Generalized Integral Fluctuation Relation with Feedback Control for Diffusion Processes

We extend a generalized integral fluctuation relation in diffusion processes that we obtained previously to the situation with feedback control. The general relation not only covers existing results but also predicts other unnoticed fluctuation relations. In addition, we find that its explanation of time-reversal automatically emerges in the derivation. This interesting observation leads into an alternative inequality about the entropy-like quantity with an improved lower bound. Two feedback-controlled Brownian models are used to verify the result.     

垂直上升管泡状流压力波动的多尺度分析

从理想的单气泡运动物理模型和离散正交小波与统计相结合的信号处理技术研究了泡状流压力波动的机理与多尺度时频规律,得到以下结论:泡状流的压力波动主要来源于气泡的运动和诱导湍流及液相湍流,其中在低流速下前者产生的压力波动幅度大于后两者;存在一个临界频率,低于该频率压力信号均方根随频率的增加而增加,高于该频率压力信号均方根随频率的增加而减小;发生流型转变时压力信号的时频特征发生明显改变,最大均方根出现在更低的频率区。     

Sr2RuO4超导转变温度附近c方向磁阻行为研究

测量了Sr2RuO4正常态和超导转变温度附近(T=1.5K,4.0K,6K)c方向的磁阻,通过对正常态磁阻分析获得了Sr2RuO4在温度4K和6K下的平均自由程.通过超导涨落分析获得了Sr2RuO4的超导特性参数ξab(0)和ξc(0).     

稳定耦合效率的沟槽结构半导体激光器

为了稳定半导体激光器激射光束在光纤传输过程的耦合效率,提出一种沟槽结构的半导体激光器,并对该结构激光器的光束、耦合效率及P-I特性进行研究。在普通条形半导体激光器的脊形区刻蚀了周期性的沟槽结构,来改善半导体激光器有源区的增益分布。通过对比普通结构与沟槽结构半导体激光器的光束分析,测试其耦合效率以及P-I特性。结果表明:沟槽结构的半导体激光器能够使光腔内模式更加稳定,输出光束更加集中,并避免了"Kink"效应的发生;与此同时,耦合效率提高至97.7%,并且较普通结构激光器更为稳定。沟槽结构半导体激光器有效地解决了光斑跳动问题,稳定了激光器的耦和效率。     

Critical Behaviors and Finite-Size Scaling of Principal Fluctuation Modes in Complex Systems

Complex systems consisting of N agents can be investigated from the aspect of principal fluctuation modes of agents. From the correlations between agents, an N×N correlation matrix C can be obtained. The principal fluctuation modes are defined by the eigenvectors of C. Near the critical point of a complex system, we anticipate that the principal fluctuation modes have the critical behaviors similar to that of the susceptibity. With the Ising model on a two-dimensional square lattice as an example, the critical behaviors of principal fluctuation modes have been studied. The eigenvalues of the first 9 principal fluctuation modes have been invesitigated. Our Monte Carlo data demonstrate that these eigenvalues of the system with size L and the reduced temperature t follow a finite-size scaling form λ_n(L, t)=L^γ/ν f_n(tL^1/ν), where γ is critical exponent of susceptibility and ν is the critical exponent of the correlation length. Using eigenvalues λ₁, λ₂ and λ₆, we get the finite-size scaling form of the second moment correlation length ξ(L, t)=Lξ(tL^1/ν). It is shown that the second moment correlation length in the two-dimensional square lattice is anisotropic.     

Comparisons of MFDFA,EMD and WT by Neural Network,Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform (WT) with each of neural network (NN), Mahalanobis distance decision rules (MDDR) and support vector machine (SVM). Following this, performance of different combinatorial methods was compared using a group of gearbox vibration data containing slightly different fault patterns. The results indicate that MFDFA performs better in feature extraction of gearbox vibration data and SVM does the same in fault identification. Naturally, the method associating MFDFA with SVM shows huge potential for fault diagnosis of gearboxes. As a result, this paper can provide some useful information on construction of a method for gearbox fault diagnosis.     

Multifractal analysis of the fracture surfaces of foamed polypropylene/polyethylene blends

The two-dimensional multifractal detrended fluctuation analysis is applied to reveal the multifractal properties of the fracture surfaces of foamed polypropylene/polyethylene (PP/PE) blends at different temperatures. Nice power-law scaling relationship between the detrended fluctuation function F_q and the scale s is observed for different orders q and the scaling exponent h(q) is found to be a nonlinear function of q, confirming the presence of multifractality in the fracture surfaces. The multifractal spectra f(α) are obtained numerically through Legendre transform. The shape of the multifractal spectrum of singularities can be well captured by the width of spectrum and the difference of dimension . With the increase of the PE content, the fracture surface becomes more irregular and complex, as is manifested by the facts that increases and decreases from positive to negative. A qualitative interpretation is provided based on the foaming process.     

水平管段塞流压力/压差波动特性分析

本文在对水平管段塞流压力波动特性进行理论分析的基础之上,对其压力/压差波动特性进行了试验研究。结果表明,折算液速对段塞频率的影响作用远大于折算气速的影响作用;分别增加管线中的气量、液量,或者气液量同时增加,均会造成管线运行过程中均值压力/压差和最大压力/压差的增加;压力信号的概率密度分布大部分呈双峰分布,但其中也存在单峰和多峰分布;压差信号的概率密度符合正态分布;压力信号的功率谱密度具有频率波动范围窄、幅值大的特点;与同工况压力信号的功率谱密度相比,压差信号的功率谱密度具有频率波动范围宽、幅值小的特点。     

消光起伏光谱的时间相关处理

消光起伏光谱法(TFS)是一种新的颗粒测量方法,可同时测量两相流中颗粒的粒径分布和体积浓度。由于在测量原理和结构上非常简单,这种方法可用来实现在线、实时测量。然而在实际测量中,消光起伏光谱法对颗粒粒径分布的分辨率还比较低且对高浓度颗粒系的测量须考虑颗粒相互作用效应。本文提出一种新的数据处理方法-消光起伏相关光谱法(TFCS),通过对消光起伏信号在不同相关时间参数下进行相关计算来得到消光起伏光谱以提高消光起伏法对颗粒粒径分布的分辨率。     

Correlation between detrended fluctuation analysis and the Lempel–Ziv complexity in nonlinear time series analysis

We focus on the study of the correlation between the detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamical systems including logistic map and Duffing model are investigated. Moreover, the influences of the Gaussian random noise on both DFA and LZC are analyzed. The results show a high correlation between DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent α and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between DFA and LZC is applied to the feature extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained.