CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

Application of a Current and Voltage Mixed Control Mode for the New Fast Control Power Supply at EAST

A feedback control system is needed to restrain plasma vertical displacement in EAST （Experimental Advanced Superconducting Toknmak）. A fast control power supply excites active feedback coils, which produces a magnetic field to control the plasma＇s displacement. With the development of EAST, new demands on the new fast control power supply have led to an enhanced ability of fast response and output current, as well as a new control mode. The structure of cascaded and paralleled H-bridges can meet the demand of extended capacity, and digital control can reMize current and voltage mixed control mode. The validity of the proposed scheme is confirmed by experiments.

Fast and Slow Responses of the North Pacific Mode Water and Subtropical Countercurrent to Global Warming

Six coupled general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are em-ployed for examining the full evolution of the North Pacific mode water and Subtropical Countercurrent (STCC) under global warming over 400 years following the Representative Concentration Pathways (RCP) 4.5. The mode water and STCC first show a sharp weakening trend when the radiative forcing increases, but then reverse to a slow strengthening trend of smaller magnitude after the radiative forcing is stablized. As the radiative forcing increases during the 21st century, the ocean warming is surface-intensified and decreases with depth, strengthening the upper ocean's stratification and becoming unfavorable for the mode water formation. Moving southward in the subtropical gyre, the shrinking mode water decelerates the STCC to the south. After the radiative forcing is stabilized in the 2070s, the subsequent warming is greater at the subsurface than at the sea surface, destabilizing the upper ocean and becoming favorable for the mode water formation. As a result, the mode water and STCC recover gradually after the radiative forc-ing is stabilized.

Trajectory tracking and obstacle avoidance method for robots based on fast terminal sliding mode

For accurate trajectory tracking and obstacle avoidance in finite time of a nonholonomic mobile robot,a trajectory tracking controller based on global fast terminal sliding mode method is proposed,which has the advantages of chattering-free and adjustable convergence time.First of all,the kinematics model of the robot is established in mobile carrier coordinates.Secondly,the global structure including terminal attractor and exponential convergence of the fast terminal sliding mode trajectory tracking controller is proved by Lyapunov stability theory,ensuring that the trajectory and heading angle tracking error converges to a smaller zero range in finite time.Finally,the artificial potential field obstacle avoidance method is introduced to make the robot not only track the reference trajectory strictly,but also avoid the obstacles.The simulation results show that the proposed method can achieve a stable tracking control in finite time for a given reference trajectory.

Nonsingular Fast Terminal Sliding Mode Control Based on Nonlinear Disturbance Observer for a Quadrotor

Given external disturbances and system uncertainties,a nonsingular fast terminal sliding mode control(NFTSMC)method integrated a nonlinear disturbance observer(NDO)is put forward for quadrotor aircraft.First,a NDO is proposed to estimate the actual values of uncertainties and disturbances.Second,the NFTSM controller based on the reaching law is designed for the attitude subsystem(inner loop),and the control strategy can ensure Euler angles’fast convergence and stability of the attitude subsystem.Moreover,the NFTSMC strategy combined with backstepping is proposed for the position subsystem(outer loop),which can ensure subsystem tracking performance.Finally,comparative simulations show the trajectory tracking performance of the proposed method is superior to that of the traditional sliding mode control(SMC)and the SM integral backstepping control under uncertainties and disturbances.

Fuzzy Fractional-Order Fast Terminal Sliding Mode Control for Some Chaotic Microcomponents

In this paper, we propose a novel fractional-order fast terminal sliding mode control method, based on an integer-order scheme, to stabilize the chaotic motion of two typical microcomponents. We apply the fractional Lyapunov stability theorem to analytically guarantee the asymptotic stability of a system characterized by uncertainties and external disturbances. To reduce chattering, we design a fuzzy logic algorithm to replace the traditional signum function in the switching law. Lastly, we perform numerical simulations with both the fractional-order and integer-order control laws. Results show that the proposed control law is effective in suppressing chaos. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Simulation-Based Optimization for the Fast Fashion Replenishment

Fast fashion is a commercial pattern which provides fashionable clothes at affordable price.This mode needs rapid response supply chain to respond to varying fashion trends.New styles are introduced in every sale period to cover fashion trends.In order to maximize profits,replenishment quantity of each style should be decided in every period.The purchasing and replenishing process over multiple periods based on uncertainty customer demand is modeled,which is formulated by a stochastic choice process.Heterogeneous consumers visit a store in a stochastic sequence and choosing dynamically from the available fashion styles(buy or not buy) according to a utility maximization criterion.The purchase process in a retail shop for multi-period is simulated.An algorithm which combines simulated anneal(SA) with gradient estimation is proposed to find the optimal replenishing strategy from the simulation program.

Fast mode decision for Hierarchical-B pictures in scalable video coding

To decrease the computational complexity of adaptive inter-layer prediction and improve the encoding efficiency in sealable video coding, a mode decision algorithm is proposed by exploiting the part of used candidate modes of the co-located reference macrobloeks for Hierarchical-B pictures. This scheme reduces the amount of the candidate modes to generate a dynamic list for the current encoding macroblock according to the statistical information derived from the co-located reference macroblocks in different temporal levels. The experimental results show that this fast algorithm reduces approximately 31% encoding time on average with the negligible loss of encoding performance.

Edge Feature Based Fast Mode Decision Algorithm for H.264 Intra Prediction

A fast intra mode decision algorithm is proposed in this paper to reduce the complexity of H. 264 encoder. The proposed algorithm adopted the pre-processing method based on edge feature in pictures to filter out some impossible prediction modes. Context information and pre-computed threshold are used to determine whether it is necessary to check the DC mode. This method is able to get rid of most of candidate modes so that only 66--150 modes are left for the final mode decision, instead of 592 modes in the case of full search （FS） method of H. 264. Simulation results demonstrate that the coding time of the proposed algorithm falls down 71.7% compared with FS method, while the performance loss is trivial compared with FS mode decision scheme.

The analysis of marketing strategy of fast fashion brand from the success of＂H＆M＂

In recent years, in fashion industry, especially in clothing market industry, the development of fast fashion brands is very rapid, the accounts in clothing market is more and more, which has become art indispensable part in clothing market field. It can be said the fast fashion brand takes a deep shock for whole clothing market, and it has made some clothing brands take introspection about their traditional and old marketing mode. Fast fashion brands just catch the consumer psychology of current consumers （especially young consumers）, a smart of consumers are attracted to buy for its ＂fair price＂ , ＂ high quality＂ , ＂fashion＂ and other characteristics. In this paper, the success case of the most successful ＂H＆M＂ brand is taken as entry point, then the marketing strategy of fast fashion brands is explored and analyzed to find out the ＂magic weapon＂ of fast fashion brands＇ development, and it is provided some reference and suggestions for marketing strategy improvement of traditional clothing brands.

The Non-Singular Fast Terminal Sliding Mode Control of Interior Permanent Magnet Synchronous Motor Based on Deep Flux Weakening Switching Point Tracking

This paper presents a novel non-singular fast terminal sliding mode control(NFTSMC)based on the deep flux weakening switching point tracking method in order to improve the control performance of permanent interior magnet synchronous motor(IPMSM)drive systems.The mathematical model of flux weakening(FW)control is established,and the deep flux weakening switching point is calculated accurately by analyzing the relationship between the torque curve and voltage decline curve.Next,a second-order NFTSMC is designed for the speed loop controller to ensure that the system converges to the equilibrium state in finite time.Then,an extended sliding mode disturbance observer(ESMDO)is designed to estimate the uncertainty of the system.Finally,compared with both the PI control and sliding mode control(SMC)by simulations and experiments with different working conditions,the method proposed has the merits of accelerating convergence,improving steady-state accuracy,and minimizing the current and torque pulsation.

The Constitution and Working Mechanism of the Fast Fashion Enterprise Financial Competitiveness

With the Pearson Correlation Coefficient measurement method, this paper studies the correlation between the various elements influencing the financial competitiveness of the fast fashion enterprieses and the long-term financial stability, which shows that the financial resilience has the biggest correlation with the long-term financial stability, which is followed in sequence by financial management capacity, financial resources capacity, financial performance capacity, and financial activity capacity. This is due to the fact that such factors as financial solvency, financial operations, profitability, growth capacity and the ability to generate cash exert effect upon the long-term stability of the financial competitiveness.

FAST EVALUATION OF THE CHARACTERISTICS OF SINGLE-MODE OPTICAL FIBERS

This paper describes a method for fast engineering evaluation of the transmission characteristicsof single-mode optical fibers.A versatile microcomputer program is presented which can be utilized to ana-lyze mode field characteristics of single-mode fibers with arbitrary refractive index profiles.Our computationshows that the mathematical algorithm and the corresponding programs developeod in this paper are rela-tively simple and accurate enough for the engineenring design of single-mode fibers for optical communica-tion systems.

混合光谱曲线的Fast ICA盲源解混及影响因子研究

混合物高光谱分析是矿物无损检测的关键技术。文中将混合物350~2500 nm区间光谱反射率变化看作时域变化的一维序列信号,将混合物光谱分离转变为时域信号的盲源分离问题。为了分析混合光谱解混速度和准确度的影响因子,采用Fast ICA数学模型对混合物实测高光谱反射曲线进行了盲源解混试验,从解混过程的白化方式、初始权阵,源光谱高斯性3个方面分析了光谱解混的结果,为混合物光谱检测的后期分析提供了研究基础。选取化学纯氧化铜和氧化亚铜混合物、碱式碳酸铜和氢氧化铜混合物为试验对象,采用解混性能指数PI、光谱均方根误差和光谱角距离作为评价指标,开展了源光谱高斯性与g函数、ZCA和PCA白化方式、及单位权、随机权、指定权3种初始权对解混光谱结果影响的对比实验。实验结果表明:Fast ICA算法在未知混合矿物高光谱先验信息的基础上,能够有效分离出混合物组分源光谱,样本分离精度PI值均小于0.18,光谱盲源解混的效果显著。解混后光谱曲线与源光谱曲线在特征趋势上保持一致,具有相同的吸收位置和特征峰,但是存在一定尺度的差异。另外,源光谱的高斯性与解混g函数的选取会直接影响解混结果,亚高斯区间曲线分离精度优于超高斯部分。依据高斯性进行分段解混结果的吸收特征会更加突出,与源光谱反射率数值差异也随之增大;光谱预处理的白化方式会对解混结果精度产生微小影响,经过ZCA白化后解混结果的分离精度、光谱的准确度均略高于PCA白化;在Fast ICA模型3种初始权解混结果对比发现,以初次迭代结果计算的指定权进行迭代解混时,其分离精度、解混准确度及解混时间均为最佳,解混过程更易收敛。结果表明,根据全波段高斯性来选取g函数解混效果最优,分离指数PI小于0.14,光谱角距离在0.1左右,ZCA白化比PCA白化对解混光谱的影响更小,ZCA白化后的两组混合物解混的分离指数在0.1左右,而PCA白化的分离指数PI均高于0.13,当指定权作初始权时有助于提高牛顿迭代中收敛速度,使解混光谱更接近组分已知光谱,指定权的解混时间低于0.2 s,其他两种定权方式均在0.3 s以上。

基于Fast-ICA算法的改进EEMD算法在桥梁工程中的运用

目前,以振动分析为基础的桥梁结构健康状态评估方法逐渐受到人们的重视,模态参数识别作为振动分析的关键问题之一,还需进一步完善。模态参数识别即是对结构的振动信号进行动力特性参数的识别,以得到结构的频率、振型以及阻尼比。为了精确地识别出桥梁结构的模态参数,需先对传感器采集的结构响应信号进行分解和重构以保留结构的真实信息,再对重构信号进行模态参数辨识。现阶段,集合经验模态分解算法作为常用的信号分解算法之一,存在两大缺陷,即所得本征模态函数间存在模态混叠现象和有效本征模态函数的定义没有统一的标准。基于此提出对应的改进算法,首先将夹角余弦法用于筛选有效IMF分量;其次将盲源分离算法中的Fast-ICA算法嵌入EEMD分解算法中以避免模态混叠现象的发生;接着将所提算法运用于模拟信号以验证其可行性;最后将所提改进算法运用于实际桥梁工程的响应信号中,并对比分析所得模态参数值以验证该算法的可靠性。结果表明,所提算法较现有EEMD算法具有更好的信号分解效率,且能够有效地剔除结构的虚假信息,保留实际桥梁结构的真实信息。

基于FastICA技术与TLS-ESPRIT方法的电力系统低频振荡模态辨识

对于目前电力系统中低频振荡参数辨识中的噪声干扰和精度问题,提出了一种新的提取低频振荡模态参数的方法,将快速独立分量分析技术(fast independent component analysis,Fast ICA)和总体最小二乘-旋转不变技术(total least squares-estimation of signal parameters via rotational invariance technique, TLS-ESPRIT)联合起来。首先运用FastICA技术对含有噪声的电力系统低频振荡广域测量信号进行预处理而达到降噪效果,而后将处理后的信号作为新的输入信号利用TLS-ESPRIT算法进行估计辨识,从而得到各个模态特征参数。通过对理想信号、EPRI-36机系统和电网实测信号仿真验证了所提方法的有效可行性,不但能够有效抑制噪声并准确地辨识低频振荡参数,而且在抗干扰性和提取精度上与传统辨识方法相比来说是有一定优势的。

基于改进FastICA和Prony算法的同步电机参数辨识

电力系统模型及参数的准确性对系统动态仿真至关重要。针对Prony算法在辨识同步电机参数存在对环境噪声敏感及输入数据要求较高的问题,提出一种结合经验模态分解的快速独立分量分析方法以实现对短路电流噪声信号的提取。首先对理想同步电机短路电流做经验模态分解,获取1组固有模态函数;然后通过计算各函数的互相关系数,找出独立性最强的几个分量,与原含噪短路电流及残余项一起作为快速独立分量分析方法的输入,输出1组独立分量,其中1个分量作为噪声估计量,进而得到去噪后的短路电流;最后利用Prony算法对去噪后的短路电流分析,辨识出同步电机瞬态及超瞬态参数。数字仿真结果验证了所提方法的可行性与有效性。

基于EEMD-FastICA的单通道超声回波信号去噪研究

针对单通道超声检测回波信号易受到噪声信号的影响导致缺陷诊断精度低的问题,提出基于集合经验模态分解(EEMD)和快速独立成分分析(Fast ICA)的单通道超声回波信号盲源分离方法(EEMD-Fast ICA)。首先应用EEMD算法对回波信号进行自适应分解,得到多个不同尺度的固有模态函数(IMF)分量,利用主成分分析(PCA)预估源信号的数目,通过相关系数法筛选出相应的IMF分量进行重构,并将重构信号与原始采集信号组合构成新的多维观测信号,解决了原始采集信号盲源分离中存在的欠定问题,然后进行快速独立成分分析(Fast ICA)运算,实现了单通道超声检测信号的噪声分离。实验结果表明:EEMD-Fast ICA方法能对采集超声回波信号进行有效的降噪处理,并且能保护真实信号的频域特征信息。

基于自适应EEMD与Fast Kurtogram的齿轮箱故障诊断

针对集合经验模式分解算法中添加白噪声幅值大小和总体平均次数过分依赖于人的主观经验或多次尝试,具有较大主观性和盲目性的不足,提出一种自适应EEMD结合快速峭度图(Fast Kurtogram)的故障诊断方法。首先将采集到的振动信号进行EMD预处理以自适应的获取EEMD算法的关键输入参数,然后结合峭度与互相关系数“双阀值准则”快速选取分量进行信号重构以突出故障特征,并通过快速峭度图选取最佳滤波参数,最后对滤波后的信号做包络谱分析,实现故障特征频率的提取与故障诊断。通过模拟信号分析及减速器齿轮箱的故障诊断工程应用,并与EMD方法及传统EEMD方法进行对比分析,验证了提出方法的有效性。结果表明,所提出的方法能够从含有强烈背景噪声的信号中成功提取出减速器齿轮箱的早期微弱故障特征,提高了故障诊断的及时性与准确性。

基于CEEMD和FastICA的滚动轴承故障诊断研究

由于滚动轴承振动信号易受噪声干扰的影响、故障特征提取较为困难。为此,提出了一种基于互补集合经验模态分解(CEEMD)和快速独立分量分析(Fast ICA)的轴承故障特征提取方法。该方法首先利用CEEMD算法将原故障振动信号进行分解运算,得到一系列模态分量(IMF);然后依据峭度准则选取一些模态分量来完成观测信号的重构,剩余其他的模态分量完成虚拟噪声通道信号的重构;再利用Fast ICA方法对重构信号进行降噪;引入Teager能量算子(TKEO)对降噪后的信号进行解调处理;最后对解调后的信号进行快速傅里叶变换(FFT)运算,分析变换后信号的频谱特征,提取出原信号的故障特征频率。将该方法应用到滚动轴承故障实际数据中,实验结果表明,该方法可以有效提取出滚动轴承故障的基频和倍频特征信息。