基于弧形阵列的局部放电Dir-MUSIC定位算法

局部放电是衡量电力设备绝缘状态的重要指标,局放检测需要解决局放源定位问题。多重信号分类(multiple signal classification,MUSIC)采用全向天线作为接收阵列,可实现多源信号的超分辨率空间谱估计,但要求高信号采样率,且在低信噪比情况下抗干扰能力不足。为此,提出基于弧形阵列的Dir(directional)-MUSIC算法,采用定向天线接收信号的强度信息,实现低信噪比下的局放源波达方向估计。设计了接收局放信号的Vivaldi天线阵列,并在不同信噪比下对算法的有效性进行仿真验证。结果表明:在低信噪比-10 dB来波方向5°下角度误差为0.14°,优于MUSIC算法;阵列在信噪比10 dB,测向范围[-80°,80°]内定位均方根误差小于1.5°。证明了基于弧形阵列的Dir-MUSIC算法有效提高了局放定位精度,且对噪声具有良好的鲁棒性,具有用于局放检测的潜力。

基于MUSIC算法特征值损伤因子的板状结构损伤程度评估

研究了多重信号分类(MUSIC)算法在板状结构损伤检测中的应用,提出一种基于MUSIC算法特征值的损伤因子,为基于MUSIC算法的板状结构损伤成像技术提供了一种可靠的损伤程度评估理论。首先利用MUSIC算法计算的高精度特征值和Lamb波损伤散射信号幅值的相关性,采用Abaqus有限元仿真软件模拟不同程度的损伤,将板状结构中的损伤成像定位之后,根据散射信号将结构损伤程度转化为特征值变化量,根据特征值计算损伤因子,建立损伤评估模型预测损伤程度,并通过试验验证其正确性。试验结果表明,在合适的激励频率下,特征值损伤因子随着损伤程度的增加呈现出线性变化,能较好地反映损伤程度;该方法具有较高的准确性和稳定性,在一定损伤程度内能够有效地反映结构损伤程度。

MUSIC学习动机模型在仪器分析教学中的实践与研究

基于MUSIC(Empowerment-Usefulness-Success-Interest-Caring)学习动机模型开展仪器分析教学工作,是目前调动学生学习积极性和锻炼自主学习能力的重要依托。文章立足MUSIC学习动机模型赋权分析、有用研究、成功探索、兴趣指引、关怀体验步骤,深入分析实践应用准则,并围绕现有教学不足确定优化方向,通过明晰学习任务目标、探寻知识需求、构建多样实践活动、立足教材整合资源、师生深度互动评价等策略,促进高职学生良性成长。

DOA估计算法的一种修正MUSIC算法的研究

传统改进MUSIC算法通过对接收信号协方差矩阵作预处理,使信号协方差矩阵分解得到信号子空间与噪声子空间正交,从而降低噪声的影响。但当信号间隔很小时,随着信噪比的降低,传统改进MUSIC算法已无法分辨出信号。基于此问题提出的修正MUSIC算法在使信号子空间与噪声子空间正交的基础上,充分利用了噪声子空间及其特征值对噪声子空间的修正,进而构造谱峰搜索函数估计出信号。通过仿真实验,证实了在信噪比很低的情况下,信号间隔很小且存在相关信号时,修正MUSIC算法能准确地估计出传统改进MUSIC算法不能估计的信号。

The Impact of Western Scholars on the Music Education of the Jingpo Ethnic Group in Yunnan,China

This research attempts to uncover the influence of Western scholars’activities on music education in the Jingpo areas of Dehong Dai-Jingpo Autonomous Prefecture in western Yunnan,China,during the early 20th century.The differences exist in the cultures,languages,and beliefs of the various ethnic groups in Yunnan.As such,systematic basic education is difficult to carry out in order,especially music education.However,this phenomenon changed in the early 20th century with the arrival of Western scholars.This study will analyze existing literature and data collected in fieldwork to trace the origins of Western music education in the Jingpo areas.In addition,the study intends to explore the localized and modernized music education model used by Western scholars for Jingpo.Furthermore,this research may help broaden the understanding of minority communities to the development of music education policies that precede the implementation of regulations.Additionally,it seeks to raise awareness of the music of ethnic minority groups worldwide and to achieve equality of musical education resources in ethnic minority areas as much as possible.

The Impact of Contextual Teaching Method on Sichuan Folk Song Education and Students’Musical Expressiveness

This study explores the application of the contextual teaching method in Sichuan folk song education and its impact on students’musical expressiveness.By incorporating contextual teaching methods in music classes,this research investigates the effectiveness of this approach in enhancing students’understanding of Sichuan folk songs and improving their musical expressiveness and emotional expression.A mixed-method research approach is employed,utilizing classroom observations,questionnaires,interviews,and statistical analysis to assess the practical outcomes of contextual teaching in folk song education.

The Future of Detroit’s Music Industry:A Descriptive Study of Industry Professional Views

The purpose of the present study was to describe and provide insight on Detroit’s music industry,with the practical goal to inform the current state of music in Detroit and draw conclusive suggestions for bringing music back to a forefront of Detroit.The study drew on a sample of convenience(N=4)by researcher networking and past work in the Detroit music industry.Eight themes were identified in a content analysis of interview responses.The study revealed a deep and common vision shared among diverse industry professionals-to bring national recognition back to Detroit.Everyone wants Detroit’s music industry to make a comeback,however,they realize it is a slow process to achieve that goal.

An Analysis of the Musicality of Yong Liu’s Yulinling:Farewell in Autumn Based on the Perspective of Language Musicology

From the perspective of linguistic musicology,this paper discusses the musical features of Yulinling:Farewell in Autumn,a representative work of Yong Liu,a famous lyricist of the Song Dynasty.Through a systematic analysis of the core elements of the poem,such as rhyme,timbre,key,and rhythm,it reveals the intrinsic connection between poetry and music and proposes a new way of thinking about the modern interpretation of ancient poetic songs.This paper not only enriches the understanding of Yong Liu’s works but also provides new theoretical support for modern music education of classical poetry.In addition,this paper explores the practical application value of poetic musicality analysis in cultural inheritance and modern adaptation of classical poems,providing new insights into the contemporary inheritance of cultural heritage.

Exploration of Oral History in Ethnomusicological Research

Oral history,which collects historical materials by means of oral interviews,is an important auxiliary means for ethnomusicological research.Marked by the First Symposium on Oral History of Music in 2014,the academic community has launched a series of discussions and elaborations on its concepts,methods,norms,meanings,and other dimensions,and a series of academic achievements have been born.In this paper,we will combine relevant theses to explain how music oral history as contemporary history can achieve“intertextuality”in ethnomusicological research,emphasize its humanistic attributes,and summarize our reflections on the basis of existing academic experiences.

基于DSP的快速MUSIC测角算法

基于阵列信号处理的MUSIC算法,由于其超高的角分辨率和角精度,受到广泛的关注。但是其高运算复杂度,较大地限制了算法的硬件实现。为此,基于MUSIC算法的基本原理,文中分析了该算法在DSP(TI1843)硬件系统实现中关键子步骤的运行时长。针对最为耗时和占用内存最大的空间谱构建和谱峰搜索子步骤,分别提出了便于工程实现的简化方法,并通过硬件平台移植验证了算法的可行性,对比发现该方法大大减少了运算时间和内存空间,具有较高实际应用价值。

基于卡尔曼滤波与MUSIC算法的传感阵列定向方法

对局放源信号的准确测量与有效识别是局部放电检测、定位及分析的关键。受电磁传感器方向特性灵敏度局限,现有基于到达角(AOA)定位的局部放电定位方法主要运用于声音信号。为此,提出一种卡尔曼滤波算法与多信号分类器(MUSIC)算法相结合的特高频传感阵列定向方法,即首先采用卡尔曼滤波算法能够有效处理电磁幅值信号,减小信号波动性及测量误差,大大提升信号测量精度;然后针对单一传感器建立了传感器方向性的参考矩阵,对任意来波信号使用MUSIC算法进行数据匹配,从而得到精确的来波方向;最后经过试验验证,所提算法可将传感器阵列的方向识别结果误差减小至5°以内,提升了测量精度。

改进MUSIC算法的超声波测风方法研究

针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。

局部放电定向超声阵列Dir-MUSIC测向算法仿真

超声波检测方法在电力设备绝缘状态检测定位中应用广泛。针对局部放电超声测向MUSIC算法存在的采样率要求高、计算复杂度大等不足,提出基于定向超声阵列信号强度信息的定向多重信号分类(directional multiple signal classification,Dir-MUSIC)算法。在阐述该算法理论模型和应用条件基础上,针对均匀圆盘超声阵列,仿真研究了不同增益方向图主瓣宽度、不同信噪比条件下Dir-MUSIC算法的测向精度。仿真结果表明8阵元阵列在-5 dB信噪比、方向图主瓣宽度为90°~120°时测向精度最高,均方根误差小于2°。最后基于研制的微型机电系统麦克风(microelectro-mechanical system,MEMS)定向超声阵列进行了测向试验,结果表明8阵元圆盘超声阵列测向均方根误差最小为2.76°,测向标准差最小为2.72°,验证了Dir-MUSIC算法的有效性与准确性。

降维长矢量MUSIC算法的高速并行FPGA实现

在极化敏感圆阵通过降维长矢量多重信号分类(DRLV-MUSIC)算法实现DOA估计的现场可编程门阵列(FPGA)实现时,为了进一步缩短计算时间的同时节省硬件占用资源,提出一种特征值与特征向量并行计算的FPGA实现结构,同时利用矩阵相乘的重复性,提出一种并行矩阵运算方法,缩短FPGA的计算时间同时节约硬件资源。该FPGA方案由预处理模块、协方差矩阵计算模块、并行Jacobi算法计算特征值模块、并行特征向量计算模块组成。实验结果表明,与非并行的DRLV-MUSIC算法相比,该方案在减少计算时间、降低硬件占用资源的同时保证了测角精度。

一种奇异值分解与子空间加权联合的改进MUSIC算法

在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力。针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法。该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合。仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法。

基于Root-MUSIC算法的激光扫频干涉测量方法研究

玻璃的厚度作为工业生产中的一项重要的指标,如何对其快速的测量成为了研究热点。提出采用激光扫频干涉测量方法,将采样的信号进行插值拟合,校正其拍频非线性,再通过频谱估计法获取距离信息。当测量厚度较小的玻璃时,FFT算法因受栅栏效应和频谱干涉的影响,导致难以在频谱图中将玻璃前后表面的频率区分出来。为解决该问题,采用Root-MUSIC算法处理信号,实验结果表明在FFT算法分辨临近目标频率失效的情况下,Root-MUSIC算法可以进行有效区分,运算效率也远高于MUSIC算法,且精度可达50微米左右,为解决玻璃厚度测量问题提供了思路。

The Function of Education in Codification of Radif in Iranian Dastgahi Music

The goal of this research is to investigate the ideas that functioned in shaping thought of Radif in Iranian Dastgahi music. In this regard by preparing the reasons, we pay attention to the function of education in codification of Radif. Although Iranian traditional music has long history but its codification to seven Dastgah-s and five Avaz-es in the format of Radif is attributed to Qajar Dynasty in the 19th century. In this transformation, Mirza Abdollah, Tar-Player of the Nasser al-Din Shah Period, has played an important role in final codification of Radif. Due to the truth that the past masters scrimped in music education, and Mirza Abdollah had encountered with problems to learn music, hence he has mostly cared about music education in adulthood, and it is probable that one of the reasons in collecting Gusheh-s （approximately a small piece of the Iranian Dastgahi music） and organizing them into coherent sets was making simplicity for the students to learn. Furthermore, masters who seriously and continuously have taught after Mirza Abdollah, left a narration （version） of Radif. On the other hand, definite patterns in placement of Gusheh-s and phrases of the Radif have been organized in a manner that makes educational function for it.

利用改进MUSIC方法进行洋流方位估计

洋流能够调节全球热量的分布,降低航运成本。以往的洋流方位估计方法未对洋流信号源之间的相关性进行完全消除,导致估计效果较差。因此,本文设计了基于改进MUSIC方法的洋流方位估计方法。首先,以采集到的洋流信号为基础,构建洋流阵列信号模型,并对洋流信号进行降噪处理,提高信号数据的质量。然后,为减少洋流信号源之间相关性对估计结果的影响,利用改进MUSIC方法及协方差矩阵,对洋流信号源相关性进行消除,通过计算信号源的相关参数,构建洋流方位估计模型。最后,通过对洋流信号源的转换,实现洋流方位的估计。在仿真试验中,以南海部分海域为试验对象,对不同谱点下估计方法的估计效果进行评价,与以往的洋流方位估计方法相比,设计的基于改进MUSIC方法的洋流方位估计方法精度高达97.2%,应用效果更好。

DQ变换和MUSIC算法在ITER磁体电源信号间谐波检测中的应用

随着国际热核聚变实验堆(International Thermonuclear Experimental Reactor,ITER)计划的逐步开展,保证ITER磁体电源系统的稳定运行显得尤为重要。文章采用将DQ变换和多信号分类(multiple signal classification,MUSIC)算法相结合的方法进行间谐波频率检测,信号的幅度和相位由最小二乘法来估计。DQ变换可以消除大幅度ITER基波分量,MUSIC算法可以通过矩阵特征分解检测出短数据条件下的谐波和间谐波,适用短时平稳的间谐波检测,两者相结合可以有效检测出大幅度基波附近存在小幅度间谐波。仿真实验表明,计算经DQ变换后检测出的ITER信号谐波频率时,取中间信号计算真实频谱较为正确,两侧信号则有较大的误差。

Classification of musical hallucinations and the characters along with neural-molecular mechanisms of musical hallucinations associated with psychiatric disorders

BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiological classification of MH is of particular importance and offers valuable insights to understand MH,and further to develop the effective treatment of MH.Over the recent decades,more MH cases have been reported,revealing newly identified medical and psychiatric causes of MH.Functional imaging studies reveal that MH activates a wide array of brain regions.An up-to-date analysis on MH,especially on MH comorbid psychiatric conditions is warranted.AIM To propose a new classification of MH;to study the age and gender differences of MH in mental disorders;and neuropathology of MH.METHODS Literatures searches were conducted using keywords such as“music hallucination,”“music hallucination and mental illness,”“music hallucination and gender difference,”and“music hallucination and psychiatric disease”in the databases of PubMed,Google Scholar,and Web of Science.MH cases were collected and categorized based on their etiologies.The t-test and ANOVA were employed(P<0.05)to compare the age differences of MH different etiological groups.Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed.RESULTS Among the 357 yielded publications,294 MH cases were collected.The average age of MH cases was 67.9 years,with a predominance of females(66.8%females vs 33.2%males).MH was classified into eight groups based on their etiological mechanisms.Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses.CONCLUSION We carried out a more comprehensive review of MH studies.For the first time according to our knowledge,we demonstrated the psychiatric conditions linked and/or associated with MH from statistical,biological and molecular point of view.