Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.

An Investigation of Frequency-Domain Pruning Algorithms for Accelerating Human Activity Recognition Tasks Based on Sensor Data

The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)efforts.However,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems.This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity inference.Unlike traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for analysis.It emphasizes the low-frequency components by calculating their energy spectral density values.Subsequently,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational costs.Notably,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone architecture.The computational feasibility and data sensitivity of the proposed scheme are thoroughly examined.Impressively,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,respectively.Concurrently,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.

Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network

Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.

A New Transient Impedance-Based Algorithm for Earth Fault Detection in Medium Voltage Networks

This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.

Analysis of Transforming dq Impedances of Different Converters to A Common Reference Frame in Complex Converter Networks

DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.

The application of elastic impedance inversion in reservoir prediction at the Jinan area of Tarim Oilfield

The Triassic reservoir in the Jinan area of Tarim Oilfield consists largely of interbedded sand and shale. Because of the large overlap between sandstone and shale impedance, it is difficult to distinguish sandstone from shale by acoustic impedance alone. Compared to acoustic impedance, elastic impedance contains more lithologic and physical information of the reservoir. Based on meticulous well-tie calibration, elastic impedance data volumes for 10°, 20°, and 30° emergence angles are obtained using pre-stack elastic impedance inversion. A non-linear statistical relationship between elastic impedance and shale content is set up by a PNN neural network. The non-linear mapping relationship is used to predict the reservoir shale content from elastic impedance, which will depict and predict the reservoir oil-bearing sands.

A comparison of deep learning methods for seismic impedance inversion

Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This paper is dedicated to comprehensively studying on the significant aspects of deep neural networks that affect the inversion results.We experimentally reveal how network hyperparameters and architectures affect the inversion performance,and develop a series of methods which are proven to be effective in reconstructing high-frequency information in the estimated impedance model.Experiments demonstrate that the proposed multi-scale architecture is helpful to reconstruct more high-frequency details than a conventional network.Besides,the reconstruction of high-frequency information can be further promoted by introducing a perceptual loss and a generative adversarial network from the computer vision perspective.More importantly,the experimental results provide valuable references for designing proper network architectures in the seismic inversion problem.

Robust Impedance Control of Robots Using an Adaptive Interaction Force Observer

For robot interaction control,the interaction force between the robot and the manipulated object or environment should be monitored.Impedance control is a type of interaction control.Specifically,in impedance control,the dynamic relationship between the interaction force and the resulting motion is controlled.In order to control the impedance of a mechanical system,typically,the interaction force has to be sensed.Due to the inherent limitations of direct force sensing at the interaction site,in the present work,the interaction force is observed using robust observers.In particular,to enhance the accuracy of impedance control,a first order sliding mode impedance controller is designed and incorporated in the present paper.Its advantage over positionbased interaction control algorithms is demonstrated through experimentation.Experimental results are given to show the effectiveness of the proposed algorithms.

基于电磁时间反演P范数判据的配电网故障定位

目前电磁时间反演(electromagnetic time reversal,EMTR)多应用在单一线路故障定位,且现有判据在高阻抗接地情况下效果不理想。针对上述问题,基于EMTR故障定位原理和均匀传输线理论推导了传播过程中线路故障信号与测量信号的传递函数,根据传递函数的相关性提出了P范数判据。利用ATP-EMTP搭建10 kV配电网线路,对比了2范数与P范数判据在复杂配电网中的定位性能,并验证了所提判据在混合配电网线路的适用性。最后,分析了配电网发生低阻抗及高阻抗接地故障下P范数判据的鲁棒性。仿真结果表明,该方法在过渡电阻高达3 kΩ的情况下能准确定位,且定位精度高,受噪声、故障类型和采样频率的影响小。

脉冲晶闸管热网络模型及运行状态评估方法

针对脉冲晶闸管承受强流脉冲导致的瞬态电磁热力联合冲击产生的热疲劳累积突发性失效问题,提出了在脉冲晶闸管内布置温度传感器的状态监测方法,建立了脉冲晶闸管的高阶瞬态热阻抗网络模型,分析了强流脉冲作用下脉冲晶闸管内部不同层的温度分布规律,并通过理论计算验证了分析方法的有效性。计算结果表明,热网络模型计算相对偏差小于5%。可为脉冲晶闸管型强流开关的状态评估提供理论指导。

考虑出行阻抗的城市群综合客运网络可达性

为阐明城市群综合客运交通网络的多层空间结构并丰富网络的功能属性,以耦合这一全新视角提出考虑城市交通换乘的城市群综合客运网络模型,并从出行费用与出行时间出发分析节点与网络的可达性。首先,基于复杂网络理论Space-P建模方法,将城市群中的客运站点抽象为网络中的节点并进行编号,以票价与出行时间标定边权,分别构建公路客运子网、铁路客运子网。其次,划设城市群中的各城市中心城区为独立的交通小区,将有城市公共交通、步行等换乘方式连接的节点间添加耦合边,构建耦合子网。再次,将两类客运子网通过耦合子网组合叠加为综合网络,改进传统网络效率指标,构建加权的阻抗效率指标评估节点与网络的阻抗可达性。最后,以成渝城市群为实例研究,构建成渝城市群综合客运网络并分析公路、铁路、综合网络的阻抗可达性。结果表明:多个节点的耦合使得这些节点的阻抗可达性较好,因此在研究城市群综合客运交通网络时,城市交通换乘是不可忽略的因素;综合网络的阻抗可达性相较于公路子网、铁路子网分别提升了83.4%,28.5%,公路与铁路客运子网的耦合提高了城市群客运交通网络的阻抗可达性;推进站点间、多种运输方式间的耦合协调,继而构建更加便捷顺畅的城市群综合客运交通网络,是降低旅客的出行成本的重要途径。

反共振隔振器结构综合

为解决反共振隔振器结构正向分析方法存在的组合不充分性、构型规律不清晰等问题,本文提出一种逆向综合方法。将反共振隔振器作为机械网络,根据新机电严格相似性,先建立了“惯容-弹簧”机械网络综合理论,在阻抗综合和导纳综合2种方法基础上发展了混合综合方法。推导了反共振隔振器传递函数的一般表达式,阐明了传递曲线在隔振区还存在以负斜率衰减的第2种特征形态。以反共振特性为技术条件,根据所述综合方法得到了完备的“惯容-弹簧”反共振隔振器结构,由单惯容、单弹簧和周期性串、并联的“惯容-弹簧”二元件等4种结构单元组成,分析了构型规律,实现了从单频点结构到多频点结构的推演性,并讨论了阻尼影响。本文示例了悬臂端带质块的双简支-悬臂梁作为“惯容-弹簧”二元件串联结构的一种实现形式,验证了所综合的反共振隔振器结构的正确性。本文为工程上设计具有不同隔振特性的反共振隔振器结构提供了理论指导。

基于DREAM_ZS算法的EIT电阻率反演方法研究

针对电阻抗成像(EIT)中的电阻率反演及其不确定性量化问题,提出基于贝叶斯理论的不确定性分析方法.首先,利用反向传播(BP)神经网络模型作为正问题替代模型,取得了计算精度高的结果,并且大大提高计算效率.然后,采用基于贝叶斯理论的自适应差分进化Metropolis抽样(DREAM_ZS)算法对电阻率进行反演,并对不同激励模式和不同先验分布进行了对比分析.对模拟头部的4层同心圆模型的反演结果显示,DREAM_ZS抽样算法能够对4个参数进行准确识别,相对激励模式的反演效果最优.4个参数的不确定性程度不同,头皮电阻率不确定性最小,敏感性最强,其次是颅骨,大脑和脑脊液的不确定性较大.进而,对高维参数的圆模型进行仿真,采用相对激励模式,DREAM_ZS抽样算法能够准确反演二维圆模型的各个参数.参数的先验分布为正态分布时,与均匀分布相比,其反演结果不确定性小,对算法的识别效果更强.

基于EMI-CNN的建筑施工模板支撑体系节点健康监测

为预防模板坍塌引发建筑施工安全事故风险,提出一种基于压电阻抗法(EMI)和卷积神经网络(CNN)的模板支撑体系节点智能监测方法。首先,利用压电陶瓷传感器(PZT)的机电耦合特性及其集驱动-传感于一体的特点,建立PZT-节点耦合系统的机电阻抗传感机制模型;其次,基于EMI法,以与待测结构耦合的PZT片电导信号为监测指标,确定模板支撑体系节点松动的发生;然后,以敏感频段内PZT片的801个原始电导信号为模型输入,9个节点松动程度为模型输出,构建162组学习样本和27组测试样本,建立EMI-CNN模型,确定节点松动程度;最后,以一个实际工程中的建筑施工模板体系节点为例,验证EMI-CNN模型的有效性,并对比分析EMI-BP模型。研究结果表明:EMI-CNN模型经过85次迭代达到收敛,预测准确率达到100%,相较于EMI-BP模型提高29.63%。该监测方法可实现对建筑施工模板支撑体系节点健康状态实时、准确、无损监测。

基于多层残差网络的地震提频处理在薄储集层识别中的应用

基于多层残差网络的地震提频处理方法,通过智能化网络将测井高频信息与地震数据相结合,能有效提升纵向分辨率,保持横向连续可追踪,利于薄储集层识别。针对AMH地区常规处理的地震数据仅能识别厚度大于30 m的碳酸盐岩层,无法有效识别厚度较小的薄储集层的问题,提出基于多层残差网络的地震提频处理方法,以井旁地震振幅作为训练数据,测井相对波阻抗作为训练标签,利用深度学习网络多层残差网络开展训练,获取相对波阻抗曲线的预测模型;通过将地震数据作为输入,利用深度网络训练模型计算得到相对波阻抗数据体,进而得到提频后的地震数据体相对应的反射系数体。通过对靶区地质情况的分析认识,对宽频子波进行标定后提取合适的宽频子波,与反射系数体进行褶积,得到提频后的地震数据体;利用提频后的地震数据体开展储集层反演,反演结果纵向具有较高分辨率,与主要目的层能够较好匹配,横向可以进行识别和追踪,利用高分辨地震数据反演结果实现AMH地区的薄储集层识别。结果表明,通过基于多层残差网络的地震提频处理及相应的高分辨模型反演,在AMH地区能够识别厚度大于10 m的薄储集层,较好地解决由于地震分辨率低无法识别薄储集层的问题,有效提高了薄储集层预测的精度,对同类型薄储集层识别具有借鉴意义。

稀疏井网下隔夹层精细预测方法研究及效果分析

在薄层预测领域,地质统计学和波形指示反演技术是当前的两大主流方法。然而,地质统计学反演在井点稀疏区域构建精确变差函数方面存在挑战,难以实现对薄层的准确预测。尽管波形指示反演对井网分布的依赖性较小,但其高频成分在描述地震振幅的空间变化方面存在不足。为了克服这些限制,提出了叠前构形反演技术。该技术通过对比地震数据体中能量和频率特征的横向变化,有效预测目标岩性的空间分布。充分利用了地震数据体的横向信息,其高频信息与地震数据体的空间能量变化高度吻合。通过射线域弹性阻抗分析,该方法能够提取更多弹性参数,其结果与AVO特征相一致,从而更准确地反映叠前地震数据不同射线域能量的横向变化,精确预测多种岩性类型。在稀疏井网的研究区,对于复杂岩性隔夹层的预测具有显著优势。该方法在多个油田的实际应用中已显示出卓越的效果,对于1 m以上夹层的预测准确率超过80%,明显优于传统的反演技术,特别适合于稀疏井网区域的复杂岩性隔夹层预测。

基于动态磁网络法大型感应电机阻抗参数及起动特性计算

为提高大型感应电机动态阻抗参数及起动特性计算的准确性及计算效率,构建了考虑电机转子转速变化引起气隙磁导变化的非线性动态磁网络模型。以一台6.5 MW大型感应电机为例,分析起动电流变化对电机内各处饱和程度的影响机理,在此基础上,根据磁力线路径及电机内饱和程度确定动态磁网络模型中的非线性磁导的计算方法。将动态磁网络模型与转子趋肤效应模型相结合,提出计及漏磁并考虑转子转速变化及饱和偏移现象的电机动态阻抗参数的计算方法。推导并建立基于转子多回路法的大型感应电机的动态数学模型,根据电机瞬态过程中的电磁耦合关系,确定动态数学模型与磁网络模型的动态耦合求解方法,提出大型电机瞬态过程中考虑电流、转速、饱和及趋肤程度变化的动态阻抗参数及起动特性的计算方法。通过与有限元计算结果及实验结果对比,验证了所提出计算方法的正确性。

考虑近邻度值之和的城市轨道网络抗毁性研究

为解决城市轨道交通站点或线路失效引发的网络级联瘫痪问题,考虑了网络节点一阶邻域的影响作用,基于非线性容量负载模型,提出了负载分配阻抗系数,并建立了考虑近邻度值之和的非线性容量负载优化模型,通过优化负载结构来调整节点在负载重分配时的备择概率,减少级联过程中节点的失效数,提高网络抗毁性。以重庆市轨道网络为实例应用,仿真分析网络在两种模型下的抗毁性。结果表明:优化模型中负载容忍系数的增大对网络抗毁性的改善效果更显著;优化模型中节点的负载分配阻抗系数越大,节点在负载重分配时的备择概率越低,越不容易发生级联过载。

基于神经网络参数自学习的阻抗控制

针对机器人在打磨过程中环境刚度和位置未知,传统的阻抗控制难以有效保持打磨质量的问题,提出了一种基于调节参数神经网络自学习的阻抗控制。由于基于李雅普诺夫稳定性理论设计的阻尼参数补偿方法中调节参数的选取直接影响系统的控制性能,根据阻尼补偿的数学描述,构建神经网络,用于其参数自适应调节,设计不同的激励函数用于反映阻尼在多种因素影响下变化的特征。通过所搭建的神经网络在线学习,实现参数的优化,以适应打磨过程环境变化。在斜面、平面及曲面等不同环境下,考虑其刚度突变、刚度动态变化、期望力动态变化等因素的仿真实验,结果表明所提出的控制方法与传统控制方法相比,具有更小的超调和稳态误差,并能够适应环境参数未知的情况,明显提高打磨质量和效率。