECT多相流测量系统设计

针对ECT在多相流测量过程中存在的成像质量不高、精度低、稳定性差等问题,提出并实现了基于PCNN图像融合的ECT多相流测量系统。系统采用模块化设计,主要包括阵列电极传感器模块、电容采集电路模块、上位机成像软件模块。文中设计改进的网络模型,将Tikhonov和Landweber算法的成像结果作为输入进行双通道融合成像,改善了传统算法存在的伪影、模糊的现象。实验结果表明:应用尼龙棒模拟气固两相流对系统进行测试,系统对于微小电容的测量具有良好的稳定性和准确性,通过系统的成像结果可以准确地分辨管道内不同介质的位置,其中对于流型的预测成功率为100%,相含率的测量误差在1%以内。

基于ELM的超声多特征融合螺栓应力测量方法

针对传统超声波螺栓应力测量中存在的非线性和不适定性问题,提出一种基于极限学习机(ELM)的超声多特征融合螺栓应力测量方法。首先基于声弹性理论和散射理论,根据超声回波信号提取声时差及瑞利散射范围内多晶体材料中纵波的衰减系数等超声波特征参数。然后通过向量降维选择声时差、衰减系数和有效受力长度作为模型输入特征向量,建立了基于ELM的超声多特征融合螺栓应力测量模型。搭建螺栓轴向应力超声波测量实验平台,对不同材料和规格的螺栓进行螺栓应力的测量,并对比了使用传统的超声测量方法的测量结果,验证了传统超声检测方法的局限性。对比了ELM与其他机器学习方法包括BP、支持向量回归(SVR)的测量结果和精度。结果表明,提出的方法有效克服了传统超声测量方法的不足,能实现不同材料不同规格的螺栓应力测量,并且测量精度更高(平均相对误差为3.86%),泛化能力更好。

基于抗差自适应滤波的高速列车融合测速算法

针对Kalman滤波在高速列车融合测速过程中因观测粗差和动力学模型误差而引起的融合精度下降问题,提出一种基于抗差自适应滤波的高速列车融合测速算法。首先,在Kalman滤波的基础上构建异常检测函数和误差判别统计量,用于检测和区分传感器异常观测导致的观测粗差和动力学模型误差;然后,针对观测粗差和动力学模型误差,分别采用三段式函数和指数函数构造抗差因子和自适应因子,通过2种因子合理调节观测信息和模型信息在状态估计中的权重,从而降低观测粗差和动力学模型误差对融合结果的影响;最后,通过2种运行场景以及算法对比,仿真验证抗差自适应滤波算法性能。仿真结果表明,与基于Kalman滤波的融合测速算法相比,所提出算法无论在观测粗差场景还是在动力学模型误差场景,均具有更高的精度和稳定性。

基于HSA-SVR的压电式车削测力仪多维力解耦

文中针对压电式多维力测力仪向间干扰大,制约测量精度的问题,分析了向间干扰对测力仪测量精度的影响,提出了一种基于支持向量回归机(SVR)的非线性解耦算法。利用混合模拟退火算法(HSA)对SVR进行参数寻优,对比并分析了HSA-SVR和线性最小二乘解耦法(LS)的解耦性能,证明经该方法解耦后向间干扰最大为0.526%,非线性误差最大为0.214%,HSA-SVR具有更好的非线性解耦效果。

重磁测量在海岛礁及其邻域中的应用与展望

重磁测量在海岛礁及其邻域的应用与研究中不可或缺。由于海岛礁及邻域重磁场空间分布的梯度和幅度变化较大,又受地理环境与技术的限制,获取该区域高分辨率、高精度的重磁测量信息较为困难,导致测量成果质量不高且产品单一,亟需科技创新来扭转此局面。本文系统分析了海岛礁及其邻域地理环境、重磁特征和相关测量技术方法的发展现状,针对存在的问题,深入探讨了该领域的新技术、新方法及应用研究,并对未来发展方向作出展望:(1)加强重磁测量技术方法的研究,构建高水平的立体观测系统;(2)深化多源数据融合处理技术方法研究,丰富测量成果的输出方式;(3)充分利用海岛礁作为定点的中长期观测平台的天然优势,开展地球物理场的融合研究,拓展其应用与研究领域。

基于多传感器信息融合的机床测量数据自动补偿系统

机床测量数据自动补偿能够减少加工误差,为提高机床加工精度,设计基于多传感器信息融合的机床测量数据自动补偿系统。首先设计温度传感器、单片机主控与补偿脉冲发生装置,获取机床运动数据,然后采用多传感器融合算法融合采集信息,对信息中误差处理,采用粗糙集理论寻找最佳补偿值,实现机床测量数据自动补偿。实验结果表明,该系统能够有效减少加工误差,可以满足实际应用要求。

基于Stacking融合模型的PHEV复合储能系统实时能量分配策略

为了解决插电式混合动力汽车单一电池低比功率、无法响应暂态功率需求的问题,设计了电池和超级电容并联式复合储能系统。同时针对采用动态规划法优化负载电流分配时缺乏实时性的问题,利用不同驱动工况下动态规划优化的结果构成训练集进行训练,并综合GRU网络以及XGBoost算法,提出了一种Stacking集成学习框架下多模型融合的能量分配策略。仿真结果表明,与仅使用单一电池的储能系统相比,基于Stacking融合模型的实时能量分配系统在UDDS和US06两种循环工况下,电池峰值电流分别降低了48.7%和50.8%,有效削弱了电池的峰值电流,提升了电池的整体性能。