Experimental Study on the Identification of Scholte Waves Based on Acoustic Pressure Field Measurement

Scholte waves at the seafloor interface are generally identified by their velocity features and seismic fields,which are measured using ocean bottom seismometers and geophones.These methods are effective in cases where there is a considerable difference between the velocities of Scholte and acoustic waves in water.However,they are ineffective when the velocities of these two types of waves are close to each other.Thus,in this paper,a method based on acoustic pressure field measurement for identifying Scholte waves is proposed according to their excitation and propagation characteristics.The proposed method can overcome the limitations on the velocities of two types of waves.A tank experiment is designed and conducted according to the proposed method,and an ocean environment is scaled down to the laboratory size.Acoustic measurements are obtained along virtual arrays in the water column using a robotic apparatus.Experiments show that changes in Scholte wave amplitudes,depending on different source depths and propagation distances,are consistent with the theoretical results.This means that Scholte waves generated at the seafloor interface are successfully measured and identified in the acoustic pressure field.

High-Accuracy NLOS Identification Based on Random Forest and High-Precision Positioning on 60 GHz Millimeter Wave

60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

Application of PRO-ESPRIT algorithm in identification of surface acoustic wave identification-tags

The surface acoustic wave （SAW） identification （ID）-tags have great potential for application in radio frequency identification （RFID） due to their characteristics of wireless sensing and passive operation. In the measurements based on the frequency domain sampling （FDS）, to expand the range of detection and allow the system work in harsh environments, it is necessary to enhance the identification capability at low SNR. In addition, to identify the tags in real time, it is important to reduce identification time. Therefore, estimation of signal parameters based on the Procrustes rotations via the rotational invariance technique （PRO-ESPRIT） is adopted. Experimental results show that good identification capability is achieved with a relatively faster measurement speed.

A New Principle of Fault Identification of on the Same Tower Based on Traveling Wave Reactive Powers

In order to improve the reliability of fault identification of the double-circuit transmission lines on the same tower, a new algorithm for fast protection of double-circuit transmission lines on the same tower based on the reactive powers of traveling wave is proposed. With the implementation of S-transform, the initial traveling wave reactive powers are calculated and the change characteristics of reactive power under different fault conditions are studied. The protection criterion is constructed by analyzing the ratio of the reactive powers of the same end on double-circuit transmission lines and the ratio of the reactive powers at both ends on the same line. According to the ratio of reactive power on the same side of the line and both ends of the same line, it is possible to identify whether the faults of the double-circuit line of the same tower occurred in or out of the protection zone. A large number of simulation results show that the protection performance is sensitive and reliable, and quick to respond. The criterion is simple and is basically not affected by fault initial angles, fault types, and transitional resistances.

Study on Correlation and Quantiative Error Estimation Method Among the Splitting Shear Wave Identification Methods

Based on the propagation characteristics of shear wave in the anisotropic layers,thecorrelation among several splitting shear-wave identification methods hasbeen studied.Thispaper puts forward the method estimating splitting shear-wave phases and its reliability byusing of the assumption that variance of noise and useful signal data obey normaldistribution.To check the validity of new method,the identification results and errorestimation corresponding to 95% confidence level by analyzing simulation signals have beengiven.

A New Interface Identification Technique Based on Absolute Density Gradient for Violent Flows

An identification technique for sharp interface and penetrated isolated particles is developed for simulating two-dimensional,incompressible and immiscible two-phase flows using meshless particle methods in this paper.This technique is based on the numerically computed density gradient of fluid particles and is suitable for capturing large interface deformation and even topological changes such as merging and breaking up of phases.A number of assumed particle configurations will be examined using the technique,including these with different level of randomness of particle distribution.The tests will show that the new technique can correctly identify almost all the interface and isolated particles,and also show that it is better than other existing popular methods tested.

The pre-stack migration imaging technique for damages identification in concrete structures

Pre-stack migration imaging (PMI) method,which is used in geophysical exploration by the performance of single side detection and visually display,can be used to identify the location,orientation,and severity of damages in concrete structure.In particular,this letter focuses on the experimental study by using a finite number of sensors for further practical applications.A concrete structure with a surface-mounted linear PZT transducers array is illustrated.Three types of damages,horizontal,dipping and V-shaped crack damage,have been studied.A pre-stack reverse time migration technique is used to back-propagate the scattering waves and to image damages in concrete structure.The migration results from the scattering waves of an artificial damage are presented.It is shown that the existence of the damage in concrete structure is correctly revealed through migration process.

Adaptive Reclosing Scheme Based on Traveling Wave Injection For Multi-Terminal dc Grids

The hybrid dc circuit breaker(HCB)has the advantages of fast action speed and low operating loss,which is an idealmethod for fault isolation ofmulti-terminal dc grids.Formulti-terminal dc grids that transmit power through overhead lines,HCBs are required to have reclosing capability due to the high fault probability and the fact that most of the faults are temporary faults.To avoid the secondary fault strike and equipment damage that may be caused by the reclosing of the HCB when the permanent fault occurs,an adaptive reclosing scheme based on traveling wave injection is proposed in this paper.The scheme injects traveling wave signal into the fault dc line through the additionally configured auxiliary discharge branch in the HCB,and then uses the reflection characteristic of the traveling wave signal on the dc line to identify temporary and permanent faults,to be able to realize fast reclosing when the temporary fault occurs and reliably avoid reclosing after the permanent fault occurs.The test results in the simulation model of the four-terminal dc grid show that the proposed adaptive reclosing scheme can quickly and reliably identify temporary and permanent faults,greatly shorten the power outage time of temporary faults.In addition,it has the advantages of easiness to implement,high reliability,robustness to high-resistance fault and no dead zone,etc.

基于多层全连接神经网络的6C地震波极化向量识别研究

利用机器学习原理,提出一种基于多层全连接(multi-layer fully connected, MFC)神经网络的六分量(six-component, 6C)地震波极化向量识别方法。首先利用6C地震波各波型极化向量数学模型和一系列仿真参数得到5种波型和噪声波型各5 000个极化向量数据集,然后随机选取其中5 000个作为测试集,其余划分为训练集,进行MFC神经网络与支持向量机(support vector machine, SVM)的综合辨识性能对比实验。结果表明,MFC神经网络模型识别5种极化向量类型(SH波和Love波视为一类)和6种极化向量类型的效果均显著优于SVM模型,平均识别率分别达到99.786%和87.940%。

基于分形维数的高速铁路地震预警系统地震P波震相识别方法

为更好保障高速铁路的运营安全,高速铁路地震预警系统的实施十分必要。5级以下的小震事件占据地震事件的95%以上,提高高速铁路预警系统在小震震相识别的速度与精度能有效保障高铁安全运营。提出一种基于分形维数的地震震相识别算法,优化传统分形维数的计算方法,提高分形维数的计算速度。引入分形斜率曲线并划分为4个阶段,分析临界点的分形斜率特征,通过识别分形斜率极值时刻判断地震波精准到达时刻。提出的算法平均误差达到0.006 3 s,标准差达到0.043 8 s,满足高速铁路地震预警系统的时效性和准确度要求。

基于静止轴系改进高频方波注入同步磁阻电机无传感器控制

同步磁阻电机因为其成本低、可靠性高,发展前景可观。在低速域无传感器控制方面,因同步磁阻电机转子具有凸极性,使用高频方波信号注入方法进行转子角度估计最为合适。但是,电机在低速运行中伴随着交直轴电感交叉饱和问题,该问题影响着电机的动稳态性能。为此,对传统高频方波注入控制方法进行改进,提出基于静止轴系的双高频方波注入无传感器控制方法,降低高频幅值带来的高频转矩脉动,抑制因交叉饱和效应导致的角度估计误差,提高电机动稳态性能。在1.5 kW同步磁阻电机实验平台上进行实验验证。结果表明,与传统高频注入法相比,改进高频注入法消除了交叉饱合引起的角度估计误差,并降低高频信号带来的转矩脉动问题,提高了电机的动稳态性能。

协方差驱动随机子空间辨识的参数敏感性分析

协方差驱动随机子空间辨识(Covariance-driven Stochastic Subspace Identification,SSI-cov)是近年来发展较为成熟的工作模态分析方法。针对其识别精度和效率对参数设置具有较高敏感性的问题,基于敏感性分析方法,利用奇异熵增量跳跃点明显程度、频率平均识别误差、阻尼比总变异系数、振型平均模态置信因子、运行时间5种评价指标以及稳定图,通过一经典五自由度层模型仿真算例,研究Toeplitz矩阵行块数、采样频率、数据长度对SSI-cov识别结果的影响规律。并给出既能满足精度要求又可控制程序运行时间的参数建议取值范围。最后,通过一缩尺三层框架模型在白噪声激励下实测数据对提出的根据SSI-cov改进参数设置进行验证,结果表明提出的各参数建议取值范围均较为合理。

基于多帧聚类的紧凑型HFSWR虚假点迹识别方法

紧凑型高频地波雷达发射功率低,目标检测时信噪比低、虚警率高,会产生大量虚假点迹,影响后续目标跟踪性能。为了滤除虚假点迹,利用目标的运动特性,提出了一种多帧聚类与极限学习机分类两级级联的虚假点迹识别方法。首先,利用基于最优邻域尺寸的多帧聚类方法,将连续多帧中与待识别点迹属于同一潜在目标的点迹聚类成簇。然后,计算簇内待识别点迹与其相邻帧内点迹的距离-多普勒速度的差分值,以其为特征利用极限学习机辨识虚假点迹。实验结果表明,所提方法能够准确将属于同一目标的点迹聚类,虚假点迹识别率达到95%。

基于高频正交方波注入法的永磁同步电机控制研究

针对传统高频注入法解调过程复杂和观测精度受非理想因素时延影响的不足,提出一种基于高频正交方波注入法的零低速位置估计方法。首先,考虑估计旋转轴系注入的低可靠性,选择将高频信号注入静止轴系,采用简单代数运算提取出高频响应电流,通过解调正向虚拟高频响应电流初步估计转子位置;然后,针对主要非理想因素进行影响分析,在此基础上,通过解调负向虚拟高频响应电流提取出相位滞后角,完成补偿;最后,为避免启动阶段位置收敛错误,通过获取电感变化趋势判断出磁极极性。实验结果表明,所提算法在各类工况均能稳定收敛,且最大平均误差不超过1°,说明了算法的抗扰性以及准确性。

基于路面等级识别的车辆半主动悬架内外环控制

针对车辆在不同路面等级下对悬架动态性能与馈能特性需求不同的问题,提出一种基于RF-XGBoost路面等级识别算法的半主动悬架内外环控制策略。利用随机森林(Random Forest,RF)模型对极端梯度提升(Extreme Gradient Boosting,XGBoost)算法进行优化,搭建RF-XGBoost算法模型对路面等级进行识别。将路面等级与悬架控制策略相结合,设计外环为天地棚控制,内环为自适应滑模控制的内外环控制,实现非线性悬架的自适应控制。仿真结果表明,相比传统混合天地棚控制的悬架,内外环控制下的悬架在A级路面下簧载质量加速度降低15.52%,并实现50.4 W的振动能量回收,在B、C级路面下簧载质量加速度分别降低15.09%、16.72%,轮胎动载荷分别降低11.63%、11.42%,在D级路面下轮胎动载荷降低14.12%。台架试验的结果与仿真分析的结果基本一致,表明所设计的自适应内外环控制有效。

基于Kalman滤波与应变信号的舰船轴系推力辨识研究

在线、实时、准确监测舰船螺旋桨推力对船-机-桨匹配设计、舰船快速性预报及推进轴系健康管理等具有重要意义。然而,受轴系振动及环境干扰等测量噪声影响,螺旋桨推力产生的微弱应变信号易被测量噪声淹没,导致难以准确测量推力。当前,一些常用的信号降噪方法,比如傅里叶变换、小波分析等均是基于纯数据降噪,未考虑测量数据中潜藏的力学机制。不同于这类降噪方法,Kalman滤波可同时考虑测量数据噪声及数据中的力学机制,对目标实现最小方差无偏估计,因而有更高的估计精度。因此,本文利用Kalman滤波结合应变测量信号提出一种螺旋桨推力高精度、在线辨识方法。以恒定转速、变转速及低频波动转速3种工况为例,研究了不同信噪比下本文方法的推力辨识精度与鲁棒性。研究表明,在信噪比仅为20 d B时,推力辨识最大相对误差仅为4.85%,因此本文方法在低信噪比下仍有很高的辨识精度与鲁棒性。同时,本文提出方法属于时域辨识方法,在转速突变、螺旋桨缠绕渔网等突发工况时亦能实时跟踪推力变化,因此可用于螺旋桨推力及轴系状态的在线、实时监测。

基于响应复合能量因子的波浪激励下高桩码头桩基损伤识别

波浪是高桩码头桩基长期、重要外部激励,探索波浪激励下桩基损伤识别对构建码头健康监测体系具有重要意义。由于波浪激励下桩基动力响应呈现多类型信号混叠、窄带、非平稳的特性,使得现有损伤识别方法难以实现准确判定。为解决上述问题,首先构建基于自适应噪声的完备集成经验模态分解与K-means++的信号自动重构方法,以提取损伤特征子信号;然后,通过融合能量、相位等信息,构建新型复合能量损伤识别指标,以提高微小波浪激励下损伤识别的敏感性和鲁棒性。进一步,在高桩码头桩基多种损伤工况下开展波浪激励试验,验证新方法在损伤识别方面的有效性。结果表明,新构建的复合能量因子兼顾能量、相位的优点,能准确识别出损伤存在、损伤位置和损伤程度。

基于电压行波波形特征的柔性直流输电线路雷击识别方法

柔性直流输电系统的线路主保护速动性要求极高,线路受雷击干扰后引入大量高频暂态信号易引发线路保护误动作。针对此问题,首先根据行波的传播规律分析雷击后线路电压行波特征,得出雷击干扰与故障的特征差异。雷击干扰时,暂态过程中电压行波经线路边界反射后波头极性不发生改变,行波波头到达线路边界的时间间隔大。雷击故障时,电压行波经故障点反射后波头极性发生改变,行波波头到达线路边界的时间间隔小。然后,根据电压行波波形特征差异提出长短窗结合的雷击干扰识别方法。最后,利用PSCAD/EMTDC电磁暂态仿真软件对所提方法进行验证。结果表明,所提出的雷击干扰识别方案能够快速、可靠、准确地识别雷击干扰与故障情况,不受雷击位置、线路参数、雷电流参数和噪声的影响。

基于双特征的短波红外星图识别算法

在短波红外波段进行昼夜测星有望实现近地空间全天时自动导航,其中,星图识别算法是实现全天时导航的关键技术之一。传统的三角形算法在导航星数增多的情况下,由于其匹配特征维度低,在识别时容易出现冗余匹配、误匹配的情况。针对这一问题,提出了一种基于双特征的短波红外星图识别算法,该算法选取三角形的面积与外切圆半径双高维特征作为匹配特征,通过构建面积特征的K矢量索引,应用K矢量查找法降低匹配识别的计算复杂度。此外,还提出一种优化的观测三角形的选择策略,减少了匹配过程中的计算量,提高算法的识别速度。实验表明,星点位置噪声低于2像素时,算法的平均识别率优于95%;伪星数未超过50%时,平均识别率可达87.6%;并通过实际观星试验验证了所提算法的可行性,与改进的三角形算法相比,该算法在识别速度、识别率以及抗噪声能力等方面都有明显优势。