基于同轴图像传感的激光焊接过程质量监测技术

针对激光焊接过程中由于参数不匹配、装配误差等原因造成的焊接过程不稳定等焊接缺陷，基于同轴图像传感技术，建立起一套激光焊接过程中的质量在线监测系统，对焊接过程中的熔池图像进行了采集分析及其熔池特征信息的提取. 结果表明，在激光功率为1 500 W的不等厚不锈钢薄板激光焊接试验条件下，焊接过程中的不稳定、下塌缺陷以及焊偏现象与熔池形状各特征信息变化具有一定的相关性. 结合BP神经网络算法对所提特征信息进行分类、识别，基于LabVIEW软件平台，可实现相应缺陷的自动化识别及报警功能.     

System identification with measurement noise compensation based on polynomial modulating function for fractional-order systems with a known time-delay

This study presents a system identification method based on polynomial modulating function for fractional-order systems with a known time-delay involving input and output noises in the time domain. Based on the polynomial modulating function and fractional-order integration by parts, the identified fractional-order differential equation is transformed into an algebraic equation. By using the numerical integral formula, the least squares form for the system identification is obtained. In order to reduce the effect of noises existing in the input and output measurements, the compensation method for the input and output noises is also studied by introducing an auxiliary high-order fractional-order system in the revised identification algorithm. Finally, the effectiveness of the proposed algorithm is verified by the simulation result of an illustrative example and the experimental result of temperature identification for a thermal system.     

两栖装甲车辆由陆入水过程中车体稳定性分析

对两栖装甲车辆水动力学模型的构建以及基于水上航行耐波稳定性的横摇运动进行了探索。首先建立了两栖装甲车辆由陆入水的动力学模型,并进行了仿真分析；而后将模型试验与融入遗传算法的系统辨识思想相结合,构建了对于两栖装甲车辆摇荡运动的辨别模型；最后通过对车辆在水上的横摇运动试验设计及侧体方案布置,对辨别模型的应用效果进行了验证。通过研究发现,水动力学模型能够对两栖装甲车辆由陆入水时的车体稳定性情况进行准确描述,基于遗传算法的系统辨识方法能够分段辨识两栖装甲车辆的横摇运动,为车辆水上航行稳定性的研究提供了借鉴。     

A framework for cloned vehicle detection

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy. It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately, and further explore the motives through discerning the behaviors of cloned vehicles. The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles, which opens up a new opportunity for cloned vehicle detection. Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold. In this paper, we propose a two-phase framework, called CVDF, to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number. In the detection phase, cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood. In the behavior analysis phase, consider the traces of vehicles that uses the same license plate will be mixed together, we aim to differentiate the trajectories through matching degreebased clustering and then extract frequent temporal behavior patterns. The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’ behavior effectively. Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.     

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

In this paper, a discrete‐time piecewise affine (PWA) model of a wind turbine during Maximum Power Point Tracking (MPPT) region is identified. A clustering‐based identification method is utilized to create PWA maps for nonlinear aerodynamic torque and thrust force functions. This method exploits the combined use of clustering, pattern recognition, and parameter identification techniques. The well‐known K‐means clustering method is employed along with a perceptron‐based multiclassifier for pattern recognition and the least squared technique for parameter estimation. The identified maps are approximated the nonlinear static functions of the dynamic model of the wind turbine. Characteristics of a 5‐MW wind turbine are considered and the resulting model, which consists of 25 subregions is compared with the nonlinear dynamic model. Two test cases are studied in order to validate the presented model. Simulation results demonstrate the effectiveness and accuracy of the PWA model such that the response of the identified PWA model is fitted well to the nonlinear one. The PWA model identified in this paper can be widely used for advanced control systems design and long‐term performance and security assessment of the power grid.     

基于HHT-RDT算法的高拱坝泄流结构工作模态识别方法

根据高拱坝泄流结构自身的工作特点,为准确辨识环境激励下的结构模态参数特征,提出了一种基于改进的HHT-RDT算法的高拱坝泄流结构工作模态识别方法。以某高拱坝原型振动响应测试资料为基础,利用改进的小波阈值-EMD算法对原始信号进行降噪预处理,滤除干扰噪声的同时保留有效特征信息;采用HHT-RDT算法识别高拱坝泄流结构的工作模态参数,运用带通滤波对振动响应信号的EMD过程进行控制得到结构的各阶模态分量,利用RDT法提取各阶模态分量的自由衰减信息,识别出高拱坝泄流结构系统的固有频率及阻尼比。工程实例表明,该方法避免了复杂系统定阶过程,有效提高结构振动响应工作模态识别精度,为辨识高拱坝泄流结构的工作模态参数提供捷径。     

Efficient parameterisation of nonlinear system models: a comment on Nöel and Schoukens (2018)

Abstract

Nöel, J. P., & Schoukens, J. [2018. Grey-box state-space identification of nonlinear mechanical vibrations. International Journal of Control, 91, 1–22] discuss a methodology for the discrete-time state-space identification of nonlinear systems and apply this to experimental data from the well known Silverbox nonlinear circuit, producing a model characterised by 13 parameters. This model explains the data very well but the parameter estimates are not well defined in the optimisation results, with the very large confidence bounds suggesting that the model is over-parameterised. This comment shows that this is indeed the case and that the data can be explained equally well by an alternative continuous-time, State-Dependent Parameter (SDP) transfer function model with only 6 parameters, the estimates of which are well defined with very tight confidence bounds. The comment also raises questions about how the model form for nonlinear systems such as the Silverbox should be identified and suggests that the Data-Based Mechanistic (DBM) approach to modelling has some advantages in this regard.     

川西彭州地区雷口坡组潮坪相薄储层辨识机理研究

川西彭州地区三叠系雷口坡组雷四上亚段潮坪相薄储层识别难度极大。围绕如何从复合地震强反射中区分并识别上、下两套储层面临的地球物理难题,采用先“分”后“合”的研究思路,基于实际地层结构及不同储层叠加样式建立正演模型,利用全波场波动方程正演模拟技术,剖析了不同主频条件下薄储层的地震响应特征,通过波形差异化分析,从复合地震响应中“剥离”出了两套储层所引起的地震响应特征及变化规律,明确了两套储层在不同频带下的地震识别标志和识别方法,为该区强反射界面干扰下两套薄互层储层辨识机理分析及精准预测奠定了基础。基于不同频带下薄储层辨识机理的分析结果,定性预测了薄储层平面展布,提出了深层潮坪相薄储层识别和预测难题的解决方案,为该区地震资料品质评价、面向薄储层的地震采集技术设计、地震资料处理及薄储层预测提供了依据和指导。     

利用非糖物质快速鉴别单花蜂蜜

本文利用非糖物质快速鉴别四种常见的单花蜂蜜(枸杞蜂蜜、荆条蜂蜜、枣花蜂蜜和洋槐蜂蜜)。利用固相萃取除糖提取蜂蜜非糖物质,并用高效液相色谱(HPLC)结合化学计量学进行蜂蜜花源的判定。发现了四种单花蜂蜜的非糖特征标志物。芦丁、丁香酸甲酯、阿魏酸的含量在四种蜂蜜中差异最大。对羟基苯甲酸和脱落酸在枸杞蜂蜜中平均含量分别为0.59μg/g和0.42μg/g,可作为枸杞蜂蜜的花源标志物;对香豆酸和肉桂酸在荆条蜂蜜中平均含量为0.08μg/g和0.05μg/g,可作为荆条蜂蜜的潜在花源标志物;阿魏酸、异阿魏酸是枣花蜂蜜的特征标志物,平均含量分别为0.40μg/g和0.76μg/g;洋槐蜂蜜的特征标志物是芦丁和丁香酸甲酯,平均含量为0.08μg/g和0.14μg/g。利用高效液相色谱指纹图谱结合化学计量学方法(主成分分析和判别分析),四种单花蜂蜜依据其花源被成功区分。本研究对蜂蜜真实性判定有重要参考价值,为后续蜂蜜真伪鉴别奠定基础。