A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

In this paper, the ensemble empirical mode decomposition （EEMD） is applied to analyse accelerometer signals collected during normal human walking. First, the self-adaptive feature of EEMD is utilised to decompose the ac- celerometer signals, thus sifting out several intrinsic mode functions （IMFs） at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition （EMD）, the EEMD-based method has the following advantages： it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.

Position Control of a Series Elastic Actuator Based on Global Sliding Mode Controller Design

A series elastic actuator(SEA) is a powerful device in the area of human-machine integration, but it still suffers from difficult position control issues. Therefore, in this paper,an efficient approach is proposed to solve this problem. The approach design is divided into two steps: feedback linearization(FL) and global sliding mode(GSM) controller design. The bounded analysis is presented and global asymptotic convergence is analytically proven. Simulation and experiment results illustrate the effectiveness of the proposed scheme.

Derivation of Reliability Index Vector Formula for Series System and Its Application

In this study, a reliability index vector formula is proposed for series system with two failure modes in term of the concept of reliability index vector and equivalent failure modes. Firstly, the reliability index vector is introduced to determine the correlation coefficient between two failure modes, and then, the reliability index vector of a series system can be obtained. Several numerical cases and an analysis on offshore platform are performed, and the results show that this scheme provided here has better computational accuracy, and its calculation process is simpler for the series systems reliability calculations compared with the other methods. Also this scheme is more convenient for the engineering applications.

Patterns of cross-correlation in time series:A case study of gait trails

A complex system contains generally many elements that are networked by their couplings.The time series of output records of the system's dynamical process is subsequently a cooperative result of the couplings.Discovering the coupling structure stored in the time series is an essential task in time series analysis.However,in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average.We propose a concept called mode network to preserve the structural information.Firstly,a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution.The mode functions are employed to represent the contributions from different elements of the system.Each mode function is regarded as a mono-variate time series.All the mode functions form a multivariate time series.Secondly,the co-occurrences between all the mode functions are then used to construct a threshold network(mode network)to display the coupling structure.This method is illustrated by investigating gait time series.It is found that a walk trial can be separated into three stages.In the beginning stage,the residue component dominates the series,which is replaced by the mode function numbered M14 with peaks covering^680 strides(~12 min)in the second stage.In the final stage more and more mode functions join into the backbone.The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M11,M12,M13,and M14,with peaks covering 200-700 strides.Hence,the mode network can display the rich and dynamical patterns of the coupling structure.This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.

Application of Power Series on Vibration Analyses

Power series is extensively used for engineering studies. To raise the synthesis efficiency and computation accuracy of vibration mode synthesis (MS), the choosing of power series as the component mode-function is studied in this paper, and emphasis is laid on its effect upon the system computation accuracy when using the mode synthesis method for a high speed compound rotating elastic system.

ZJC 480 Series Helium-Neon Laser Blood Vessel Internal Exposure Apparatus

The ZJC 480 series helium-neon laser blood vessel internal exposure apparatus was developed by the Changchun Zhongji Photoelectric Co, Ltd. It is used in the treatment of ischemic anoxic cardiovascular and cerebrovascular diseases, nervous system disorders, and respiratory and urinary system disorders. In 1995, the ZJC series was appraised as the "95 State-Grade Key New Product" by the State Science and Tech-

Constructivism-based Course Books＇ Compiling and a New Mode of Teaching

The paper explores the positive impact of constructivism-based course books compiling and the new mode of teaching, analyzes their effects on helping students improve their integrated English language competence, and pointing out that the development of teaching non-English major graduate students has raised new demands for teaching devices, teaching methodology and new roles of teachers and learners. Therefore, New Century Graduate English Series, as constructivism-based course books, has made some beneficial attempts for the implementation of the new graduate English teaching reauirements.

Mode analysis of structures using the Fourier p-element method

The Fourier p-element method is an improvement to the finite element method,and is particularly suitable for vibration analysis due to the well-behaved Fourier series.In this paper,an iteration procedure is presented for solving the resulting nonlinear eigenvalue problem.Three types of Fourier version shape functions are constructed for analyzing the circular shaft torsional vibration,the plate in-plane vibration and annular plate flexural vibration modes,respectively. The numerical results show that this method can achieve higher accuracy and converge much faster than the FEM based on polynomial interpolation,especially for higher mode analysis.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

ANALYSIS OF COUPLED-MODE FLUTTER OF PIPES CONVEYING FLUID ON THE ELASTIC FOUNDATION

The governing equation of solid-liquid couple vibration of pipe conveying fluid on the elastic foundation was derived. The critical velocity and complex frequency of pipe conveying fluid on Winkler elastic foundation and two-parameter foundation were calculated by po,ver series method. Compared,with pipe without considering elastic foundation, the numerical results show that elastic foundation can increase the critical flow velocity of static instability and dynamic instability of pipe. And the increase of foundation parameters may increase the critical flow velocity of static instability and dynamic instability of pipe, thereby delays the occurrence of divergence and flutter instability of pipe. For higher mass ratio beta, in the combination of certain foundation parameters, pipe behaves the phenomenon of restabilization and redivergence after the occurrence of static instability, and then coupled-mode flutter takes place.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

基于改进EMD和ARMA的MEMS陀螺仪随机误差补偿方法

微机电系统(Micro-Electro-Mechanical System,MEMS)陀螺仪的随机误差限制了其测量精度。为了降低MEMS陀螺仪的随机误差,提出一种基于改进的经验模态分解(Empirical Mode Decomposition,EMD)和优化的自回归滑动平均(Autoregressive Moving Average,ARMA)模型的方法。该方法在传统EMD的基础上,结合Hausdorff距离和累积标准化模态均值以提取信号中的噪声和趋势项,对剩余信号进行ARMA建模和滤波。采用沙猫群优化算法优化建模的定阶过程,采用改进的自适应滤波补偿随机误差。试验结果表明:相较于传统EMD和传统ARMA方法,新方法在静态试验中得到的均方根误差分别降低52.5%和34.4%,在动态试验中得到的均方根误差分别降低50%和32.35%;新方法有效抑制了随机误差,提升了MEMS陀螺仪的使用精度。

vbICA方法用于GNSS坐标序列共模误差提取研究

全球导航卫星系统(global navigation satellite system,GNSS)坐标序列精度主要受共模误差(common mode error,CME)影响。为提高GNSS坐标序列精度,采用变分贝叶斯独立分量分析方法(variational Bayesian independent component analysis,vbICA)提取实验区20个GNSS测站坐标序列的CME,并对比分析vbICA与主成分分析(principal component analysis,PCA)和独立分量分析(independent component analysis,ICA)的滤波性能。结果表明,vbICA滤波效果优于PCA和ICA;经vbICA滤波后,E、N、U方向坐标残差序列均方根(RMS)平均降低36.57%、31.63%、10.97%,距离相关系数平均降低60.53%、56.84%、25.80%;扣除CME后,GNSS速度场估计更加可靠和精确,可有效提高GNSS坐标序列精度,为地球动力学研究提供可靠的数据支撑。

基于VMD-TCN-GRU模型的水质预测研究

为充分挖掘水质数据在短时震荡中的变化特征,提升预测模型的精度,提出一种基于VMD(变分模态分解)、TCN(卷积时间神经网络)及GRU(门控循环单元)组成的混合水质预测模型,采用VMD-TCN-GRU模型对汾河水库出水口高锰酸盐指数进行预测,并与此类研究中常见的SVR(支持向量回归)、LSTM(长短期记忆神经网络)、TCN和CNN-LSTM(卷积神经网络-长短期记忆神经网络)这4种模型预测结果对比表明:VMD-TCN-GRU模型能更好挖掘水质数据在短时震荡过程中的特征信息,提升水质预测精度;VMD-TCN-GRU模型的MAE(平均绝对误差)、RMSE(均方根误差)下降,R^(2)(确定系数)提高,其MAE、RMSE、R^(2)分别为0.0553、0.0717、0.9351;其预测性能优越,预测精度更高且拥有更强的泛化能力,可以应用于汾河水质预测。

GPS时间序列共模误差提取方法及对比分析

区域GPS网中,各个测站坐标时间序列会存在共模误差(CME),该误差会对GPS测站坐标精度产生一定影响,因此去除共模误差对提高站点观测精度具有重要意义。本文在叠加滤波方法的基础上,以各个GPS站点相关性大小为权重进行加权,利用相关加权滤波方法对辽宁及周边省份GPS观测数据进行处理分析,提取共模误差向量,并与叠加滤波的结果进行对比和分析。结果表明,两者均能在一定程度上提取共模误差向量,相关加权叠加滤波法比叠加滤波法具有更好的适用性,均方根提升更为明显,能够更好地提取共模误差。

基于动态反馈的卓越工程师导论系列讲座新模式探索

为全面提高高校工程教育人才培养质量,并提供重要的示范和引导,教育部于2010年提出了“卓越工程师教育培养计划”,先后近200所高校入围卓越计划高校名单。在众多入围高校中,卓越工程师系列讲座是准毕业生培养计划中的主要内容。然而,学生预期讲座内容与讲座专家实际内容输出之间的差异严重影响了各类型工程技术人才的培养效果。创新性地提出了卓越工程师导论系列讲座新模式,基于问卷调查的方式实现闭环动态反馈,进而对系列讲座内容进行动态调整,实现学生预期与讲座专家输出内容的高度一致,提高学生与讲座专家之间的互动积极性,提高学生对相关领域的认知水平,为培养工程技术人才奠定坚实的基础。

基于参数估计-滑模阻抗控制的SEA性能分析

重载四足机器人的足部与地面接触过程和步态转换过程中会受到不确定的冲击载荷作用,易导致足部机构载荷过大从而造成结构的冲击损坏。因此,针对使用液压串联弹性执行器(series elastic actuators,SEA)作为足部末端在非结构环境下动态性能差的问题,提出了基于环境参数估计的滑模阻抗控制方法(environmental parameter estimation sliding mode,EPESM)。以阀控液压缸的活塞位移传递函数为基础建立了基于位置内环的SEA阻抗控制模型,并以PID作为基础控制器;为改善SEA阻抗控制的动态性能,根据Lyapunov第二法构建稳定的自适应环境参数估计方法对SEA期望位置进行前馈补偿;为提升自适应环境参数估计方法在SEA工作过程中不同阶段的动态性能和环境变化适应性,使用模糊控制方法对自适应环境参数估计方法中的自适应参数进行寻优;以SEA状态方程为基础构建滑模控制器与PID控制器进行动态性能对比分析。仿真结果表明:在变SEA弹簧刚度工况和变环境刚度下,EPESM阻抗控制的响应速度明显更快,可将调节时间从平均5 s缩短到1 s内,能更快地达到预期位移和预期接触力,且能略微降低稳态误差,使接触力误差保持在±6 N内。在动态跟踪工况下,EPESM阻抗控制的动态性能更好,在快速进入跟踪状态后,可以长时间保持0.2 s以内的相位滞后和5.2%的幅值误差。