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 Genre Classification Using DenseNet and Data Augmentation

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone huge changes.As the number ofmusic products increases daily and themusic genres are extremely rich,storing,classifying,and searching these works manually becomes difficult,if not impossible.Automatic classification ofmusical genres will contribute to making this possible.The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive(FMA)data set.For Small FMA,it is more efficient to augment the data by generating an echo rather than pitch shifting.The research results show that the DenseNet121 model and data augmentation methods,such as noise addition and echo generation,have a classification accuracy of 98.97%for the Small FMA data set,while this data set lowered the sampling frequency to 16000 Hz.The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.

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。

Intrahepatic portal venous systems in adult patients with cavernous transformation of portal vein: Imaging features and a new classification

Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to investigate the imaging features of intrahepatic portal vein in adult patients with CTPV and establish the relationship between the manifestations of intrahepatic portal vein and the progression of CTPV. Methods: We retrospectively analyzed 14 CTPV patients in Beijing Tsinghua Changgung Hospital. All patients underwent both direct portal venography(DPV) and computed tomography angiography(CTA) to reveal the manifestations of the portal venous system. The vessels measured included the left portal vein(LPV), right portal vein(RPV), main portal vein(MPV) and the portal vein bifurcation(PVB). Results: Nine males and 5 females, with a median age of 40.5 years, were included in the study. No significant difference was found in the diameters of the LPV or RPV measured by DPV and CTA. The visualization in terms of LPV, RPV and PVB measured by DPV was higher than that by CTA. There was a significant association between LPV/RPV and PVB/MPV in term of visibility revealed with DPV( P = 0.01), while this association was not observed with CTA. According to the imaging features of the portal vein measured by DPV, CTPV was classified into three categories to facilitate the diagnosis and treatment. Conclusions: DPV was more accurate than CTA for revealing the course of the intrahepatic portal vein in patients with CTPV. The classification of CTPV, that originated from the imaging features of the portal vein revealed by DPV, may provide a new perspective for the diagnosis and treatment of CTPV.

Music Genre Classification Using African Buffalo Optimization

In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.

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。

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。

Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.

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.

Classification of Sailboat Tell Tail Based on Deep Learning

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect.Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual fatigue.In this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease.This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction performance.Considering the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM classifier.All experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the classifier.The remaining one group was used as the test set for testing.The highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments.The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

Comprehensive understanding of glioblastoma molecular phenotypes:classification,characteristics,and transition

Among central nervous system-associated malignancies,glioblastoma(GBM)is the most common and has the highest mortality rate.The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide.In precision medicine,research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity,as well as the refractory nature of GBM toward therapy.Deep understanding of the different molecular expression patterns of GBM subtypes is critical.Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes.The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors.These subtypes also exhibit high plasticity in their regulatory pathways,oncogene expression,tumor microenvironment alterations,and differential responses to standard therapy.Herein,we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype.Furthermore,we review the mesenchymal transition mechanisms of GBM under various regulators.

Curve Classification Based onMean-Variance Feature Weighting and Its Application

The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

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。

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.