基于最小熵翻卷积的网络故障特征提取仿真

传统的基于FRFT网络故障特征提取方法当网络信号发生突变时,由于受到噪声和信号衰弱的影响,导致网络故障特征极其微弱,并且网络的拓扑结构和权值分布成非线性映射,将信号简单排列成矩阵,无法有效实现对网络故障特征的提取。提出一种基于小波滤波以及最小熵翻卷积的网络故障特征提取方法,将突变信号在与之相邻尺度上的小波系数直接相乘,依据阈值对噪声中的网络故障信息进行采集并过滤噪声,使获取的小波系数信噪比大大增强。将突变信号小波变换值在几个尺度上进行计算,实现网络故障特征的初提取。获取一个逆滤波器,通过网络输出恢复网络输入信号,依据解卷积后获取的序列对可能估计值的最优解进行计算,求出逆滤波器矩阵,分析了最小熵归迭代算法的具体实现过程。仿真结果表明,所提方法具有很高的准确性。

基于谱熵梅尔积和改进VMD的轴承故障预警

针对传统轴承故障预警实时性较差、故障特征提取准确性影响预警效果的问题,将语音端点识别思想进行迁移,采用谱熵梅尔积特征的双门限法实时追踪故障起始点.为克服变分模态分解(variational mode decomposition,VMD)参数选取不当和端点效应对提取效果造成的影响,提出能量差网格搜索法对VMD进行参数寻优,并用支持向量回归机对端点效应进行抑制,结合多尺度加权排列熵在检测振动信号随机性方面的优势,充分发挥VMD对信号的重构能力,对起始点后的故障段进行特征捕捉.通过实际轴承故障信号的实验及数据分析,验证了该方法在轴承故障预警中的有效性.

以熵的观点对益心康泰治疗脑血管病作用的初步探讨

本文以熵的观点对藏药益心康泰治疗脑血管病的作用机制进行了初步探讨。在分析脑血管病发病方面引进了积熵观点,通过对该药药性、配方分析认为益心康泰可增加负熵、消除体内积熵可能是其作用机制之一。

基于WSGMD和AMOMED的风力发电机齿轮箱轴承早期故障诊断

围绕风力发电机齿轮箱轴承早期故障诊断困难这一难题,提出一种基于加权辛几何模态分解和自适应多点最优最小熵解卷积的诊断新方法。首先,采用辛几何模态分解将原始振动信号分解为多组辛几何分量,计算各分量的Gini系数并据此对各分量进行加权重构,得到信噪比提升的重构信号。为了进一步强化放大重构信号中的故障特征,通过鲸鱼优化算法对多点最优最小熵解卷积的滤波长度及冲击周期进行自动搜索,获取最佳参数后对重构信号做进一步解卷积处理,最终通过解卷积信号的包络解调分析来识别轴承服役状态。风电现场机组振动监测信号验证表明,该方法可准确辨识出轴承早期故障,具有广阔的工程应用前景。

一种新的声发射时差定位方法

基于声发射信号具有与语音信号相似的非线性和非平稳性的特点,结合语音信号端点检测原理,将谱熵能量积引入声发射信号到达时间的测量中,以此计算出声发射信号的到达时间,进而由时差计算声发射源的位置。在实验中,构造声发射源面定位实验,并通过数学方法消除速度参量,使定位结果仅与到达时间有直接关系。以此计算声发射源坐标,该方法有效提高了定位精度,其中相对定位误差最大为2.49%。

基于谱熵梅尔积的语音端点检测方法

为了克服传统语音端点检测算法在低信噪比环境下准确率低的问题,提出一种基于谱熵梅尔积(MFPH)的语音端点检测算法.首先,提取带噪语音信号的梅尔频率倒谱系数中的第一维参数MFCC0,将其与谱熵的乘积作为最终区分语音段和背景噪声段的融合特征参数;然后,结合模糊C均值聚类算法和贝叶斯信息准则(BIC)算法对MFPH特征参数门限值进行自适应估计;最后,采用双门限法进行语音端点检测.实验结果证明,与传统方法比较,该方法在-5~15 d B低信噪比环境下的语音端点检测准确率有较大提高.

基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法

针对注意缺陷与多动障碍(ADHD)临床诊断主要依靠医生主观评估,缺乏客观辅助依据的问题,提出了一种基于语音停顿度和平坦度的ADHD自动检测算法。首先,通过频带差能熵积(FDEEP)参数自动定位语音有话区间,并提取停顿度特征;然后,使用变换平均幅度平方差(TAASD)参数计算语音倍频率,并提取平坦度特征;最后,结合融合特征和支持向量机(SVM)分类器来实现ADHD的自动识别。实验共采集了17位正常对照组儿童和37位ADHD患儿的语音样本。实验结果表明,所提算法能自动检测正常儿童和ADHD患儿,识别正确率为91.38%。

基于语音流畅度与模糊聚类的精神分裂症自动识别

针对精神分裂症诊断周期长、缺乏客观诊断依据的问题,提出语音流畅度矩形参数过能熵积,结合模糊聚类双重定位停顿区域的算法辅助诊断精神分裂症。该算法综合了精神分裂症患者语音流畅度低及能量平缓的特征,定位精神分裂症语音的停顿区域,提出语音流畅度量化参数提取算法,结合SVM(支持向量机)分类器,实现精神分裂症的自动识别。提取28例精神分裂症患者和28例正常对照组语音的流畅度声学特征,自动识别精神分裂症,正确率为85%以上。提出的基于过能熵积与模糊聚类的精神分裂症自动识别算法,能为临床诊断精神分裂症提供客观、有效、无创的辅助依据。

Discussion of Garfinkle-Horowitz-Strominger Dilaton Black Hole Entropy

We discuss the entropy of the Garfinkle-Horowitz-Strominger dilaton black hole by using the thin film brick-wall model, and the entropy obtained is proportional to the horizon area of the black hole confirming the Bekenstein-Hawking＇s area-entropy formula. Then, by comparing with the original brick-wall method, we find that the result obtained by the thin film method is more reasonable avoiding some drawbacks, such as little mass approximation, neglecting logarithm term, and taking the term L^3 as a contribution of the vacuum surrounding the black hole, and the physical meaning of the entropy is more clearer.

Quantification of Blur and Its Applications

Human visual sense has two aspects in our feeling for blurred image, that is, one is the amount of blur depending on object size, the other is the amount of blur independent of the object size. In the former for example, when the image size becomes larger, the author feels smaller amount blur. The quantitative evaluation based on entropy for blurred images is proposed in this paper. The author calls this metric ＂variation entropy＂. This metric has two kinds of aspects that coincide with the human visual sense. The first is the absolute evaluation of blur, and the second is the relative evaluation of blur. The former can be quantified by variation entropy for a unit boundary length （or L-type variation entropy： HL ）, which is dependent on resolution, and the latter can be quantified by variation entropy for a unit area （or A-type variation entropy： H^A ）, which is independent of resolution. These two metrics have complementary properties. At last, two variation entropies are applied to the standard kanji character database, and then the strong relation between variation entropy and accuracy of recognition is discussed. The tendency of writing skills for grades is evaluated by applying the metric to a database collected from school children.

Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm

A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrangement of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum.A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations.Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is feasible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the algorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.

Entropy of Schwarzschild-de Sitter Black Hole with Generalized Uncertainty Principle Revisited

We study the entropy of Schwarzschild-de Sitter black holes based on generalized uncertainty principle with brick-wall method by counting degrees of freedom near the horizons and obtain the entropy proportional to the surface area at the horizons without cut-off. And reveal the possible value of the minimum length.

An Improved Interactively Method Based on Fuzzy Satisfying Degree for Multi-objective Decision Making

In order to make a decision in the face of multiple objectives, it is necessary to know the relative importance of the different objectives. Yet, it is often very difficult to specify a set of precise weights before possible alternatives solutions are known. In this paper, we present an improved weighted method, which is based on a modified definition by the membership function of fuzzy theory; an interactive, iterative method for arriving at an acceptable solution. The decision maker gradually discerns what is achievable and adjusts his aspirations and implicitly the specification of weights and trade-offs between his objectives, in the light of what he learns. To aid the decision maker＇s cognition and to allow him to express his wishes in a natural way, we present decision maker with grey relational degree to select the best solution from the finite solutions.

On Renyi entropies of order statistics

In this paper we consider a generalize dynamic entropy measure and prove that this mea- sure characterizes the distribution function uniquely. Also we propose cumulative resi- dual R^nyi entropy of order statistics and prove that it also determines the distribution function uniquely. Applications of entropy concepts to DNA sequence analysis, the ulti- mate support for the biological systems, have been widely explored by researchers. The entropy measures discussed here can be applied for analysis of ordered DNA sequences.

Entropy Quantization from a Neutral Black String

Taking into account the Bekenstein-Hawking area law,based on the analysis of Zeng and Liu et al.that area spectrum is determined by the periodicity of an outgoing wave,we discuss on the quantization of entropy from a neutral black string.In addition,applying the adiabatic invariant quantity method proposed by Majhi and Vagenas,we further verify the entropy quantum of the neutral black string.As a result,two different methods show that the quantum of entropy is △S = 2π,which is in agreement with Bekenstein's proposal.