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。

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。

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。

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。

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。

The Role of Music Therapy in Supporting Intellectually Disabled Youth in Senegal

Introduction: Music therapy is a practice for helping and supporting people with intellectual and relational difficulties. This study illustrated the benefits of music therapy for young people living with intellectual disabilities (YLID) in an African context. Methodology: This study investigated six young individuals with intellectual disabilities who had undergone three years of music therapy. They were participants in the inclusive non-academic training program at the National School of Arts in Dakar from 2017 to 2019. Data collection utilized individual interviews with the youths, evaluation grids from teachers and psychiatrists. Guardians provided informed consent along with the assent of the young participants. Results: The six young were aged between 18 and 30 years old, with an average age of 24.6 years. Four of the YLID were male. Three young people with intellectual disabilities had delayed psychomotor development. Observations revealed the beneficial influence of music therapy on the health and well-being of young individuals. Music played a role in alleviating stress and anxiety among youth with intellectual disabilities (YLID), enhancing their mood and mental health. It assisted in navigating challenging situations and heightened alertness among YLID. Additionally, music therapy contributed to improvements in dyslexia, fine and gross motor skills, and memory development among intellectually disabled youth, ultimately facilitating their integration into society. Conclusion: In light of our results, music therapy makes a major contribution to the empowerment of YLID. Engaging in musical activities helps young people connect with others through instrumental expression and a sense of accomplishment. By facilitating music therapy, it becomes possible to combat discrimination and stigmatization, thus promoting the social inclusion of intellectually disabled youth. Therefore, it is important to promote music therapy in Senegal to meet the needs of YLID.

Effectiveness of Music-Based Therapeutic Intervention on People with Dementia: A Rapid Review

Background: Dementia is a condition with progressive cognitive dysfunction and manifestation of both behavioral and psychosocial symptoms. Non-pharmacological measures such as music therapy are gaining importance since efficacy and safety of people with dementia have been questionable for pharmacological measures. Patient’s response to music is persistent even in the later stage of dementia. Aim: This rapid review aims to identify, analyze, evaluate, and summarize the best available evidence on the effectiveness of music-based therapeutic interventions among people with dementia. Method: CINAHL Cochrane Library, internet websites of rapid review producers, and reference lists were searched to identify articles for inclusion. Two reviewers independently screened the literature search results. Effectiveness, music-based therapeutic intervention, dementia, Alzheimer’s disease, systematic review and systematic review with meta-analysis terms were used to abstract data from included studies. Main Findings: 11 SRs and SRs with meta-analysis were reviewed which revealed positive effect of music therapy on five major outcomes with 9 studies effect on behavioral outcome, 6 studies with positive effect on psychosocial outcome reducing anxiety, 6 with improved cognition, 1 study revealed with improved quality of life and 1 study revealed effect on physiological outcomes. Conclusion: Music therapy has positive effect on treatment of dementia but further studies with larger sample size and specified to single intervention should be conducted to provide generalisable and precise results on this topic.

Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces.Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs.However,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise.It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization.Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation structure.Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird sounds.Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct.By clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

Experimental and numerical investigations on acoustic damping of monoclinic crystalline wideband sound absorbing structures

In order to overcome the limitations of traditional microperforated plate with narrow sound absorption bandwidth and a single structure,two multi-cavity composite sound-absorbing materials were designed based on the shape of monoclinic crystals:uniaxial oblique structure(UOS)and biaxial oblique structure(BOS).Through finite element simulation and experimental research,the theoretical models of UOS and BOS were verified,and their sound absorption mechanisms were revealed.At the same time,the influence of multi-cavity composites on sound absorption performance was analyzed based on the theoretical model,and the influence of structural parameters on sound absorption performance was discussed.The research results show that,in the range of 100-2000 Hz,UOS has three sound absorption peaks and BOS has five sound absorption peaks.The frequency range of the half-absorption bandwidth(α>0.5)of UOS and BOS increases by 242% and 229%,respectively.Compared with traditional microperforated sound-absorbing structures,the series and parallel hybrid methods significantly increase the sound-absorbing bandwidth of the sound-absorbing structure.This research has guiding significance for noise control and has broad application prospects in the fields of transportation,construction,and mechanical design.

ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

Experimental realization of fractal fretwork metasurface for sound anomalous modulation

Natural creatures and ancient cultures are full of potential sources to provide inspiration for applied sciences.Inspired by the fractal geometry in nature and the fretwork frame in ancient culture,here we design the acoustic metasurface to realize sound anomalous modulation,which manifests itself as an incident-dependent propagation behavior:sound wave propagating in the forward direction is allowed to transmit with high efficiency while in the backward direction is obviously suppressed.We quantitatively investigate the dependences of asymmetric transmission on the propagation direction,incident angle and operating frequency by calculating sound transmittance and energy contrast.This compact fractal fretwork metasurface for acoustic anomalous modulation would promote the development of integrated acoustic devices and expand versatile applications in acoustic communication and information encryption.

Characterization of Aquifers in Crystalline and Crystallophyll Basement Zones Using the Electrical Resistivity Method (Trails and Electrical Soundings) in the Gagnoa Region, (Central-Western Côte d’Ivoire)

Introduction: Located in the central-western part of Côte d’Ivoire, the subsoil of the Gagnoa region is made up of sedimentary volcano formations and granitoids with developed fracturing. This complex Precambrian basement contains most of the region’s water resources. This is at the origin of the high failure rate during the various hydrogeological prospecting campaigns. Methodology: The database consists of resistivities from 42 holes and 51 trails drilled as part of the implementation of high-throughput drilling in the study area. The objective of this study is to deepen the knowledge of the fissured basement by interpreting profile curves and electrical soundings. It will be a question of classifying the different types of anomalies obtained on the profiles and their shapes. The orientation of the lineaments observed on the profiles was determined. Results: The interpretation of the geophysical data revealed various anomalies, the main ones being of the CC (Conductor Compartment) and CEDP (Contact between two bearings) types. These types of anomalies are mainly expressed in various forms: the “V”, “W” and “U” shapes. From these anomalies and the appearance of the electrical profiles, lineaments and their orientations were identified with N90-100, N130-140, N170-180 as major orientations. Conclusion: These results could contribute to a better understanding of the fractured environment of the Gagnoa region.

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。

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。

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Train-induced aerodynamic characteristics of vertical sound barriers influenced by several factors

Investigations into the aerodynamic properties of vertical sound barriers exposed to high-speed operations employ computational fluid dynamics.The primary focus of this research is to evaluate the influence of train speed and the distance(D)from the track centerline under various operating conditions.The findings elucidate a marked elevation in the aerodynamic effect amplitude on sound barriers as train speeds increase.In single-train passages,the aerodynamic effect amplitude manifests a direct relationship with the square of the train speed.When two trains pass each other,the aerodynamic amplitude intensifies due to an additional aerodynamic increment on the sound barrier.This increment exhibits an approximate quadratic correlation with the retrograde train speed.Notably,the impact of high-speed trains on sound barrier aerodynamics surpasses that of low-speed trains,and this discrepancy amplifies with larger speed differentials between trains.Moreover,the train-induced aerodynamic effect diminishes significantly with greater distance(D),with occurrences of pressure coefficient(CP)exceeding the standard thresholds during dual-train passages.This study culminates in the formulation of universal equations for quantifying the influence of train speed and distance(D)on sound barrier aerodynamic characteristics across various operational scenarios.

The influence of reflections from the train body and the ground on the sound radiation from a railway rail

Purpose – The vibration of the rails is a significant source of railway rolling noise, often forming the dominantcomponent of noise in the important frequency region between 400 and 2000 Hz. The purpose of the paper is toinvestigate the influence of the ground profile and the presence of the train body on the sound radiation fromthe rail.Design/methodology/approach – Two-dimensional boundary element calculations are used, in which therail vibration is the source. The ground profile and various different shapes of train body are introduced in themodel, and results are observed in terms of sound power and sound pressure. Comparisons are also made withvibro-acoustic measurements performed with and without a train present.Findings – The sound radiated by the rail in the absence of the train body is strongly attenuated by shieldingdue to the ballast shoulder. When the train body is present, the sound from the vertical rail motion is reflectedback down toward the track where it is partly absorbed by the ballast. Nevertheless, the sound pressure at thetrackside is increased by typically 0–5 dB. For the lateral vibration of the rail, the effects are much smaller. Oncethe sound power is known, the sound pressure with the train present can be approximated reasonably well withsimple line source directivities.Originality/value – Numerical models used to predict the sound radiation from railway rails have generallyneglected the influence of the ground profile and reflections from the underside of the train body on the soundpower and directivity of the rail. These effects are studied in a systematic way including comparisons with measurements.

An improved algorithm based on equivalent sound velocity profile method at large incident angle

With the development of ultra-wide coverage technology,multibeam echo-sounder(MBES)system has put forward higher requirements for localization accuracy and computational efficiency of ray tracing method.The classical equivalent sound speed profile(ESSP)method replaces the measured sound velocity profile(SVP)with a simple constant gradient SVP,reducing the computational workload of beam positioning.However,in deep-sea environment,the depth measurement error of this method rapidly increases from the central beam to the edge beam.By analyzing the positioning error of the ESSP method at edge beam,it is discovered that the positioning error increases monotonically with the incident angle,and the relationship between them could be expressed by polynomial function.Therefore,an error correction algorithm based on polynomial fitting is obtained.The simulation experiment conducted on an inclined seafloor shows that the proposed algorithm exhibits comparable efficiency to the original ESSP method,while significantly improving bathymetry accuracy by nearly eight times in the edge beam.

Sound event localization and detection based on deep learning

Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.