Comparative Analysis of EEG Signals Based on Complexity Measure

The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.

A NEW SYSTEM DYNAMIC EXTREMUM SELF-SEARCHING METHOD BASED ON CORRELATION ANALYSIS

Objective To propose a new dynamic extremum self searching method, which can be used in industrial processes extremum optimum control systems, to overcome the disadvantages of traditional method. Methods This algorithm is based on correlation analysis. A pseudo random binary signal m sequence u(t) is added as probe signal in system input, construct cross correlation function between system input and output, the next step hunting direction is judged by the differential sign. Results Compared with traditional algorithm such as step forward hunting method, the iterative efficient, hunting precision and anti interference ability of the correlation analysis method is obvious over the traditional algorithm. The computer simulation experimental given illustrate these viewpoints. Conclusion The correlation analysis method can settle the optimum state point of device operating process. It has the advantage of easy condition , simple calculate process.

井下钻具耦合振动测量模型及实钻数据分析

为了监测井下钻柱的实际振动情况,调整钻进参数来控制井下钻柱的振动特性,从而优化钻进,提高钻速。针对如何通过测量的加速度信号对不同振动形式进行解译与表征的井下振动测量关键问题,建立钻柱振动的解析动力学模型,研究了某种特定振动模式下传感器输出信号的测量,分析了传感器输出信号与钻柱振动模式的关系。通过对模拟信号中振动模式所对应的加速度输出特征进行研究,分析得出实钻数据中所包含的振动信息。为井下振动测量信号的量化分析与运动模式解译提供依据。

基于手机信令数据的城市居民动态OD矩阵提取方法

现有的城市居民出行调查周期较长,交通小区划分粒度粗糙,导致调查不能及时准确地获取居民出行信息。针对该问题,提出了一种基于手机信令数据的城市居民动态OD矩阵提取方法。首先,针对信令数据中的两种复杂噪声:乒乓切换和漂移数据,提出了基于窗口阈值的检测与等效位置替换方法,以及复杂漂移点的检测和标记处理方法;然后,提出一种改进的ST-DBSCAN聚类方法,引入一种等时化方法将时间信息与空间信息相结合,识别出行过程中的驻留点;最后,基于地理信息系统构建含有道路关键节点的路网,将居民出行OD与路网节点相匹配,有效推导出城市居民动态OD矩阵。实验结果表明:与ST-DBSCAN算法相比,所提改进的ST-DBSCAN算法在聚类效果和识别速度上分别提升了6.10%和5.26%;与统计方法和二阶统计量方法相比,基于改进的ST-DBSCAN算法的动态OD矩阵提取方法在均方误差(MSE)上分别降低了16.98%和21.55%。以北京市为例,运用提出的动态OD矩阵提取方法,能够及时有效地分析城市居民日常与高峰时段的出行特征。

复杂电能信号谐波时频域特征分析及其对电能计量影响

为准确分析复杂电能信号谐波时频域特性对电能计量的影响,提出了一种零值搜索-短时傅里叶变换时频域电压、电流信号幅度和相位分析算法,利用信号波形过零特征,自适应调整短窗傅里叶变换的时域窗宽,解决了电网频率波动导致的相位累积效应对相位分析的影响,算法的幅值分析误差为10-3、相位分析误差小于1°;建立了复杂电能信号时频域数据表征模型和4类时频域全局化特征矩阵提取算法,解决了时频域特性表征与提取的问题;以典型非线性动态负荷中频炉为分析案例,分析给出了其4类时频域全局化特征,并建立了畸变波形动态电能测试信号特征模型,通过辅助实验验证了4个时频域全局化特征对电能表动态超差的影响,提出了对国际和国内标准修改的建议。

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。

基于变采样分辨率相关分析法的铁道客车轮对动平衡测试

铁道客车轮对上的不平衡质量影响列车运行安全及速度的进一步提升,轮对安装前需要精确检测不平衡质量并进行动平衡处理。本文提出自适应小波消噪、MUSIC(Multiple Signal Classification)谱估计、相关分析法相结合的方法,实现铁道客车轮对动平衡振动信号特征的提取。通过Labview软件与MATLAB混合编程实现软件开发,并搭建铁道客车轮对动平衡测试平台进行实验验证,实验验证结果表明,经过降噪和采样分辨率的控制,可以克服现场噪声及频率干扰问题,提取的信号幅值及相位具有较高的精度,可实现轮对上不平衡质量及位置的准确探测。

行星传动系统均载与动载系数试验测量改进设计

试验测量行星传动系统均载与动载系数的精准度是评价系统动态性能的基础。针对现有行星传动系统均载与动载系数测量方法存在灵敏度与精度欠佳、系数测试算法缺失、测试应力点存误等问题,本文提出了一种方便、实用且具有高精度的行星传动系统均载与动载系数测试方法,定义了系统系数的测试算法与概念,明确了精确应力测试位置点及不同位置间差异性,采用应变片电桥传感器串并联设计,实现了拉压应力叠加、温度补偿以及繁琐标定免除等优点,为精准测取行星传动系统均载与动载性能提供了重要的理论基础和技术手段。

Dynamics modeling and electromechanical coupling characteristics analysis of cage induction motors

Induction motors, as typical electromechanical energy conversion devices, have received limited attention in previous studies on electromechanical coupling vibrations, precise modeling, and the use of electromechanical coupling effects for fault diagnosis and condition assessment in motor drive systems. This study proposes a comprehensive model of cage induction motors that integrates the multiple coupled circuit model with a rotor-bearing dynamics model. The model accounts for the linear increase in the magnetomotive force across the slot and incorporates the skidding characteristics of bearings in the rotor-bearing system. By calculating the time-varying mutual inductance parameters based on the air-gap distribution in the vibration environment, the electromechanical coupling vibration of the cage motor is investigated. Furthermore, this study examines the electromechanical coupling vibration characteristics influenced by various factors, including bearing clearances, radial loads, and the vertical excitation frequencies of the stators. The results show that the proposed model improves the excitation inputs for the electrical and mechanical systems of the motor compared with conventional models. Increased bearing clearance and radial load affect the current and torque similarly but have opposite effects on the slip ratio. This study provides a deeper understanding of electromechanical coupling mechanisms and facilitates the use of such phenomena for fault diagnosis and condition assessment in motor-driven systems.

新型电力系统动态电能测量技术及其标准化研究

以新能源为主体的新型电力系统是促进现代电力系统低碳转型发展的重要前提与必然趋势。在新型电力系统中,新能源占比提高,非线性电力电子设备大量应用,使得电力系统的动态复杂性日益凸显。电网波形失真、可再生能源双向测量等新型运行工况对电能动态测量技术提出了更高要求。针对新型电力系统中动态电能测量面临的问题和挑战,首先分析了典型业务场景下动态电能测量技术特征与标准化需求,然后综述了动态电能信号构建、动态电能测量、动态电能测量评价等关键技术现状及其在新型电力系统场景下面临的核心问题,然后总结了动态电能测量关键技术及其标准化发展趋势,提出了新型电力系统场景下动态电能测量标准体系框架。相关研究为新型电力系统建设和动态测量技术发展及标准体系完善提供方向指引。

Multifractal Analysis of Human Heartbeat in Sleep

We study the dynamical properties of heart rate variability （HRV） in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulated by the interaction of two branches of the autonomic nervous system： the parasympathetic and sympathetic nervous systems. By investigating the multifractal properties of light, deep, rapid-eye-movement （REM） sleep and wake stages, we firstly find an increasing multifractal behaviour during REM sleep which may be caused by augmented sympathetic activities relative to non-REM sleep. In addition, the investigation of long-range correlations of HRV in sleep with second order detrended fluctuation analysis presents irregular phenomena. These findings may be helpful to understand the underlying regulating mechanism of heart rate by autonomic nervous system during wake-sleep transitions.

Quantitative modeling of bacterial quorum sensing dynamics in time and space

Quorum sensing (QS) refers to the cell communication through signaling molecules that regulate many important biological functions of bacteria by monitoring their population density. Although a wide spectrum of studies on the QS system mechanisms have been carried out in experiments, mathematical modeling to explore the QS system has become a powerful approach as well. In this paper, we review the research progress of network modeling in bacterial QS to capture the system's underlying mechanisms. There are four types of QS system models for bacteria: the Gram-negative QS system model, the Gram-positive QS system model, the model for both Gram-negative and Gram-positive QS system, and the synthetic QS system model. These QS system models are mostly described by the ordinary differential equations (ODE) or partial differential equations (PDE) to study the changes of signaling molecule dynamics in time and space and the cell population density variations. Besides the deterministic simulations, the stochastic modeling approaches have also been introduced to discuss the noise effects on kinetics in QS systems. Taken together, these current modeling efforts advance our understanding of the QS system by providing systematic and quantitative dynamics description, which can hardly be obtained in experiments.

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.

基于多尺度多重分形法的煤岩破坏电位信号特征研究

煤岩变形破坏诱发的表面电位信号包含损伤演化的关键信息,在煤岩动力灾害监测领域得到广泛研究,但大多是在单一时间维度对电位时序信号的波动特征进行研究,对时序信号的非线性、多尺度特征变化规律缺乏深入研究。针对该问题,搭建了煤岩破坏电位监测系统,同步测试了原煤和辉长岩2种试样的电位时序信号,并通过多尺度多重分形(MMA)法,深入研究了多尺度下的电位信号非线性特征,得到了电位时序信号的奇异性指数、奇异维数、局部赫斯特指数等参数,并采用L2范数对赫斯特曲面予以量化。实验结果表明:原煤和辉长岩的总体电位信号都呈现出多尺度多重分形特征,且裂纹萌生前后的电位多重分形图谱呈现一定差异性;相较于辉长岩,煤样在加载前后阶段不同位置处电位信号的奇异性指数差异Δα正负趋势呈现不同特征,表明了煤样具有更强的非线性演化特征;多尺度下局部赫斯特指数的L2范数更好地体现出试样不同通道电位信号间的长程相关性,并能够量化试样电位时序信号的非线性演化特征,进而实现煤岩失稳破坏预测。

农用柴油机齿轮室盖结构动态特性分析与改进

针对某型单缸农用柴油机在标定工况下铸铝齿轮室盖结构振动问题,采用现代信号处理技术结合有限元模态分析技术分析齿轮室盖的结构动态特性。首先采用变分模态分解方法(VMD)与同步压缩小波变换法(SWT)相结合的方法获取了齿轮室盖结构振动的主变分模态分量,分析其时频特性,然后通过频率响应法识别齿轮室盖结构的固有特性,分析两者相关性,在此基础上再利用有限元模态分析技术进一步研究齿轮室盖结构振动的模态特性,找到导致其动态特性变差的薄弱环节,最后采用加强筋结构改进设计措施提高齿轮室盖的模态频率,避开结构共振,优化结构动态特性。

行星轮系早期故障诊断的研究与进展

行星轮系是重要的旋转机械部件,对其早期故障进行诊断是避免重大事故发生的重要手段。行星轮系早期故障诊断已经成为学者研究的焦点。分析了行星轮系早期故障信号的特点,梳理了行星轮系早期故障机理、信号获取方法、故障特征提取与识别的研究现状;归纳总结了早期故障诊断研究存在的主要问题,提出了开展基于动力学模型的早期复合故障机理研究、基于多传感器的早期故障信号获取方法研究、基于多域分析的早期故障特征指标研究、基于数值模型驱动的早期故障诊断研究等方面的建议;为进一步开展行星轮系早期故障诊断的研究提供依据。

基于动态特性和小波包的铣削颤振识别

在金属铣削尤其是低刚度工件加工过程中,颤振是影响工件表面质量、加工效率和刀具寿命等的关键因素。为了避免加工产生的颤振,从信号处理的角度出发,提出了一种基于系统动态特性和小波包的铣削颤振识别方法。通过模态实验获取系统的模态参数,依据颤振频率在系统固有频率附近会出现峰值的特点,采用小波包对原始切削力信号进行分解,然后选取包含丰富颤振信息的频段进行重构,最后对比和分析铣削力信号时频谱图和希尔伯特频谱,实现颤振识别,并对所提出的方法进行了实验验证。结果表明,所提出的方法具有有效性和可靠性。

旋转机械故障分析与监测技术研究

通过传感器和信号调理技术采集旋转机械转动信号,在此基础上用VB对采集信号进行分析,实现旋转机械在线监测、动平衡分析,以及轴承故障分析。实验结果表明,该方法可以有效监测旋转机械设备的异常状态,保证设备安全可靠运行。

基于重心平均动态时间规整算法的有载分接开关机械故障诊断

有载分接开关(on-load tap changer,OLTC)是电力变压器中故障频率较高的部件之一,为了实现有载分接开关的故障诊断,提出了一种基于重心平均动态时间规整算法的故障诊断方法。针对OLTC振动信号的非线性与强时变性,提出自适应变分模态分解算法,计算OLTC振动信号的时频特征矩阵。进一步地,引入重心平均动态时间规整算法,自适应地计算OLTC振动信号时频特征矩阵之间的差异度,从而实现OLTC的故障诊断。研究结果表明,利用重心平均动态时间规整算法能够通过计算多组信号的重心平均序列提高故障诊断的鲁棒性。同时,基于所计算的差异度,不仅能够实现OLTC故障的有效诊断,还能在一定程度上定量表征故障的严重程度。该研究能够为OLTC的故障诊断提供新的思路。

多声道气体超声流量测量动态数据提取与分析

气体超声波流量计研制中,准确定位每个声道正程和逆程收到的回波信号特征点,是精确测量得到该方向传播时间以及通过计算各声道时间差精确得到气体流量值的关键。提出了提取与分析气体超声流量计动态调试数据的方法,获取了回波信号各峰的归一化幅值数据;比较其归一化系数差值,得出最大差值所在的相邻峰为特征峰,两特征峰之间的上升过零点为到达时间阈值点,根据确定的阈值点得到精确的传播时间值。通过Python完成对原始数据的提取和分离并整理到Excel文件中,通过Excel将归一化幅值数据转换为散点图并加以分析,从而快速准确地获取阈值点,进而定位回波信号特征点。通过该方法设定阈值参数的DN50型双声道气体超声流量计,实验证明,所研究的DN50型双声道气体超声流量计能满足气体超声流量计检定规程中对1.0级仪表精度的要求。