Low-Power Operational Amplifier for Real-Time Signal Processing System of Micro Air Vehicle

A low-power complementary metal oxide semiconductor（CMOS） operational amplifier （op-amp） for real-time signal processing of micro air vehicle （MAV） is designed in this paper.Traditional folded cascode architecture with positive channel metal oxide semiconductor（PMOS） differential input transistors and sub-threshold technology are applied under the low supply voltage.Simulation results show that this amplifier has significantly low power,while maintaining almost the same gain,bandwidth and other key performances.The power required is only 0.12 mW,which is applicable to low-power and low-voltage real-time signal acquisition and processing system.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Estimation of a Type of Form-Invariant Combined Signals under Autoregressive Operators

We focus on a type of combined signals whose forms remain invariant under the autoregressive operators. To extract the true signal from the autoregressive noise, we develop a strategy to separate parameters and use a two-step least squares approach to estimate the autoregressive parameters directly and then further give the estimate of the signal parameters. This method overcomes the difficulty that the autoregressive noise remains unknown in other methods. It can effectively separate the noise and extract the true signal. The algorithm is linear. The solution of the problem is computationally cheap and practical with high accuracy.

Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.

Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal

In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesian product graph of the time-and vertex-graphs.By assuming the signals follow a Gaussian prior distribution on the joint graph,a meaningful representation that promotes the smoothness property of the joint graph signal is derived.Furthermore,by decoupling the joint graph,the graph learning framework is formulated as a joint optimization problem which includes signal denoising,timeand vertex-graphs learning together.Specifically,two algorithms are proposed to solve the optimization problem,where the discrete second-order difference operator with reversed sign(DSODO)in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm,and the time-graph,as well as the vertex-graph,is estimated by the other algorithm.Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time-and vertex-graphs from noisy and incomplete data.

IMMUNE RBF NETWORK AND ITS APPLICATION IN THE MODULATION-STYLE RECOGNITION OF RADAR SIGNALS

Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.

Generalized Parseval’s Theorem on Fractional Fourier Transform for Discrete Signals and Filtering of LFM Signals

This paper investigates the generalized Parseval’s theorem of fractional Fourier transform (FRFT) for concentrated data. Also, in the framework of multiple FRFT domains, Parseval’s theorem reduces to an inequality with lower and upper bounds associated with FRFT parameters, named as generalized Parseval’s theorem by us. These results theoretically provide potential valuable applications in filtering, and examples of filtering for LFM signals in FRFT domains are demonstrated to support the derived conclusions.

An Improved Second-Order Multisynchrosqueezing Transform for the Analysis of Nonstationary Signals

Second-order multisynchrosqueezing transform(SMSST),an effective tool for the analysis of nonstationary signals,can significantly improve the time-frequency resolution of a nonstationary signal.Though the noise energy in the signal can also be enhanced in the transform which can largely affect the characteristic frequency component identification for an accurate fault diagnostic.An improved algorithm termed as an improved second-order multisynchrosqueezing transform(ISMSST)is then proposed in this study to alleviate the problem of noise interference in the analysis of nonstationary signals.In the study,the time-frequency(TF)distribution of a nonstationary signal is calculated first using SMSST,and then aδfunction is constructed based on a newly proposed time-frequency operator(TFO)which is then substituted back into SMSST to produce a noisefree time frequency result.The effectiveness of the technique is validated by comparing the TF results obtained using the proposed algorithm and those using other TFA techniques in the analysis of a simulated signal and an experimental data.The result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study.

Integrated system of traditional THz time-domain spectroscopy and asynchronous optical sampling

Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the traditional THz-TDS system and the complex control of the asynchronous optical scanning(ASOPS)system,which requires frequent calibration,we combine traditional THz-TDS and ASOPS systems to form a composite system and propose an all-fiber trigger signal generation method based on the time overlapping interference signal generated by the collinear motion of two laser pulses.Finally,the time-domain and frequency-domain spectra are obtained by using two independent systems in the integrated systems.It is found that the full width at half maximum(FWHM)of the time-domain spectra and the spectral width of the frequency-domain spectra are almost the same,but the sampling speed of the ASOPS system is significantly faster than that of the traditional THz-TDS system,which conduces to the study of the transient characteristics of substances.

Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

The anomaly detection of the brake operating unit (BOU) in thebrake systems on metro vehicle is critical for the safety and reliability ofthe trains. On the other hand, current periodic inspection and maintenanceare unable to detect anomalies in an early stage. Also, building an accurateand stable system for detecting anomalies is extremely difficult. Therefore,we present an efficient model that use an ensemble of recurrent autoencodersto accurately detect the BOU abnormalities of metro trains. This is the firstproposal to employ an ensemble deep learning technique to detect BOUabnormalities in metro train braking systems. One of the anomalous caseson metro vehicles is the case when the air cylinder (AC) pressures are less thanthe brake cylinder (BC) pressures in certain parts where the brake pressuresincrease before coming to a halt. Hence, in this work, we first extract the dataof BC and AC pressures. Then, the extracted data of BC and AC pressuresare divided into multiple subsequences that are used as an input for bothbi-directional long short-term memory (biLSTM) and bi-directional gatedrecurrent unit (biGRU) autoencoders. The biLSTM and biGRU autoencodersare trained using training dataset that only contains normal subsequences. Fordetecting abnormalities from test dataset which consists of abnormal subsequences, the mean absolute errors (MAEs) between original subsequences andreconstructed subsequences from both biLSTM and biGRU autoencoders arecalculated. As an ensemble step, the total error is calculated by averaging twoMAEs from biLSTM and biGRU autoencoders. The subsequence with totalerror greater than a pre-defined threshold value is considered an abnormality.We carried out the experiments using the BOU dataset on metro vehiclesin South Korea. Experimental results demonstrate that the ensemble modelshows better performance than other autoencoder-based models, which showsthe effectiveness of our ensemble model for detecting BOU anomalies onmetro trains.

Energy Saving for Automatic Train Control in Moving Block Signaling System

With rapid development of the railway traffic, the moving block signaling system （MBS） method has become more and more important for increasing the track capacity by allowing trains to run in a shorter time-headway while maintaining the required safety margins. In this framework, the tracking target point of the following train is moving forward with its leading train. This paper focuses on the energy saving tracking control of two successive trains in MBS. Nonlinear programming method is used to optimize the energy-saving speed trajectory of the following train. The real-time location of the leading train could be integrated into the optimization process. Due to simplicity, it can be used for online implementation. The feasibility and effectiveness are verified through simulation. The results show that the new method is efficient on energy saving even when disturbances present.

A Discussion on the Current MUTCD Standards on Traffic Control Signals for Freeway Entrance Ramps

In Section 4I.02 of the newly published Manual on Uniform Traffic Control Devices(MUTCD),standards were specified for the placement of signal heads for traffic control signals at freeway entrance ramps,or ramp meters.For simultaneous operations,i.e.when ramp control signals are operated such that green signal indications are always displayed simultaneously to all of the lanes on the ramp,a minimum of two signal faces per ramp shall face entering traffic.For staggered operations,i.e.the ramp control signal are operated such that green signal indications are not always displayed simultaneously to all of the lanes on the ramp,one signal face shall be provided over the approximate center of each separately-controlled lane.Based on a nation-wide survey,it was realized that the two standards did not fully reflect the state-of-the-practice with respect to ramp metering operations.Due to the lack of significant conflicting movements at ramp meters,for any on-ramp lane,a minimum of one overhead mounted,or one upper and one lower roadside-mounted signal face is sufficient to control the metering operations.The minimum necessary number of signal faces changes according to the total number of lanes,how the signal faces are mounted(overhead or roadside mounting),and how the meters are operated(simultaneous or staggered).Suggestions are made to revise the two standards to better reflect the current nation-wide practices,and better consider the specific operational needs for ramp meters.

四网融合的信号系统方案研究

城市轨道交通、市域(郊)铁路和城际铁路建设均在向网络化运营推进,各种轨道交通之间的互联互通已经是工程建设必须面临的问题,而信号系统的兼容性则是实现各种轨道交通互联互通的一个基础条件。目前,CBTC系统和CTCS列控系统在各自轨道交通领域均处于统治性的市场地位,两种列控系统对土建条件的需求截然不同,由此导致工程规模、功能定位等各方面的重大差异。重点论述两种列控系统在运营能力、公交化运营等方面的差异,针对目前互联互通所面临的技术难点,提出解决方案,为后续工程设计提供一个可行的思路。

基于ZigBee通信网络的农机作业监测系统设计

首先,介绍了ZigBee通信网络技术,并设计了系统总体设计方案;然后,分别从转速测量模块、振动信号采集模块和割台高度检测设计了系统硬件部分;最后,针对设备节点和协调器两部分设计了ZigBee无线网络传输软件,实现了农机作业监测系统。测试结果表明:系统能够正确采集农机作业参数并通过无线网络发送到云平台,证明系统的可靠性和可行性。

基于建筑信息模型数据驱动的铁路设备运维多模态知识图谱构建

铁路信号设备是保障行车安全、提高运输效率的核心装备,加强信号设备智能运维是降低铁路运行风险的必要基础保障。目前,针对我国基于建筑信息模型(BIM)的智能运维平台存在不能精准映射各设备的行为规律和相互之间互馈作用的机理,须同时依靠经验知识进行推断等问题。首先构建了铁路设备运维文本知识图谱;其次构建卷积神经网络(CNN)-团组图卷积神经网络(cgGCN)模型对BIM图像模态数据进行处理,完成对20种铁路信号设备零件图信息的标注,实验结果表明模型准确率达到95.38%,精确率和召回率的调和平均值F1达到95.58%;最后将BIM图像信息以视觉模态嵌入运维文本知识图谱,利用Neo4j图数据库实现多模态知识图谱可视化展示,从而精准映射各信号设备相互之间互馈作用的机理,为后续现场铁路运维人员实施安全管理和运维决策提供在线服务和指导。

区域轨道交通跨线站配线方案研究

“四网融合”发展背景下,区域轨道交通网络化运营成为重要发展趋势,而跨线站则是实现不同轨道交通线路互联互通的关键节点,因此有必要对跨线站设计进行探究。从优化车站运输组织出发,对跨线站配线方案进行研究,为实际配线布设提供参考。首先,以跨线运营模式为研究对象,根据跨线前后信号制式异同,将跨线分为同信号制式跨线和跨信号制式跨线,分析两种跨线模式列车作业种类及流程,并研究两种模式下行车组织、承载客流形式、应急管理等因素对于车站配线设置的相关需求。之后,结合配线需求,针对同信号制式和跨信号制式跨线站,分别提出了配线布置原则。最后,基于布置原则,在进一步分析两种跨线站列车作业特征基础上,研究并提出适用于不同车站构型、不同客流特征的典型配线设置方法,并进一步考虑运输组织适应性、建筑规模、工程量等因素提出适应不同实际运营场景的建议方案。通过研究,对于同信号制式跨线站,建议以在发车端设单渡线、交叉渡线等配线为主;对于跨信号制式跨线站,结合当前技术条件建议增设到发线,并根据实际需求设置折返线等其他配线。

无线传感节点中频域传输信号解调与控制方法

为提高无线传感网络节点中的频域传输信号抗噪能力,优化前端镜像与噪声信号处理能力,以无线传感节点中的频域传输信号为研究对象,提出一种新的信号控制与解调方法。将无线传感信号输入设计的滤波器中,通过电容器、电阻、运算放大器相互协作完成信号滤波。引入前馈补偿通路组建两级节点信号解调线路,当输出传感信号功率超过标准,便可根据超出值,以前馈补偿的方式调节增益带宽积实现解调。在传感节点中外接一个校正电路,以校正电容调节码的方式,控制传感信号频域传输特性满足正常运行标准。仿真结果表明:在1 V的电源电压下,此滤波器中心频率为2 MHz,邻近信号抑制为55 dB,镜像信号抑制为33 dB,带内纹波为0.1 dB,通带电压增益为18 dB,工作电流为382μA。验证了所提方法具有较高的频域传输信号深度处理能力,且有助于实现无线传感网络高质量通信。

基于主线不停运的城轨支线独立运营改造方案

研究目的:针对大型铁路枢纽接入已运营轨道交通线路引起的既有线改造问题,为满足铁路枢纽与轨道交通连接线(支线)施工期间主线正常运营不受影响,对改造涉及的运营组织与接轨方案进行系统研究,并对土建、轨道、接触网、环网与动照、信号、综合监控与通信系统等的改造及影响进行深入研究,在统筹考虑实施难度、投资与工期的基础上,提出实施风险最小、技术最合理的改造方案。研究结论:(1)既有高架车站与区间改造影响范围较小且相对可控,可以实现主线正常运营情况下新建独立运营支线工程;(2)为最大程度保障主线运营不受影响,在现有技术水平条件下,应尽量避免在运营天窗期进行各类信号光电缆的割接;(3)支线独立运营方案,应尽量采取措施将新建支线与主线相连、轨道相通,以利于车辆检修与维保,并增加运营的灵活性;(4)桥梁浇筑与平移施工临近既有线,实施阶段应进一步完善工艺衔接与交叉施工统筹,增加既有线安全保障;(5)本研究结论可为类似工程提供参考。

短区间特殊信号显示关系及点灯电路设计方案优化研究

针对短区间列车反向追踪运行的特殊需求,深入研究信号显示关系及其配套点灯电路设计方案。首先,分析车站正线与到发线列车行车组织方式和允许速度的差异性,提出基于实际允许速度设计短区间信号显示关系,取消侧线红灯重复显示,以提升到发线的行车组织效率和车站的通过能力;其次,根据信号“故障-安全”原则及点灯电路技术条件,在兼容现有四显示出站信号机点灯电路的基础上,通过增加灯丝监督继电器,优化反向发车信号显示,以适应短区间反向按四显示追踪运行的需求。经仿真测试和实际应用验证,结果表明该方案能够保障行车安全,提高短区间列车运行效率,为铁路枢纽和繁忙干线短区间特殊信号显示关系优化及点灯电路设计提供参考和借鉴。

基于内电势幅值/频率和网络有功/无功电流工作点的变换器并网交流系统小扰动线性化方法

当今电力系统不明机理的振荡问题频发,亟需回归振荡的能量交换本质以认识和分析系统动态过程。系统受扰后设备将根据输入和输出有功/无功功率或电流不平衡调节内电势频率和幅值,进而调节网络中各设备端口有功/无功电流及设备输出功率。在此闭环动态过程中内电势幅值/频率对应的设备储能与有功/无功电流对应的网络储能将在一定水平上彼此交换。因此,系统动态分析的工作点和相应的小扰动数学关系应当基于表征系统储能水平的内电势幅值/频率和有功/无功电流来进行确定和构建。然而,现有研究仍然缺乏对交流系统动态过程能量内涵的认识,目前尚未从能量角度开展认识和分析交流系统动态过程的相关工作。鉴于此,该文旨在提出基于内电势幅值/频率和有功/无功电流工作点的交流电力系统小扰动线性化方法。首先以变换器并网交流系统为例,从系统基本结构及其传输有功/无功的闭环运行原理出发,通过阐述在系统受扰后内电势幅值/频率和有功/无功电流不断迭代更新的闭环动态过程中变换器和网络在一定储能水平上相互交换有功/无功的机制,明确内电势幅值/频率和有功/无功电流的稳态直流量是交流系统的工作点,并澄清其所蕴含的有功/无功存储水平物理内涵。基于内电势幅值/频率和有功/无功电流工作点,该文提出描述变换器/网络的内电势幅值/频率和有功/无功电流间的激励-响应小扰动非线性关系及其线性化的物理认识和相应的线性化方法,然后结合仿真说明系统小扰动线性化关系的适用性,最终阐释小扰动初期交、直流电气量的动态演化行为。