基于改进LMD方法的风电机组齿轮箱故障诊断研究

局部均值函数的求取是局部均值分解(LMD)的关键环节。针对局部均值函数求取存在偏差进而造成模态混叠的问题,提出了一种基于局部积分均值的LMD风电机组齿轮箱故障诊断方法。该方法改变了对相邻两极值点求平均值的思路,采用求取相邻两极值点的局部积分均值,再通过滑动平均法进行平滑处理,最终得到局部均值函数。为实现风电机组齿轮箱故障诊断,首先采用改进LMD方法对信号进行降噪处理,然后采用多尺度熵提取降噪处理后信号的特征向量,最后采用极限学习机进行故障诊断。通过仿真分析,证明了该方法能有效解决模态混叠现象,提高了LMD的分解精度。试验验证分析表明,该方法的故障诊断准确率为100%,通过对比分析表明,该方法优于其他故障诊断方法,具有工程应用价值。     

基于Shannon熵的飞机交流发电机振动分析

飞机交流发电机是飞机关键部件之一,监测其健康状况具有重要意义。通过设计实验数据获取平台采集交流发电机在转速上升(从6000转/分到8500转/分)、平稳运行(7500转/分)及转速下降(8500转/分到6000转/分)三个阶段的振动信号,利用符号序列Shannon熵的计算方法以及符号化方法设定合适参数对振动信号进行了处理,得出了交流发电机在三个阶段的振动信号的Shannon熵;通过初步的分析表明,交流发电机在正常状态下三个阶段的Shannon熵都比较有规律的在一定范围内变化,一旦发生振动异常将会引起Shannon熵突变;经过分析讨论,Shannon熵的突变与否是可以用来反映交流发电机在机械方面是否存在故障隐患的。     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

An information-theoretic approach to the written transmission of old English

Information theory offers a means for analyzing some constraints on the reading and copying process in Old English. Entropy for strings of various lengths offers a baseline measure of the uncertainty involved in transmission of Old English texts, while avoiding the pitfalls of applying models of modern reading to early medieval practice. Analysis of lengthy prose and verse texts in Old English revealed uniformly high values for entropy at all string lengths. High entropies may be the result of the language's irregular orthography, poetic koiné, and several dialects and imply that the language may have been easy to write but difficult to read. The low redundancy of the language which its high entropy values indicate suggests that the reader of Old English played an enhanced role in decoding a text and may provide an explanation for the high variability in the transmission of Old English verse.Katherine O'Brien O 'Keeffe is Professor of English at Texas A&M University and a co-director of its Interdisciplinary Group for Historical Literary Study.William Rundell is Professor of Mathematics at Texas A&M University.     

基于改进最大熵算法系统传输速率优化研究

研究系统速率优化提高问题。随着用户对系统的传输速率要求越来越高,传统的系统优化传输速率算法由于参数设置难,而造成算法复杂度特别高,使得系统的传输速率降低,为了解决上述问题,在基于误比特率和功率的分析基础上,提出了一种改进的最大熵算法优化系统传输速率方法。方法首先给出系统模型并提出自适应最大熵调制算法理论,然后在发送端和接收端能够完全知道信道状态信息的情况下,给出了最大化传输速率的低复杂度比特和功率分配算法。仿真结果表明,提出的算法可以有效地满足系统地误比特率要求,同时大大降低了系统的计算复杂度,是一种有效地系统传输速率优化方法。     

Vague集的新模糊熵

本文指出了现有Vague集模糊熵定义的不足,分析了其存在不足的根本原因,给出了Vague集模糊熵的公理化定义,提出了一种新的Vague集模糊熵的计算方法,并证明了其合理性和有效性。     

Study on structure and magnetic entropy changes of Ce2-xPrxFe16.5Co0.5 alloys

A series of Ce2-xPrxFe16.5Co0.5 alloys were preparedby arc melting under purified argon atmosphere. The structure and magnetic entropy changes in Ce2-xPrxFe16.5Co0.5 alloys were investigated by means of X-ray diffraction pattern and MPMS XL-7 magnetometer. The experimental results show that the crystal structure of Ce2-xPrxFe16.5Co0.5 alloys keeps in TH2Zn17-type rhombohedral, and the Curie temperature of Ce2-xPrxFe16.5Co0.5 alloys can be shifted to room temperature around by a composition adjustment. The magnetic entropy changes (-ΔSM) in Ce2-xPrxFe16.5Co0.5 alloys are relatively large, and a platform of magnetic entropy changes appearsnear the temperature TC. Ce2-xPrxFe16.5Co0.5 alloys are the potential working media for magnetic refrigeration with their stable chemical properties and especially low price.     

GIL机械故障诊断与预警技术研究

为了有效诊断气体绝缘金属封闭输电线路(GIL)的机械故障,搭建了110 kV GIL试验平台并设计了3种典型机械故障,通过互补集合经验模态分解(CEEMD)模糊熵值与鲸鱼优化极限学习机(WOA-ELM)模型联合方法对GIL机械故障模式进行识别与诊断。首先,利用CEEMD方法对振动信号进行分解,引入正负白噪声组对信号进行处理,得到含有故障信息的模态分量(IMF)。其次,利用模糊熵计算模态分量特征值,得到能表征故障特征的模糊熵值。最后,结合WOA-ELM模型对特征向量集进行模式识别,根据聚类结果与自适应阈值对GIL设备机械故障进行诊断和预警。结果表明,利用CEEMD与模糊熵对GIL振动信号特征进行分析,可以有效避免模态混叠和冗余噪声分量的干扰,得到能够表征故障特征的特征值;利用WOA-ELM模型可以有效实现GIL设备机械故障诊断与预警。     

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical ‘Geometry, Frequency, Noncovalent, eXtended Tight Binding’ (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent ‘Statistical Assessment of the Modeling of Proteins and Ligands’ (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.     

基于相对熵的KNN文本分类方法的研究

摘要:为提高处理文本相似度的效果,提出了一种基于相对熵度量文本差异的KNN算法.该算法首先对文本进行预处理(分字与删去停用字)和构建特征字字典; 然后计算训练集中所有文本特征字的概率,并组成训练集(特征字概率矩阵); 最后计算预测文本的特征字概率向量,并通过计算和统计K个预测文本与训练集文本间相对熵最小的文本类别个数后将数目最多的类别作为测试样本的类别.实验结果表明,该算法的分类效果不仅显著优于传统KNN、SVM、Decision Tree、朴素Bayes算法的分类效果,且在小样本数据情况下