加筋板材料辨识的导纳特征表达与冲击声特征提取

以表征物理属性的导纳特征为中间量,提取与加筋板材料属性有关的冲击声特征。先用相关分析方法获得金属加筋板物理属性的导纳特征表达以及导纳特征与冲击声特征之间的联系,间接得到表征声源物理属性的冲击声特征,然后通过支持向量机分类器验证不同特征在金属加筋板材料分类辨识中的性能。结果表明,所得的4组冲击声特征能准确识别出不同的材料,单个特征的识别率与对应材料属性的可分程度有关,理想冲击声声特征比音色特征的平均识别率更高。由此可见,利用导纳特征提取与材料属性相关冲击声特征的方法是有效的,且所提的特征能够很好的反映声源材料属性。      

基于平板冲击声的声源特性表征及自动识别*

以平板结构导纳函数为纽带,建立冲击声信号特征与声源特性之间的关联,获得与声源属性密切相关的特征用于目标分类。针对四边简支矩形被击板,借助信号参数识别算法获得与声源物理属性有关的6维导纳特征,并从冲击声样本中提取80维音色特征,将音色特征和导纳特征做相关性分析,获得与声源物理属性相关的信号特征集。利用BP神经网络进行分类,结果表明,当采用与特定声源物理属性相关的信号特征子集时,分类效果达到同组最优。     

利用冲击声辨识声源材料的特征提取

声源辨识属于环境声识别的范畴,是模式识别的一个重要研究方向。冲击声携带了大量的声源物理信息,因此利用冲击声提取特征进行声源材料辨识是提高声目标识别分类性能的重要途径。对球-板撞击物理模型合成的冲击声连续统,提出使用基函数学习法提取目标特征,同时利用短时傅里叶变换和小波包变换进行特征提取,以此为基础完成被击平板的材料识别。研究结果表明,利用基函数学习法获得的特征,对于冲击声分类的效果明显优于短时傅里叶变换和小波包变换方法,说明利用该方法进行冲击声声源材料辨识的可行性和优势。      

Feature extraction for sound source material recognition with impact sounds

Sound source recognition is a part of environmental sound recognition,which is one of the most important research areas in pattern recognition.Impact sounds carry much physical information associated with the sound sources,which makes impact sound based sound source recognition an important approach to improve recognition performance.In this study,the impact sound continuum synthesized with a ball-plate collision model is used for material recognition of the impacted plates.The basis function learning method and time-frequency representation methods,including the short time Fourier transform and the wavelet packet transform,are applied into classification and the recognition results are compared.The result shows that the features obtained by using the basis function learning perform better for material classification of the impacted plates than that by using the short time Fourier transform and the wavelet packet transform.This demonstrates the high efficiency and superiority of this method in material recognition of sound sources.     

Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets in different domains. The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.     

近岸4种鱼的声散射特征提取及融合实验研究*

为了有效地提取表征鱼类间差异的声散射特征参数,该文通过绳系法实验研究了近岸4种经济鱼类的声散射信号特征提取及融合方法。首先,通过自研双频鱼探仪采集花鲈、许氏平鲉、黑鲷和斑石鲷的个体鱼声散射信号;然后,分别测定200 kHz和450 kHz换能器下鱼体的目标强度,同时提取鱼声散射信号的时频域统计特征;最后,将降维后的时频特征与频差特征融合组成新的特征向量。该文通过实验验证了该方法的有效性,基于组合特征的支持向量机识别准确率达93%。结果表明,鱼的频率响应特性和鱼声散射信号的时频域统计特征能一定程度上反映鱼的固有属性,有效地增加判别依据能显著提高以上4种鱼类的识别准确率。     

基于特征选择的网络入侵检测模型研究

为了有效从收集的恶意数据中选择特征去分析,保障网络系统的安全与稳定,需要进行网络入侵检测模型研究。但目前方法是采用遗传算法找出网络入侵的特征子集,再利用粒子群算法进行进一步选择,找出最优的特征子集,最后利用极限学习机对网络入侵进行分类,但该方法准确性较低。为此,提出一种基于特征选择的网络入侵检测模型研究方法。该方法首先以增强寻优性能为目标对网络入侵检测进行特征选择,结合分析出的特征选择利用特征属性的Fisher比构造出特征子集的评价函数,然后结合计算出的特征子集评价函数进行支持向量机完成对基于特征选择的网络入侵检测模型研究方法。仿真实验表明,利用支持向量机对网络入侵进行检测能有效地提高入侵检测的速度以及入侵检测的准确性。     

Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.     

一种基于特征点提取的扬声器异常声检测方法

针对短时傅里叶变换在扬声器异常声检测中有效信息提取的随机性问题,提出了特征点法在扬声器异常声检测中的应用.此方法基于扬声器经扫频信号激励所得响应信号的短时傅里叶变换时频图,用改进的尺度不变特征转换算法对合格扬声器与异常声扬声器做特征提取,并将多组特征点经分割剔除后叠加组成特征矩阵模板.以合格扬声器样本提取特征曲线阈值构...     

双层加筋结构有源隔声物理机制研究

深入分析双层加筋结构有源隔声的物理机制有助于控制系统的优化设计。用模态叠加法和声振耦合理论对双层加筋有源隔声结构建模。在辐射加筋板声功率最小的控制条件下,从模态耦合的角度对有源隔声的物理本质进行了详细阐述。分析结果表明,由于双层加筋板中筋的耦合作用影响,系统的声能量传输规律及有源隔声机理与现有的模态分析结论相比均发生改变。结合筋的耦合影响,对空腔声场的模态抑制与重构机理进行修正和补充,清晰解释了双层加筋结构有源隔声的物理本质。