Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

Grouting defects are an inherent challenge in construction practices,exerting a considerable impact on the operational structural integrity of connections.This investigation employed the impact-echo technique for the detection of grouting anomalies within connections,enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes.A series of grouting completeness assessments were meticulously conducted,taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement.The findings revealed that:(i)the energy distribution for the highstrength concrete cohort predominantly occupied the frequency bands 42,44,45,and 47,whereas for other groups,it was concentrated within the 37 to 40 frequency band;(ii)the delineation of empty sleeves was effectively discernible by examining the wavelet packet energy ratios across the spectrum of frequencies,albeit distinguishing between sleeves with 50%and full grouting density proved challenging;and(iii)the wavelet packet energy analysis yielded variable detection outcomes contingent on the material attributes of the sleeves,demonstrating heightened sensitivity when applied to ultrahigh-performance concrete matrices and GFRP-reinforced steel bars.

Enhanced air-coupled impact echo technique by phase analysis of signals from multiple sensors

This paper presents an air-coupled impact echo(IE)technique that relies on the phase spectrum of the collected data to find the frequencies corresponding to the reflections from delaminations.The proposed technique takes advantage of the fact that the IE compression wave is not a propagating wave,but it is the 1st order symmetrical(S1)mode Lamb wave at zero group velocity(S1-ZGV).Therefore,it searches the phase spectra of the data collected by multiple sensors to locate the frequency corresponding to the lowest phase difference.As a result,the technique reduces the effect of propagating waves,including the direct acoustic wave and ambient noise.It is named the Constant Phase IE(CPIE).The performance of the CPIE is experimentally compared with the regular amplitude spectrum-based IE technique and two other multisensor IE techniques.The CPIE shows a performance advantage,especially in a noisy environment.

CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Comparison of the reaction of polar mesosphere winter echoes and polar mesosphere summer echoes to high-frequency heating in terms of modulated characteristics

In this work,for the first time,we have analyzed and compared the responses of polar mesosphere winter echoes(PMWE)and their summer counterpart,polar mesosphere summer echoes(PMSE),to high-frequency(HF)heating in terms of modulated characteristics(i.e.,backscatter intensity reduction,recovery,and overshoot).Both PMWE and PMSE observations were from the same site(Tromsφ,Norway;69.6°N,19.2°E)and radar(EISCAT[European Incoherent Scatter Scientific Association]very high frequency,224 MHz).The heating patterns of both PMWE and PMSE were found to be similar;however,PMSE was more greatly affected by HF heating.Polar mesosphere summer echoes showed recovery and overshoot more frequently than did PMWE.In addition,the mean recovery and overshoot of PMSE were greater than those of PMWE.The associated electron temperature enhancement was estimated for both PMWE and PMSE and showed that,compared with PMWE,the electron temperature enhancement was more significant in PMSE.The strong heating effects on PMSE may be due to the considerable increase in electron temperature.

Echo State Network With Probabilistic Regularization for Time Series Prediction

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.

Gravitational wave echoes from strange quark stars in the equation of state with density-dependent quark masses

According to the recent studies,the gravitational wave(GW)echoes are expected to be generated by quark stars composed of ultrastiff quark matter.The ultrastiff equations of state(EOS)for quark matter were usually obtained either by a simple bag model with artificially assigned sound velocity or by employing interacting strange quark matter(SQM)depicted by simple reparameterization and rescaling.In this study,we investigate GW echoes with EOSs for SQM in the framework of the equivparticle model with density-dependent quark masses and pairing effects.We conclude that strange quark stars(SQSs)can be sufficiently compact to possess a photon sphere capable of generating GW echoes with frequencies in the range of approximately 20 kHz.However,SQSs cannot account for the observed 72 Hz signal in GW170817 event.Furthermore,we determined that quark-pairing effects play a crucial role in enabling SQSs to satisfy the necessary conditions for producing these types of echoes.

Analysis of echo signal modulation characteristic parameters on aerial and space targets

Based on the scattering characteristic,the comparison of RCS(radar cross-section)at different positions of a target in the same direction of incidence can be obtained first by extruding or deleting part of the entity.A simulation method of aerial&space targets echo characteristics(A&STEC)is proposed that is universal to aerial and space targets.We utilize a fixed-wing UAV(unmanned aerial vehicle)and typical missiles in simulation.The echo signal modulation characteristic parameters are calculated theoretically by the atmospheric attenuation model,the finite element method and a MUMPS solver.The verification simulations show that this method can analyze the influence of the target shape,incident direction,detection position and detection frequency on echo waveform,intensity and energy distribution.The results show that the profile of echo waveform can invert the general shape of the target.The relationship between time and intensity can determine whether the target is moving towards or away from the detector in addition.These conclusions can provide a reference for the ballistic missile target tracking and the defense against UVA intrusion in theory.

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting.However,traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data.Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors.Moreover,it is difficult to obtain higher accuracy by relying on a single historical radar echo observation.Therefore,in this study,we constructed the Fusion GRU module,which leverages a cascade structure to effectively combine radar echo data and mean wind data.We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions.Based on the Jiangsu Province dataset,we compared some models.The results show that our proposed model,Cascade Fusion Spatiotemporal Network(CFSN),improved the critical success index(CSI)by 10.7%over the baseline at the threshold of 30 dBZ.Ablation experiments further validated the effectiveness of our model.Similarly,the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.

Echo state network based symbol detection in chaotic baseband wireless communication

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

随钻核磁共振仪器缩短回波间隔方法优化设计

核磁共振测井是一种直接探测储层孔隙流体性质的地球物理方法,随钻核磁共振技术是在钻井过程中实时获得油气藏评价参数的技术方法.面对复杂油气藏中随钻核磁共振测量问题,通过缩短脉冲序列的回波间隔,可以减少仪器测量时间,能够获得快速衰减的短弛豫组分,实现储层孔隙结构的精细表征.本文提出了一种多级能量泄放方法,能够逐级降低天线品质因数,缩短天线恢复的死区时间,在不改变回波采集信噪比的情况下,实现脉冲序列的短回波间隔采集系统设计,并在随钻核磁共振测井仪器实验样机上进行了测试验证.测试结果表明,仪器回波信号信噪比良好,振铃噪声与回波信号区分明显,测试结果与模拟仿真结果一致,能够实现短回波间隔数据采集,验证了方法的有效性和实用性.

天气雷达空中生态监测

每年数以亿计的昆虫、候鸟和蝙蝠等空中动物在全球范围内远距离迁飞,显著影响空中生态系统的结构和功能。随着全球气候和环境的不断变化,空中生态环境也在迅速恶化,对人类的生命和健康构成了严重威胁。为了保护空中生态环境,首要任务是对其进行有效监测。天气雷达具有大范围、全天时、全天候监测能力,可覆盖百千千米空间尺度,是监测和研究宏观空中生态的有效工具。十余年来,天气雷达空中生态监测领域取得了突破性的进展,极大提高了对空中迁飞动物的探测能力,可有效弥补传统监测手段存在的覆盖范围小和时间分辨率低等技术局限。天气雷达已在全球范围内的生物多样性保护、迁飞性虫害监测、疫源疫病传播防控等重大空中生态议题中发挥着日益重要的作用,有效填补了宏观生态研究空白。目前,美国、欧洲和中国分别建立了全球三大天气雷达网,为空中生态研究提供了重要的基础设施和数据支持。本文详细阐述了当前全球天气雷达空中生态监测的科学研究进展,以及通过天气雷达揭示的空中动物迁飞科学规律。然后,对天气雷达生态监测中迁飞动物的电磁散射建模、雷达回波提取、群体参数反演、雷达观测验证实验,以及宏观迁飞模型等关键技术进行了梳理和总结,并详细介绍了中国天气雷达空中生态监测的进展情况。最后,对未来发展方向进行了展望,指出针对中国当前农业迁飞性害虫和鸟类宏观迁飞等重大监测需求,需要在扩维探测、多参反演、多源预测等方面深入研究和探索,以进一步解读重大动物迁飞事件、分析宏观迁飞规律、揭示空中生态对气候变化的响应。

M8激光雷达回波的非接触式发射率三维分布测量方法

针对目前发射率测量方法只是对某一单一物质进行接触式测量,难以获得复杂目标的发射率三维分布问题,提出一种基于M8激光雷达回波的非接触式发射率三维分布测量方法。首先基于激光雷达传输距离方程分析回波强度特性,通过M8激光雷达对95%标准漫反射板进行扫描,叠加多帧单线点云,得到具有反射光谱特性的雷达强度三维点云像;运用分段多项式模型拟合距离-强度及入射角-强度之间的关系,基于得到的分段多项式模型校正距离和入射角度影响下的回波强度,使不同距离与入射角情况下所测回波强度能够真实地反映目标的反射光谱特性,校正结果显示,标准漫反射板回波强度变异系数分别从0.2676和0.3438降低到了0.0420和0.0412,回波强度一致性分别提高了84.31%和88.02%,使雷达所测得强度值能直接反映目标材料的反射光谱特性,验证了强度校正模型的有效性;最后基于得到的分段多项式校正模型对带有反射率真值贴片的缩比卫星模型雷达强度三维点云像进行强度校正,利用校正后的回波强度的反射光谱特性,计算出目标表面的反射率,运用反射法进一步推导其发射率,得到缩比卫星模型发射率的三维分布,三组卫星模型表面贴片的发射率平均偏差均分别能控制在3.33%、4.84%和4.44%。为目标识别、低空可探测技术等提供技术支持。

基于激光雷达回波信号的雾天船舶航线快速辨识

激光雷达所处环境噪声较大,船舶航线辨识困难,为解决该问题,提出基于激光雷达回波信号的雾天船舶航线快速辨识方法。采用信号累积增强算法增强雾天激光雷达回波信号,利用改进经验模态分解算法去噪处理增强后的回波信号,结合回波信号通过最小类内方差获取得到船舶航线和水面最佳分割阈值,提取船舶航线特征点。利用最小二乘法拟合法拟合船舶航线特征点,完成雾天船舶航线辨识。实验结果表明,所提方法应用后激光雷达回波信号更平滑,输出信噪比更高,均方根误差更低,船舶航线辨识准确率更理想。

雷达制导半实物仿真目标速度控制算法优化

针对末制导雷达半实物仿真试验中回波模拟质量问题,分析了时统不一致对回波模拟信噪比的影响,通过改进的中值滤波算法,优化了射频仿真系统中目标速度控制算法,有效消除了重帧、丢帧对回波信号的影响,大大提升了回波信噪比,保证了射频半实物仿真系统仿真结果的可信度。

基于宽窄带综合的飞机机型识别方法

针对传统单模态雷达飞机目标细粒度识别能力不足的问题,提出一种基于宽窄带综合的飞机机型识别方法.通过卷积网络挖掘宽带一维距离像的局部散射特性,递归神经网络挖掘窄带微动回波不同时刻的上下文特性,达到信息互补的目的.同时,针对两种模态在决策融合时存在强弱不平衡问题,设计了一种自适应决策融合网络,实现了对飞机目标的机型识别.利用七种飞机机型的仿真宽窄带数据进行了实验验证.仿真结果表明,本文方法平均识别准确率为76.7%,较传统方法提升6.4%以上.

应用脉冲回波的避雷线断线检测系统研究

为高效率、低成本地检测风力发电机组桨叶避雷线断线位置,设计基于脉冲回波的避雷线断线检测系统。该系统以单端行波法为原理,由信号发生模块,传感器模块,信号采集模块、控制器模块四部分构成。信号发生模块负责产生行波脉冲信号,传感器模块负责采集回波信号,信号采集模块负责将采集到的回波信号处理转化送给控制模块,控制模块负责分析回波信号,判断故障类型和故障点位置。该文对各个模块的电路和性能参数进行设计,搭建整体的检测系统。经实验验证,该系统可以精准的实现断线判断,并获得断线位置,测量误差精确到0.5 m以内。

空天目标雷达智能识别仿真系统设计与实现

雷达智能识别方法训练、测试与评估通常需要构建大样本的数据集,如何快速获得有效的数据集是亟待解决的问题。分析了空天目标雷达智能识别系统应用场景,并结合应用需求给出了空天目标雷达智能识别仿真系统建设思路;采用组件化、模块化设计方法,设计空天目标雷达智能识别仿真系统框架,构建空天目标智能识别仿真系统,快速准确模拟空天目标电磁特性和动态回波,具有可扩展、可重构能力。仿真结果和初步应用表明,该系统可用于不同应用场景下空天目标雷达智能识别方法的测试与评估。

社交媒体时代“回音室”与“过滤泡”之辨析

在国内研究对信息茧房、回音室、过滤泡三者概念常常混用的情况下,相较于信息茧房较高的关注度,国内对后两者的研究还远远不够,二者的异同也未得到细致辨析。经过对回音室、过滤泡相关研究进行文献计量分析,并从概念提出的背景、概念的内涵、操作化和研究结论方面深入辨析,发现二者概念的相同点在于:它们所描述的信息环境是相似的,即在网络技术的支持之下,一个由与个体喜好/倾向相匹配的同质化信息构成的信息环境。而不同之处在于:回音室强调受众主动选择与己具有相同倾向的个体进行交流互动,从而陷入只能听到自己声音的回音室中,其实质是观点同质化,所导致的后果是受众观点的极化,受众本身常被视为是主动性的;过滤泡强调算法推荐导致受众陷入同质化信息环境之中,其实质是信息接触单一化,导致的主要后果包括受众认知的窄化和观点的极化,受众往往被视为是被动性的。

凝视与想象:《回响》的回响——东西小说《回响》和电视剧《回响》读札

文学作品文本与影视艺术剧本的改编由来已久。可以说,中国现当代文学作品,始终为影视创作提供着不竭的文学、文化支撑。影视短剧《回响》以其超高的文本还原度,深度地反映当下的现实情境和时代症候,在想象与现实的缝隙中建构起能够触摸灵魂、心灵的场域,激发起警示人性的回响。本文通过对东西的长篇小说《回响》的影视化改编进行分析,从改编策略、主题哲思与心理关照等角度切入,探究在文学与影视交融中,在镜像呈现与文学想象之间,对于现实、人生和时代的多重反思和建构。

一种雷达测试中通道误差自适应补偿技术

文章针对多模式、多带宽脉宽组合的雷达系统调试测试时通道误差提取与补偿效率低的问题,提出了一种通道误差随参数变化的自适应补偿技术。该方法可在通道误差模型的基础上,根据各工作模式所设定的带宽、脉宽组合自适应生成幅相误差补偿曲线,并以回波模拟器通道误差自适应补偿为例给出了误差模型获取与自动生成的具体步骤。通过某雷达系统测试过程中的试验验证,所提出的方法幅度误差提取结果相比逐次提取结果偏差小于0.02dB,相位误差提取偏差小于0.2度,满足通道误差补偿的需求,不仅适用雷达系统调试测试,还可应用于其他通道特性稳定系统的通道误差补偿,避免繁杂的手动操作,提高测试效率。