Cheap control problem of a linear uniform rank system: Design by composite control

Cheap control problems where a small parameter μ² multiplies the control cost are considered. Due to the cheapness of control, a strong control action in the form of high-gain feedback forces the given system to have slow and fast, low and high amplitude variations. For a class of linear systems (uniform rank systems), a systematic procedure of amplitude scaling and time-scale decomposition which normalizes high and low amplitude variations and which separates slow and fast time scales is presented. The method permits the explicit characterization of all the limiting properties of the considered cheap control problem as μ → 0. Methods of calculating singular controls and how non-uniqueness can arise in them are discussed. Above all, several suboptimal composite control schemes are developed based on the decomposition of the given optimal design into two lower order subsystem designs.     

基于ITD和LS - SVM的风力发电机组轴承故障诊断

为了更好地识别出复杂条件下风力风电机组主轴承的运行状态,提出了基于固有时间尺度分解(ITD)和最小二乘支持向量机(LS-SVM)的风电机组轴承故障诊断方法。该方法首先将调心滚子轴承振动信号分解成若干个固有旋转分量和一个趋势分量之和。然后,对前几个固有旋转分量的瞬时幅值进行频谱分析,找出频谱中外圈、内圈、滚动体故障特征频率处以及转动频率处的幅值,将其作为故障特征向量。最后,将故障特征向量输入LS-SVM来识别机组轴承的运行状态。实验结果表明,该方法可以快速、较准确地诊断出风力发电机组轴承故障。     

Non-saturation Throughput of S-ALOHA Using the Time-Scale Decomposition Technique

S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.     

基于本征时间尺度分解算法的齿轮箱故障诊断

针对现有信号处理方法在齿轮箱故障诊断中的不足,将本征时间尺度分解算法( IntrinsicTime- scale Decomposition,ITD)应用到齿轮箱故障诊断中.首先介绍了ITD算法;然后将ITD算法应用到齿轮箱的故障诊断中,得到了正确的结论;最后将ITD算法与经验模式分解(EMD)算法进行了比较,结果表明...     

土流变试验设计及有关问题研究

本文在普通剪切仪基础上改装成土流变试验仪，设计了不同应力状态下上的蠕变和应力松驰试验。重点讨论了土流变试验的加载或加应变的方式和标准，提出了土流变试验中存在着时间尺度效应等问题，最后讨论了其对土流变试验的影响及相应的解决方法。     

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time-scale domains

Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT-11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de-noising of cardiac sounds using multiresolution-decomposition and multiresolution-reconstruction (MDR-MRR). The de-noised signal is then segmented based on frame-windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel-frequency) and time-scale (T-S) domain (based on Wavelet Transform) features are extracted from the de-noised signal after segmentation. The features, extracted from the Cp-domain and the T-S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K-nearest neighbor (KNN). The performance of the classifications is assessed based on the k-fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big-O measurements. The T-S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T-S domain.     

近60年黄河下游年水沙量变化的多时间尺度特征

以黄河下游花园口站为例,基于1952～2009年年水量和年沙量资料,采用经验模态分解及Hilbert-Huang变换方法分析了年水沙量变化的多时间尺度特征,探讨了各波动分量的变化原因及影响因素,获得了不同波动周期的振荡分量及趋势分量,揭示了年水沙量变化的多时间尺度结构,其中准3年左右的周期波动是引起水沙波动的重要原因。     

大规模风电接入电网多目标电源的协调控制

针对负荷、风电出力波动规律和电网不同时间尺度安全运行目标,采用多时间尺度、多电源、多控制目标的协调控制策略,提出了基于大规模风电消纳的协调控制技术,构建了区域电网内风电功率与常规电源的协调控制系统。利用该协调控制系统,在有功控制方面解决了未来7d、日前、日内风电有功出力和燃煤火电机组的协调控制问题,在无功控制方面解决了考虑风电功率分布规律的无功补偿容量优化的决策问题。     

自适应时间尺度分解方法及其应用

针对齿轮故障振动信号的非线性、非平稳和多分量的特征,在定义了瞬时频率具有物理意义的本征时间尺度函数(intrinsic time-scale function,ITF)的基础上,结合固有时间尺度分解中基线信号的构造方法,提出自适应时间尺度分解(adaptive time-scale decomposition,ATD)的时频分析方法,该方法可以自适应地将一个复杂信号分解为若干个瞬时频率具有物理意义的本征时间尺度分量之和.仿真分析验证了ATD方法的有效性以及定义本征时间尺度函数方法的合理性.分别将ATD、经验模态分解(EMD)、局部均值分解(LMD)和固有时间尺度分解(ITD)与包络解调分析相结合应用于斜齿轮故障诊断中,实验结果表明:自适应时间尺度分解方法在保证分解结果正确性的前提下,计算效率方面具有明显优势,将该方法与包络解调相结合能够有效提取到齿轮的故障特征.