A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Audio Signal Generator System Based On State Machines

A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.

Study of ultra-wideband radar signals-generated technology using two-channel signal generator

Synthesis of ultra-wideband （UWB） linear frequency modulation radar signals is a very important technology for microwave imaging, target identification and detection of low radar-cross-section （RCS） targets. A new method of UWB radar signals generation with two-channel signal generator is presented. The realization structure is given; the principle and errors of signal synthesis are analyzed. At the same time, an automatic .adjustment measure of signal phase is proposed because of phase discontinuity of waveform in this method. The simulation experiment and analysis results indicate that radar signals with large instantaneous bandwidth can be generated by means of this method on the condition that the high-speed digital devices are limited.

Frequency Stabilized GaP Continuous-Wave Terahertz Signal Generator for High-Resolution Spectroscopy

We constructed a GaP continuous-wave terahertz (CW-THz) signal generator based on different frequency generation that can tune automatically from 0.15 to 6.2 THz without mode-hopping. Independent frequency feedback control for the seed laser and power feedback control for the optical power amplifier realized higher accuracy and stability of the output THz-wave at the same time. After constructing THz spectrometer with the GaP CW-THz signal generator as a light source, we have confirmed frequency resolution to be 15 MHz by the measurement of the Doppler width of water vapor absorptions in vacuumed chamber, and frequency reproducibility to be within 5 MHz by comparing with absorption frequency data.

Development of a Double-Channel Medical Signal Generator Based on STM32 with High Performance

An open-ended and multifunctional double-channel signal generator, which based on a 32 bits monolithic integrated microcomputer, highly integrated device and LCD, is introduced in this paper. The instrument is composed of micro-computer STM32F103RD and some integrated chips (IC), which includes programmable waveform generators-AD9833 with highly frequency and phase precision. As a result, this signal generator may output not only double channels accurate sine, square or triangle waveforms with digital-controlled frequency and phase at the same time, but also many kinds of physiological signals that can be modified by USB connection with well open property. Therefore, it is convenient to measure and teach about hearing, research and study on frequency characteristic of human ear and impedance characteristic of human body in medical science. In addition, it is also very easy in experiment and research of college and medical physics for using double channels sine signal to show synthesis of two simple harmonic vibrations under different frequency, phase difference and direction, such as beat pattern and Lissajous figures. Thus it has many merits, such as the small volume, stable property, simple operation, visual display and so on. Consequently, it can be widely used in researching, teaching, debugging and maintaining.

基于System Generator的GPS信号捕获模块的实现

System Generator for DSP是业内领先的高级系统级FPGA开发工具.本文基于该软件设计并实现了4通道GPS信号捕获模块,其中包括平均采样、本地C/A码、FFT/IFFT运算、复数乘法运算、扫频控制和结果返回等6个模块,并利用Xilinx的Viertx-5XC5VSX50T-FF665芯片对模块进行了硬件协同验证.测试结果表明:设计实现的捕获模块能够准确地捕获GPS信号,与利用硬件描述语言的方法相比,本文的方法大大缩短了开发周期.

基于System Generator的任意信号发生器的设计与实现

本文利用直接数字频率合成（DDS）技术，采用Xilinx公司推出的System Generator与MATLAB Simulink系统工具，介绍一种自行设计的基于FPGA的DDS电路，实现了任意信号发生器的信号发生器的功能，具有失真度小，稳定度高的优点。

Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic samples.In this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian domains.However,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into over-fitting.Fortunately,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled data.We present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal data.According to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal.We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal dataset.Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

REALIZATION OF FUZZY-FILTER CONTROL ON A BRUSHLESS AC GENERATOR

The transistor voltage regulators have been widely adopted in the brushless AC generators in aircraft. This paper researches the digital voltage regulator. The paper presents the hardware platform of the digital voltage regulator, which is based on a DSP chip — TMS320C32. A novel fuzzy filter control structure is developed from normal fuzzy control strategy. And the fuzzy filter control algorithm is adopted in the hardware platform successfully. The computer simulation has been conducted. Some control parameters have been obtained through the simulation. The same parameters have been applied in the digital regulation experiments on a brushless AC generator. In the experiment, the digital voltage regulator results in good responses. From the experiment results, it can be seen that the new control algorithm is efficient for the digital voltage regulator.

基于System Generator的雷达干扰信号模拟

针对雷达干扰信号模拟中算法设计快速验证的应用需求,为降低系统可重构开发中的复杂程度,研究了一种基于System Generator模型的FPGA设计开发方法,并以噪声调频和噪声卷积算法实现。对应用实例进行了设计演示验证,为雷达干扰信号算法验证实验提供技术参考。

GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.

A HIGH PERFORMANCE UWB LFM WAVEFORM GENERATOR

In this paper, the design and implementation of a high performance Ultra-WideBand (UWB) Linear Frequency Modulation (LFM) waveform generator at Very High Frequency/Ultra High Frequency (VHF/UHF) band are introduced. Firstly, the design ideas for a high performance UWB LFM waveform generator are described. Then, a generation scheme for UWB LFM waveforms is presented according to the baseband digital generation method combining with the bandwidth ex-tension method via frequency doubling. An experimental system has been implemented and tested. The results show that the UWB LFM waveform generator achieves very high performance.

Numerical Studies of the Generalized <i>l</i>₁Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.

Safety Performance Functions for Traffic Signals:Phasing and Geometry

A significant proportion of urban crashes,especially serious and fatal crashes,occur at traffic signals.Many of the black-spots in both Australia and New Zealand cities occur at high volume and/or high-speed traffic signals.Given this,crash reduction studies often focus on the major signalised intersections.However,there is limited information that links the phasing configuration,degree of saturation and overall cycle time to crashes.While a number of analysis tools are available for assessing the efficiency of intersections,there are very few tools that can assist engineers in assessing the safety effects of intersection upgrades and new intersections.Safety performance functions have been developed to help quantify the safety impact of various traffic signal phasing configurations and level of intersection congestion at low and high-speed traffic signals in New Zealand and Australia.Data from 238 signalised intersection sites in Auckland,Wellington,Christchurch,Hamilton,Dunedin and Melbourne was used to develop crash prediction models for key crash-causing movements at traffic signals.Different variables(road features)effect each crash type.The models indicate that the safety of intersections can be improved by longer cycle times and longer lost inter-green times,especially all-red time,using fully protected right turns and by extending the length of right turn bays.The exception is at intersections with lots of pedestrians where shorter cycle times are preferred as pedestrian crashes increase with longer wait times.A number of factors have a negative impact on safety including,free left turns,more approach lanes,intersection arms operating near or over capacity in peak periods and higher speed limits.

基于自适应虚拟电阻的低压微电网有功均分下垂控制策略

由于低压微电网中各分布式电源的线路阻抗不匹配,传统的下垂控制策略难以按照下垂系数合理分配有功功率。为此,提出一种无需通信的自适应虚拟电阻下垂控制方法。通过将微电网中每个分布式发电机(DG)单元输出的有功功率和电压传送给自适应虚拟电阻控制器中,在保证电压和频率稳定控制的前提下实现功率按逆变器容量比例进行精确分配。论文对改进方法的微电网逆变器进行小信号稳定性分析,以优化控制器有关控制参数。仿真和实验结果验证了所提控制方法的有效性。

基于传递函数分析的双馈风力发电系统动态建模方法研究

以双馈风力发电系统(doubly-fed induction generator based wind energy conversion system,DFIG-based WECS)为例,利用小信号分析法推导出由风机、双质量块传动链构成的动力与传动系统通用传递函数方程。动力与传动系统传递函数的零极点位置、稳定性与系统参数及系统运行工作点相关。该传递函数可进一步分解为扭转分量和非扭转分量。传动链参数仅对扭转分量造成影响。在此基础上,建立了考虑动力传动、电机、变流器、控制等环节的风力发电系统传递函数模型。模型综合了各环节参数,可直观反映系统参数对系统响应的影响,有助于深入了解系统动态行为。算例及时域仿真结果证明了所提出传递函数模型的准确性和高效性,可为系统参数设计研究提供理论依据。

基于超前滞后环节附加前馈阻尼的VSG控制策略

虚拟同步发电机(virtual synchronous generator,VSG)在引入同步机二阶转子运动方程,增大电力系统等效惯量的同时,也引入了同步发电机的振荡特性,有功低频振荡等动态稳定性问题也随之而来。引入调速侧电力系统稳定器(governor power system stabilizer,GPSS)能有效抑制VSG的功率低频振荡,但其在超调量及调节时间方面的控制效果仍有待提高。通过建立VSG的小信号模型从极点配置角度分析其稳定性,揭示基于GPSS的VSG控制策略在功率动态响应上存在较高超调和较长调节时间的原因。基于此,参考GPSS控制思想,提出了一种基于超前滞后环节附加前馈阻尼补偿的虚拟同步发电机控制策略。并从理论上分析验证了所提控制策略在不影响系统稳态特性的前提下,能够提供调整自由度更高的正阻尼,在有效地抑制功率超调的同时提高了系统的调节速度,从而更好地抑制了有功功率的低频振荡。最后通过MATLAB/Simulink进行对比仿真,仿真结果与理论分析结果一致,证明了所提控制策略的正确性和有效性。

面向城区的基于图去噪的小区级RSRP估计方法

移动通信系统网络的规划、部署和优化都不同程度依赖于参考信号接收功率(RSRP)估计的准确性。传统上,基站覆盖小区内某信号接收点的RSRP可由对应的无线传播模型估计。在城市环境中,不同小区的无线传播模型需要使用大量RSRP实测数据校正。由于不同小区环境存在差异,经过校正后的模型只适用于对应小区,且小区内的RSRP估计精度低。针对上述问题,将RSRP估计问题转化为图去噪问题,并通过图像处理与深度学习技术得到小区级无线传播模型,不仅能实现小区整体的RSRP估计,且能适用于相似环境小区。首先,通过随机森林回归器逐点预测每个接收点的RSRP,得到整个小区的RSRP估计图;然后,将RSRP估计图和实测RSRP分布图之间的损失视为RSRP噪声图,提出基于条件生成对抗网络(CGAN)的图去噪RSRP估计方法,通过电子环境地图反映小区的环境信息,有效地降低不同小区的RSRP。实验结果表明,在无实测数据的跨小区RSRP预测场景下,所提方法预测RSRP的均方根误差(RMSE)为6.77 dBm,相较于基于卷积神经网络的RSRP估计方法EFsNet下降2.55 dBm;在同小区RSRP预测场景下,相较于EFsNet,模型参数量减小80.3%。

6G新型时延多普勒通信范式:OTFS的技术优势、设计挑战、应用与前景

在未来的通信网络中,被广泛期待的第6代移动通信系统(The Sixth Generation of Mobile Communications System,6G)技术将面临诸多挑战,其中包括在高速移动场景下的超高可靠通信问题。正交时频空间(Orthogonal Time Frequency Space,OTFS)调制技术克服了传统通信系统在高速移动环境下多径和多普勒效应的影响,为实现6G超高可靠通信提供了新的可能性。该文首先介绍了OTFS的基本原理、数学模型、干扰与优势分析。然后,归纳分析了OTFS技术在同步、信道估计、信号检测技术上的研究现状。接着,从车联网、无人机、卫星通信、海洋通信4个典型应用场景分析了OTFS的应用趋势。最后,从降低多维匹配滤波器、相位解调和信道估计、硬件实现的复杂度和提高对时频资源的高度利用4个角度探讨了未来研究OTFS需要克服的困难和挑战。

基于机器学习的OTFS系统信道估计与信号检测研究进展

OTFS调制可以在高速移动场景下有效对抗ICI,能提供比传统的OFDM更显著的性能增益,充分提升高多普勒扩展场景下的通信系统频谱效率,改善通信系统质量,近年来得到广泛研究。OTFS系统的研究重点和难点在于信道估计与信号检测。人工智能已经成为一种有效的信息处理手段,在各行业都得到了广泛的应用。而机器学习是人工智能中的重要分支,通过将机器学习与OTFS技术相结合,可以有效解决信道估计与信号检测中的问题,提高系统性能、稳定性和可靠性。对目前基于机器学习的OTFS系统的信道估计与信号检测算法进行了较为全面的调研、分析、对比和归纳,进而梳理出技术挑战,并就未来的技术发展趋势进行了探讨。