A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

偏置剪枝叠式自编码回声状态网络的时序预测

针对大多数模型对时间序列预测数据的预测准确率较低,为提升时间序列的预测精度,提出一种基于Biased Drop-weight的偏置剪枝叠式自编码回声状态网络(BD-AE-SGESN)的深度模型。以叠式ESN为多层深度网络框架,提出一种生成式AE算法生成每一层的输入权值,利用BD算法根据输入权重激活值进行剪枝。对比实验结果表明,该模型能够有效提升预测准确率,在3个不同的数据上,相比其它模型有着较小的预测误差和较高的稳定度。

基于冲击回波法的钢衬混凝土内部缺陷探测研究

针对冲击回波法多用于单一结构厚度和缺陷检测且在组合结构缺陷识别领域应用较少的问题,提出了基于冲击回波法检测钢衬混凝土组合结构构件厚度公式。首先采用数值模拟方法探究弹性波在钢衬混凝土内部的传播特性,分析缺陷埋深对冲击回波法特征参数的影响,然后开展室内模型试验研究,进一步验证公式的准确性。结果表明,提出的冲击回波法检测组合结构构件厚度计算公式能较好地适用于钢衬混凝土构件现场检测,现场冲击回波法检测获取的卓越频率可真实反映钢衬混凝土结构混凝土内部质量缺陷。

油气水三相流相含率超声测试模型优化

油气水多相流广泛存在于油气工业过程。针对现有油气水多相流相含率模型对超声换能器测量信息利用有限这一问题,提出了一种水基分散分层流相含率超声测试模型的优化方法。利用最大粒径模型,研究了水基分散分层流中分散相粒径变化规律,确定粒径-波长比的范围处于中波长区。根据超声脉冲回波法,采用“一发三收”超声换能器测试系统,提出了水基分散分层流中超声扩散衰减的测试方法。在此基础上,结合Faran弹性散射理论,建立了油水分散流含油率超声测试模型。利用换能器渡越时间测量信息,提出了声程波动修正参数,改进了油水分散流中混合声速测试模型,并进一步优化了含气率测试模型。仿真结果表明,水基分散分层流含气率预测平均相对误差和均方根误差分别为0.43%和0.23%,含油率预测平均相对误差和均方根误差分别为3.30%和0.28%,验证了所提出的基于混合声速的相含率测试优化模型的有效性和准确性,为油气水多相流超声测试方法提供理论依据。

工程类专业学位研究生产教融合系统运行模式研究——复杂适应系统理论的分析

工程类专业学位研究生产教融合是产业系统和教育系统互补互融、双向赋能、价值共创的实践,二者共同支撑着工程类专业学位研究生教育高质量发展,具有聚集性、非线性、“流”特性、多样性等特征。借鉴复杂适应系统理论的“刺激-反应”模型与回声模型,本研究提出了工程类专业学位研究生产教融合系统的单主体自适应微观行为模式与多主体交互宏观行为模式。面对环境的复杂性和不确定性,推动工程类专业学位研究生产教融合系统构建要主体联动,发挥产教融合系统生态圈的共生效应;资源统整,建立全链条式异质资源跨界联盟;机制创新,探索产教融合系统多层次调控机制。

基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术

基于光纤陀螺的测角仪可以实现对各项角运动参数的一体化动态精密测量,但在实际应用中,光纤陀螺测角仪受到温度变化的影响,导致测量精度下降。针对这一问题,本文提出了一种基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术。为了提高温度误差建模的进度,提高传统神经网络的逼近能力,通过自适应前向线性预测滤波器对建模用测角仪温度漂移数据进行预处理,并采用自适应小波回声神经网络建立温度漂移模型,能够避免传统神经网络结构设计的盲目性和局部最优等问题,增强了网络学习能力和泛化能力,并利用自适应律代替神经网络梯度进行网络训练,提升神经网络的逼近精度和收敛速度。实验结果表明,该模型可以提高光纤陀螺测角仪的测量精度和环境适应性,为光纤陀螺测角仪的性能优化和实际应用提供了可靠的技术支撑。

气象信息网络传输中数据压缩与传输效率优化研究

气象信息传输是气象监测的前提,气象监测对气象数据的准确性、实时性等要求严格。面对海量气象数据,必须对数据进行有效压缩与传输,才能发挥气象信息数据的价值。以气象信息网络传输中的数据压缩与传输效率优化为中心,运用理论研究法与文献分析法拓展数据压缩与传输效率优化路径,以期为未来工作研究提供参考。

双块式无砟轨道轨枕与道床板界面脱空损伤识别

为准确识别双块式无砟轨道轨枕与道床板连接面脱空损伤,建立了双块式无砟轨道冲击回波有限元模型,分析界面损伤对弹性波传播规律的影响。在室内浇筑含有不同损伤位置和不同损伤程度的混凝土复合试件,采用冲击回波法进行损伤识别。研究结果表明:脱空损伤会显著影响弹性波在界面处的传播,基于回波信号分析可识别损伤;弹性波频率主要受激振锤大小的影响,应结合结构易损位置和理论计算选取合适的激振锤;混凝土复合试件的试验厚度值与理论厚度值相差仅为1 cm,测试结果合理,可用于判断脱空损伤程度,但不能确定损伤区域;利用平面成像技术可准确识别轨枕与道床板界面脱空范围,为双块式无砟轨道结构养护维修提供一定参考,试验结果验证了冲击回波法用于双块式轨道层间脱空识别的可行性和合理性。

基于PLESN和LESQRN概率预测模型的短期电力负荷预测

针对现有电力负荷预测不能很好反映负荷数据的周期性和趋势性以及残差的波动性特征提出一种考虑周期性建模的泄露积分型回声状态网络点预测模型和泄露积分型回声状态分位数回归网络概率预测模型组合的短期电力负荷预测方法.首先为了捕捉负荷的多重特征定义了周期性和趋势性损失函数辅助优化点预测模型然后为克服残差的波动问题利用概率预测模型对点预测值与真实值的残差进行建模预测最后整合同时刻的点预测值与残差预测区间得到概率预测模型结果.实际算例结果表明与其他模型相比所提模型不仅有效抑制尖端振荡现象而且能够生成可靠的概率密度分布.

具有双储层结构的动态误差补偿回声状态网络

针对传统回声状态网络难以有效应对高阶非线性复杂模型问题,本文在理论分析的基础上提出了一种双储层结构的误差补偿回声状态网络,并设计了该网络的学习算法.该网络由计算层和补偿层构成,计算层主要承担拟合任务,补偿层则作为状态跟随器,实时补偿由于计算层对期望方差估计不足而导致的幅值偏差.对多阶振荡器和真实高阶非线性数据集的实验结果表明,本文所提网络结构较常规网络具有更高的稳定性和泛化性能,尤其对高阶非线性复杂模型的预测精度大幅度提升.

英语专业基础英语ECHO教学模式的探索

基础英语是英语专业基础阶段最重要的主干课。传统的基础英语教学模式以教师为中心,忽视对学生交际能力和思维能力的培养。本文在分析基础英语教学现状的基础上,提出了进行英语专业基础英语ECHO教学模式改革,介绍了其内容、形式和教学方法,以期对基础英语课程改革提供一些可行性建议。

江西武宁县短时强降水时空分布及回波特征分析

为了更好地监测预警暴雨或大暴雨的出现,短时强降水的监测预警十分重要。使用文献气象要素、MICAPS天气资料、雷达拼图数据,采用常规分析方法,对武宁县2012—2021年短时强降水个例进行分析,结果表明:1)武宁县国家站共出现19次短时强降水,区域站(53个)共出现611次短时强降水,最大为船滩镇区域90.4 mm/h,有10个区域站最大值超过70 mm/h。2)地面气象要素瞬间风速总体较平稳,变化不大,风速不超过10 m/s;降水量具有突变特点,有时伴有2个降水高峰;气温变化不大,比较平稳;相对湿度比较高,降水前后都维持在90%以上的高位;水汽压运行也比较平稳;本站气压波幅较大,短时强降水出现在本站气压较低值时。3)武宁县国家站短时强降水往往是由于大范围混合型降水回波中出现的短带回波所致。当短带回波的走向与回波系统的移向趋于一致时,正好经过武宁县国家站,形成“列车效应”产生短时强降水。武宁县区域站短时强降水大部分由于回波单体所致。回波单体演变也多种多样,有时孤立存在,有时三五一群,有时排列成不连续(不相接)短带。武宁县短时强降水有2种回波概念模型:一是在大片絮状回波团(带)中出现的短带回波,二是对流回波单体,有时孤立存在,有时几个回波单体同时存在,相互影响。上述研究结果为武宁县短时强降水监测预警提供了分析依据。

ECHO教学模式下的英语专业基础英语教学

基础英语是高校英语专业基础阶段最重要的必修课程。传统的基础英语教学以教师为中心,侧重于词汇和语法讲解,忽视了对学生交际能力、思辩能力和人文素质的培养。在对学生问卷访谈并总结基础英语教学现状的基础上,该文结合实际课堂教学,介绍了英语专业基础英语ECHO教学模式,希望能对基础英语课程教学模式改革的研究做出一点贡献。

Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation tasks.However,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic systems.In this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb exoskeleton.The dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain subsystems.To facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control signal.The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics.Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

Impacts of space-time-frequency synchronization errors onwide-band target echo characteristics of bistatic/multistatic radar

Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.

Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment

During high-intensity focused ultrasound(HIFU)treatment,the accurate identification of denatured biological tissue is an important practical problem.In this paper,a novel method based on the improved variational mode decomposition(IVMD)and autoregressive(AR)model was proposed,which identified denatured biological tissue according to the characteristics of ultrasonic scattered echo signals during HIFU treatment.Firstly,the IVMD method was proposed to solve the problem that the VMD reconstruction signal still has noise due to the limited number of intrinsic mode functions(IMF).The ultrasonic scattered echo signals were reconstructed by the IVMD to achieve denoising.Then,the AR model was introduced to improve the recognition rate of denatured biological tissues.The AR model order parameter was determined by the Akaike information criterion(AIC)and the characteristics of the AR coefficients were extracted.Finally,the optimal characteristics of the AR coefficients were selected according to the results of receiver operating characteristic(ROC).The experiments showed that the signal-to-noise ratio(SNR)and root mean square error(RMSE)of the reconstructed signal obtained by IVMD was better than those obtained by variational mode decomposition(VMD).The IVMD-AR method was applied to the actual ultrasonic scattered echo signals during HIFU treatment,and the support vectormachine(SVM)was used to identify the denatured biological tissue.The results show that compared with sample entropy,information entropy,and energy methods,the proposed IVMD-AR method can more effectively identify denatured biological tissue.The recognition rate of denatured biological tissue was higher,up to 93.0%.

Main Features of a Complete Ultrasonic Measurement Model: Formal Aspects of Modeling of Both Transducers Radiation and Ultrasonic Flaws Responses

This paper aims at describing the theoretical fundamentals of a reciprocity-based ultrasonic measurement model. This complete inspection simulation can be decomposed in two modeling steps, one dedicated to transducer radiation and one to flaw scattering and echo synthesis. The physical meaning of the input/output signals used in these two modeling tools is defined and the theoretical principles of both field calculation and echo computation models are then detailed. The influence on the modeling results of some changes in the simulated configuration (as the incident angle) or some input signal parameters (like the frequency) are studied: it is thus theoretically established that the simulated results can be compared between each other in terms of amplitude for numerous applications when changing some inspection parameters in the simulation but that a calibration for echo calculation is generally required.

利用ECHO模型对高血压病药物治疗管理结果评价的研究综述

高血压由于其患病率广,成本高,控制率差,在中国是一个主要的公共卫生问题。近年来,美国实施的药物治疗管理(MTM)服务在减轻疾病负担方面取得了良好的效果。Kozma等于1993年提出的经济、临床和人文产出(ECHO)模型是现阶段普遍应用的评价药学服务产出的模型之一。本文从经济性、临床性和人文性3个方面对高血压病的MTM效果进行了评价,结果表明,MTM能以较低的成本提高患者的临床疗效和生活质量,患者对MTM服务的效果满意,MTM在高血压疾病治疗中有较好的作用,可为今后临床药师的发展提供参考。

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.

融合注意力机制的CS-BiLSTM深度回声消除算法

在全双工通信系统中,声学回声会降低用户的体验,针对在双向通话场景下自适应滤波算法消除声学回声效果不理想以及非线性声学回声难以消除的问题,提出一种注意力机制与BiLSTM网络相结合的CS-BiLSTM深度声学回声消除算法。首先通过构建BiLSTM网络提取语音的时序特征,之后引入通道和空间注意力机制提取回声信号的空间特征信息,并融合均方根误差与平均绝对误差提出一种新的损失函数,提高模型的鲁棒性。改进后的CS-BiLSTM网络模型能够获得清晰的语音信号,具有更好的回声消除性能。仿真结果表明,在非线性回声和双向通话环境下,与其他几种参考算法相比,所提出的CS-BiLSTM算法在感知语音质量评价方面明显优于其他算法,更有效地实现了回声消除,此外,该算法结构简单且模型参数量更少。