改进的IPEG算法仿真实现

本文在对改进的IPEG算法分析的基础上,给出了这种算法的一种仿真实现。该算法的优点是增加了信息节点的连通性,从而减少了小停止集。仿真结果表明,与用IPEG算法相比较,利用此种方法构造的LDPC码具有更低的错误平层。     

基于HHT及主成分分析的光缆识别信号特征提取

为了解决当环境噪声较严重时光强难以维持稳定,容易误判目标光缆问题,根据干涉信号非线性、非平稳的时域特征,提出了一种希尔伯特-黄变换及主成分分析相结合的信号处理方法。探测器接收的干涉信号对进行经验模态分解,把分解得到的本征模态分量作为列向量构建主成分分析的样本矩阵,以奇异值的贡献率进行有效成分筛选;然后采用主成分分析算法完成降噪优化。最后将降噪后的信号进行希尔伯特-黄变换得到瞬时特征信号,完成目标光缆识别。结果表明,相同噪声环境下所提方法可有效提高回波干涉信号的信噪比。因此,研究结果可以降低误判目标光缆的概率,帮助工程人员更有效地完成光缆识别。     

基于前向滚动EMD技术的预测模型

运用经验模态分解(EMD)、人工神经网络(ANN)和时间序列,基于分解—重构—集成的思想,构建了一个组合预测模型。在模型的构建过程中,提出了对股票指数序列进行逐日前向滚动EMD分解的思路,将分解后的本征模函数(IMF)分量输入神经网络进行组合预测。运用上述基于前向滚动EMD模型分析沪深300指数和澳大利亚指数的波动特点和走势。结果显示:前向滚动EMD模型比ARIMA模型、GARCH模型和BP神经网络模型具有更高的预测精度。     

电解MnO2机械球磨-掺杂改性及其在超级电容器中的应用

以电解二氧化锰（EMD）为原料,采用机械球磨-掺杂对其进行改性制备了二氧化锰超级电容器电极材料,并采用SEM和XRD手段对产物进行表征。结果表明,改性前后样品均为球形γ-二氧化锰,改性前电极比容量为140 F/g,通过机械球磨-掺杂Al2O3改性的EMD电极材料比未改性的有较好的循环伏安特性,且当添加质量分数为1%时,电极比容量达到最大,为165 F/g,经过200次循环后容量仍保持在90%以上。     

经验模态分解在切削振动信号分析中的应用

为了分析切削振动的变化规律及产生根源,采用在线监测的方法采集振动信号,提出了运用经验模态分解对振动信号进行分析的方法,构建了分段频率变化的仿真振动信号,分别对仿真振动信号进行短时傅里叶变换、小波变换和经验模态分解时频分析。结果表明,经验模态分解方法具有更高的时频分辨率,运用经验模态分解方法对实际振动信号进行分析,提取典型分量绘制希尔伯特幅值谱,能较好地反映振动信号的时频变化规律。经验模态分解适合对切削振动信号进行分析,但是需要对算法的模态混叠及计算效率低等不足进行完善。研究结果对非平稳信号的分析具有参考价值。     

Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

This paper proposes a hybrid ensemble forecasting methodology that integrating empirical mode decomposition (EMD), long short-term memory (LSTM) and extreme learning machine (ELM) for the monthly biofuel (a typical agriculture-related energy) production based on the principle of decomposition—reconstruction—ensemble. The proposed methodology involves four main steps: data decomposition via EMD, component reconstruction via a fine-to-coarse (FTC) method, individual prediction via LSTM and ELM algorithms, and ensemble prediction via a simple addition (ADD) method. For illustration and verification, the biofuel monthly production data of the USA is used as the our sample data, and the empirical results indicate that the proposed hybrid ensemble forecasting model statistically outperforms all considered benchmark models considered in terms of the forecasting accuracy. This indicates that the proposed hybrid ensemble forecasting methodology integrating the EMD-LSTM-ELM models based on the decomposition—reconstruction—ensemble principle has been proved to be a competitive model for the prediction of biofuel production.     

基于零值补偿和EMD的加速度积分误差消除方法

为探求加速度积分误差的消除方法,利用频谱对实测加速度数据进行分析,以误差频率和理想的加速度变化曲线来评价误差。采用基于零值补偿和经验模态分解(Empirical Mode Decomposition,EMD)的方法,对实测数据进行了误差校正与评价,实践证明该方法能有效消除积分算法中的误差。     

一种基于经验模态分解的信噪比盲估计新算法

为了增强未知样式信号的信噪比估计性能,提出了一种基于经验模态分解（EMD） 的信号信噪比估计新算法,通过固有模态函数（IMF）分量平均周期判断信号与噪声界限。 给出了经验模态分解估计法的工作原理和流程图,分析了经验模态分解估计法的性能。仿真 结果表明,与信号空间分解法一样,经验模态分解估计法能够实现盲信号信噪比估计,后者 估计均方误差比前者要小,在0 dB信噪比下均方误差不超过0.3 dB。     

金融时间序列多分辨率实证研究的EMD方法

针对小波变换的不足之处,运用EMD方法对沪深300指数日收益率时序进行多时间尺度分解,发现其波动存在准2日、准4—5日、准15日、准28日、准70日、准140～190日及准240日等波动周期,并分析了各分量的趋势变化。结果表明,EMD作为一种全新的非线性、非平稳信号多分辨率处理方法,可以更精确地提取金融时序中不同波动周期的分量,在股市等金融系统变量的多时间尺度分析及其建模、预测中具有广阔的应用前景。     

Hedging futures performance with denoising and noise-assisted strategies

Noise processing is very important to improve hedging effectiveness. However, the existing methods are mainly considered from the view of denoising strategy, and the research on noise-assisted strategy is limited. In this paper, a framework that includes both denoising and noise-assisted strategies is proposed to comprehensively analyze the impact of noise proceeding on hedging effectiveness. In detail, the EMD technology is utilized to decompose the futures and spot original returns. Then, the decomposition terms are stepwise removed or added in the opposite way to obtain the denoised and noise-assisted returns. Finally, under the minimum-CVaR framework, the dynamic hedged portfolios based on original and processed returns are constructed to test the hedging effectiveness. Based on the daily prices of CSI300, S&P500, WTI crude oil, and gold futures contract which range from February 9, 2007, to January 10, 2020, the empirical results indicate that both denoising and noise-assisted hedging strategies can decrease CVaR compare with using original return. Furthermore, denoising or adding high-intensity noise has better hedging performance than low-intensity noise, adding uncorrelated noise performs better than adding correlated noise Robustness results by changing confidence level validate the above conclusions.