Smartphone-based bridge frequency identification using vehicle contact-point response

Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.

Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy supply.In this paper,an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the battery.More specifically,a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency,medium-high-frequency,and low-frequency characteristics of the key kinetic processes.Then,two coupled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman filtering methods and decoupled by the corresponding time-scale information.The coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion process.In addition,the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process parameters.Finally,the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic parameters.The experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the proposed strategy for voltage tracking can be limited to 13 and 16 mV,respectively.Additionally,from the entropy calculation results,the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72%and 70.05%,respectively,compared with the traditional fixed characteristic frequency algorithms.The proposed method paves the way for the subsequent development of adaptive state estimators and efficient embedded applications.

Radio Frequency Fingerprint-Based Satellite TT&C Ground Station Identification Method

This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.

Application of Radio Frequency Identification (RFID) in Dairy Information Management

As a kind of brand-new technology, radio frequency identification management, data control and acquisition. This paper introduced food safety system construction, analyzed the advantages and problems in dairy modem suggestions for solution according to the practical situation. （RFID） plays an important role in dairy information tracing and culture function extension of managing breeding technology, and finally put forward some

The Identification of Frequency Hopping Signal Using Compressive Sensing

Compressive sensing (CS) creates a new framework of signal reconstruction or approximation from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without ever reconstructing the signals. Moreover, the number of measurements necessary for most compressive signal processing application such as detection, estimation and classification is lower than that necessary for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hopping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective and efficient.

An Overview of Anti-Collision Protocols for Radio Frequency Identification Devices

Radio frequency identification(RFID) is a new type of non-contact automatic identification technology.Due to its low energy consumption,low cost,and its adaptability to harsh environments,it has been applied to many fields.In the RFID systems,data collision is inevitable when the reader sends a communication request and multiple tags respond with simultaneous data transmission.Data collision is prone to causing problems such as:identification delay,spectrum resource waste,a decreased system throughput rate,etc.Therefore,an efficient,stable anti-collision protocol is crucial for RFID systems.This research analysed the current research into RFID anticollision protocols and summarised means for its improvement through the mechanism of implementation of different types anticollision protocols.Finally,a new direction is proposed for the future development of RFID anti-collision protocol systems.

Parameter identification algorithm for fault location using one terminal data based on frequency domain

This paper presents a novel algorithm of fault location for transmission line.Solving the network spectrum equations for different frequencies the fault can be located accurately by this algorithm with one terminal data of voltage and current,and the identified parameters,such as fault distance, fault resistance,and opposite terminal system resistance and inductance.The algorithm eliminates the influence of the opposite system impedance on the fault location accuracy,which causes the main error in traditional fault location methods using one terminal data.A method of calculating spectrum from sampled data is also proposed.EMTP simulations show the validity and higher accuracy of the fault location algorithm compared to the existing ones based on one terminal data.

Remanufacturing Closed-Loop Supply Chain Model with Radio Frequency Identification Affecting Recycling Rate

In order to construct a resource-saving and environmentfriendly society,the advantages of radio frequency identification(RFID) were considered.And it put forward the idea of introducing RFID in the recycling activities of waste products.Taking into account such elements as the technical level of RFID,cost saving from remanufacturing and the cost of RFID tags,both centralized and decentralized supply chain models with different participants in waste product collection were created,in order to determine the optimal pricing strategy and RFID technical level.In the end,sensitivity analyses were conducted to analyze the impacts of scaling parameter for additional increased recovery rate with RFID on pricing and RFID technology level,and impacts of cost saving on the profits of participants in different remanufacturing closed-loop supply chain models.

Gauss linear frequency modulation wavelet transforms and its application to seismic phases identification

Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.

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.

Line of sight waypoint guidance for a container ship based on frequency domain identification of Nomoto model of vessel

CIFER software is used to identify steering and roll dynamics of a container ship. In this software, advanced features such as the Chirp-Z transform(CZT) and composite window optimization are applied to the time history of steering and roll dynamics to extract high quality frequency responses. From the extracted frequency responses, two linear transfer functions of Nomoto model are fitted for yaw and roll dynamics of the vessel. Based on the identified Nomoto model, a PID heading controller and a Kalman filter observer are constructed. The simulation results of heading controller for line of sight(LOS) waypoint guidance show excellent tracking of pilot inputs in the presence of wave induced motions and forces.

STATISTICAL FEATURE OF PITCH FREQUENCY DISTRIBUTIONS FOR OBUST SPEAKER IDENTIFICATION

This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Frequency Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead of simply taking one kind of features into account.Experimental results indicate that the hybrid approach can give outstanding improvement for text-independent speaker identification under noisy environments corrupted by AWGN.

IDENTIFICATION OF NONLINEAR DYNAMIC SYSTEMS:TIME-FREQUENCY FILTERING AND SKELETON CURVES

The nonlinear behavior varying with the instantaneous response was analyzed through the joint time-frequency analysis method for a class of S. D. O. F nonlinear system. A masking operator an definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time-varying linear one, called the generalized skeleton linear system (GSLS). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non-linearity as well. Moreover, an identification method is proposed through the skeleton curves and the time-frequency filtering technique.

基于轴箱高频振动的车轮不圆辨识方法研究

为实现对高速列车车轮高阶不圆的实时检测,研究了轴箱高频振动与车轮不圆的频谱特征和映射关系,采用频域积分方法对车轮不圆的幅值和阶次进行辨识.首先,通过静态测试和台架试验,研究我国高速铁路车轮多边形、钢轨波磨和轨道模态的表现形式;其次,通过高速列车长期服役性能跟踪试验,掌握转向架轴箱振动的时频特征和演化规律;最后,以现场出现车轮20阶多边形的车辆为研究对象,提出基于频域积分的车轮不圆阶次和幅值辨识方法.研究结果表明:CRTS-Ⅱ型轨道板钢轨三阶弯曲频率为592 Hz;列车以300 km/h运行时,20阶车轮多边形和136 mm波长钢轨波磨的响应频率分别为580 Hz和613 Hz;钢轨模态、车轮多边形以及钢轨波磨的振动主频较为集中,轴箱高频振动幅值随车速和镟后里程的增大而增大;采用加速度频域积分方法,从理论上可实现对车轮不圆幅值和阶次的辨识;基于线路实测轴箱加速度的20阶车轮多边形辨识结果与静态测试值相对误差不超过5%.

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。

变频电流激励下GIS隔离开关机械缺陷的振动非线性行为及其辨识方法

气体绝缘组合电器(gas insulated switchgear,GIS)机械缺陷是导致设备故障的关键因素之一,为此开展了变频电流激励模式下隔离开关底座松动缺陷的时频域振动图谱变化规律研究,进行了波形因子、重心频率等6个振动特征量的量化分析,采用灰狼算法优化的支持向量机研究提出了变频电流激励模式下GIS隔离开关底座松动缺陷严重程度辨识方法。结果表明:GIS隔离开关缺陷振动信号特征量与电流激励频率具有强相关性,与单一工频电流激励模式相比,变频电流激励具备更有效检测GIS设备机械缺陷的优势;基于126 kV真型GIS试验平台验证获得构建的隔离开关底座松动缺陷辨识模型的平均识别准确率达90%以上。该研究成果为揭示GIS设备的非线性机械振动特性及提升其机械缺陷检测水平提供了重要参考。

基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别

能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural network,ResNet)、门控循环单元(gated recurrent units,GRU)网络与注意力机制(attention,AT)组合的电能质量复合扰动识别新方法—Res-GRU-AT。首先利用奇异谱分解(singular spectrum decomposition,SSD)和逐次变分模态分解(successive variational mode decomposition,SVMD)对PQDs信号分别进行多尺度分解得到混合分量,再对混合分量进行希尔伯特黄变换(Hilbert-Huang transform,HHT),分析得到多尺度时频图。其次,利用Res-GRU-AT模型对多尺度时频图进行深层次特征提取、强化和识别。Res-GRU-AT模型能够利用ResNet的二维图像空间特征提取能力和GRU的时序特征提取能力进行特征融合,再通过AT进行特征加权强化,提高了PQDs的识别能力。不同方案的仿真结果表明,所提方法特征提取能力强且抗噪性能好,对复合扰动识别率高。

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%。