共查询到18条相似文献，搜索用时 187 毫秒
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不同舰船目标辐射噪声的噪声平均功率谱具有差异性特征,能在一定程度上反映舰船目标的吨位、航速、类型等。研究舰船辐射噪声信号的特征提取和频谱分解方法,对提高舰船目标的识别能力具有重要意义。传统的舰船辐射噪声关联特征提取采用的是基于定量递归分析的关联维特征提取方法,当在关联特征提取中舰船辐射噪声信号出现奇异吸引子特征时,提取的舰船目标特征产生混迭谱,导致频谱畸变,影响目标识别性能。针对这一问题,提出一种基于预畸变趋化关联特征提取的舰船噪声信号混迭谱分解方法,设计舰船辐射噪声产生与信号源系统模型,进行声传感器布置研究,进行特征提取和混迭谱分解算法改进分析。仿真实验得出,采用该方法进行舰船辐射噪声信号的预畸变趋化关联特征提取,能有效展示舰船辐射噪声信号的内部规律特征,提高对舰船辐射噪声信号的特征提取性能和目标识别精度。 相似文献
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随机信号在理论上分为稳态信号和非稳态信号两大类,稳态信号是指统计特性随时间变不变化的随机信号,反之,非稳态信号则是指统计特性随时间变化的随机信号,其包含丰富地待测目标地信息量。如何准确获取舰船非稳态噪声特征至关重要,常规的稳态信号处理方法显然不适用,因此,本文基于短时Fourier变换理论,开展舰船非稳态噪声特征提取方法研究,仿真分析结果表明,该方法可以提取出非稳态噪声特征;通过实船非稳态噪声试验数据分析表明,该方法可以快捷、有效地给出舰船非稳态噪声地特征及发生时间。 相似文献
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《科技通报》2016,(5)
通过对网络病毒的动态交互约束抑制设计,实现对病毒入侵特征的有效识别。传统方法采用模糊网络入侵状态特征向量分解方法实现病毒约束抑制,当病毒入侵为一种非平稳随机信息矢量时,对其识别性能不好。提出一种基于互信息特征提取的网络病毒动态交互约束算法。构建网络病毒入侵的信号分析模型,并进行数据采集,采用数模转换方法进行病毒数据离散采样转换,采用重采样和机器学习结合方法,进行了链路漏洞检测,填补了Web防火墙的漏洞,采用三次B样条小波进行互信息特征提取的结果是渐近最优的,利用互信息特征作为检测系统的输入,进行病毒数据提纯处理,基于平均互信息特征提取算法实现特征建模和提取,实现病毒动态交互约束。仿真结果表明,该算法能使得病毒数据在时频空间上得到较明显的聚焦,频谱峰值突出,提高了病毒特征有效识别率。 相似文献
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入侵检测数据集中含有大量高维数据和冗余信息,降低了数据挖掘过程的系统学习效率与响应速度.提出一种基于主成分分析和模糊聚类相结合的入侵检测方法PCA-FC,通过对高维数据的预处理及特征提取,减少样本数据维数,然后执行数据聚集的方法提取出评价规则,从而有效地减少了训练数据的变量和干扰项,提高了分类效率. 相似文献
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《科技通报》2015,(10)
在模糊云计算环境下,需要对特定数据进行识别定位,实现目标数据信号的准确检测和访问。传统方法采用先分层后分支的数据目标资源识别定位算法,定位识别性能计算复杂度较大,准确度不高,提出一种基于通信开销缩减和冗余任务删除的特定数据目标资源识别定位技术。首先对DAG图中的任务进行任务归并,然后将DAG图分层,从整个任务图的全局出发考虑任务的优先级,构建模糊云计算模型,设计模糊云计算核函数,创建多个线程的信息流特征编码,考虑对整个任务图调度时间起决定作用的关键任务,设计通信开销缩减算子,将调度列表头结点分配到使其具有最小最早完成时间的处理器内核上,提高对特定数据的目标资源定位识别性能。仿真实验得出,该算法定位精度较高,对目标资源的冗余任务进行有效删除,明显提高了任务调度效率,收敛性能较好。 相似文献
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本文提出了-种基于模糊聚类的手写数字识别方法,该方法先对手写数字图像进行预处理,然后对数字图像进行特征提取,得到能反映数字样本的低维特征,最后用模糊聚类的方法识别手写数字,Matlab仿真实验表明,该方法运算速度快,识别率较高. 相似文献
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目标噪声特征提取是被动声纳目标识别系统的关键技术。首先提出了一种利用从噪声极限环中提取的非线性特征来分析舰船噪声信号的新方法,然后采用基于自适应遗传BP算法的神经网络对提取的特征进行分类。实验结果表明,该系统具有较好的分类效果。 相似文献
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Web页面包含复杂的、无结构的、动态的数据信息,包含大量的、不完全的、有噪声的、模糊的、随机的数据,干扰了正常的提取过程.为此提出一种改进Apriori算法的海量Web数据高效挖掘方法.在自然连接产生候选集以前先进行一个修剪过程,减少参加连接的项集数量,因而减小生成的候选项集规模,减少了循环迭代次数和运行时间,同时在连接判断步骤中减少多余的判断次数.实验表明,该方法能够迅速排除冗余数据干扰,提高了挖掘的准确性. 相似文献
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《Information processing & management》2023,60(3):103243
The detection and identification of traffic signs is a fundamental function of an intelligent transportation system. The extraction or identification of a road sign poses the same problems as object identification in natural contexts: conditions of illumination are variable and uncontrollable, and various objects frequently surround road signs. These difficulties make the extraction of features difficult. The fusion of time and space features of traffic signs is important for improving the performance of sign recognition. Deep learning-based algorithms are time-consuming to train based on a large amount of data. They are difficult to deploy on resource-constrained portable devices and conduct sign detection in real time. The accuracy of sign detection should be further improved, which is related to the safety of traffic participants. To improve the accuracy of feature extraction and classification of traffic signs, we propose MKL-SING, a hybrid approach based on multi-kernel support vector machine (MKL-SVM) for public transportation SIGN recognition. It contains three main components: a principal component analysis for image dimension reduction, a fused feature extractor, and a multi-kernel SVM-based classifier. The fused feature extractor extracts and fuses the time and space features of traffic signs. The multi-kernel SVM then classifies the traffic signs based on the fused features. Different kernel functions in the multi-kernel SVM are fused based on a feature weighting procedure. Compared with single-core SVM, multi-kernel SVM can better process massive data because it can project each kernel function into high-dimensional feature space to get global solutions. Finally, the performance of SVM-TSR is validated based on three traffic sign datasets. Experiment results show that SVM-TSR performs better than state-of-the-art methods in terms of dynamic traffic sign identification and recognition. 相似文献
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Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments 总被引：1，自引：0，他引：1
In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the target's body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time-frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems. 相似文献
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针对传统考生身份认证方法的缺陷,提出一种基于人脸识别的考生身份认证系统。首先利用图像采集系统采集考生人脸图像,然后对人脸图像进行特征提取和特征选择,并将人脸特征输入到人脸特征库进行匹配,最后采用支持向量机算法对人脸进行分类识别。实验结果表明,该系统提高了考生身份识别的正确率,减少了识别时间,能够很好满足实际考试的要求。 相似文献
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This paper proposes an online video-based approach to handwritten Arabic alphabet recognition. Various temporal and spatial feature extraction techniques are introduced. The motion information of the hand movement is projected onto two static accumulated difference images according to the motion directionality. The temporal analysis is followed by two-dimensional discrete cosine transform and Zonal coding or Radon transformation and low pass filtering. The resulting feature vectors are time-independent thus can be classified by a simple classification technique such as K Nearest Neighbor (KNN). The solution is further enhanced by introducing the notion of superclasses where similar classes are grouped together for the purpose of multiresolutional classification. Experimental results indicate an impressive 99% recognition rate on user-dependant mode. To validate the proposed technique, we have conducted a series of experiments using Hidden Markov models (HMM), which is the classical way of classifying data with temporal dependencies. Experimental results revealed that the proposed feature extraction scheme combined with simple KNN yields superior results to those obtained by the classical HMM-based scheme. 相似文献
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指纹识别技术是当今应用最广泛的生物识别技术之一。在指纹识别过程中,图像处理、特征提取、匹配等过程数据量庞大,计算比较烦琐。BP神经网络具有良好的自学习能力、强大的分类能力和容错能力,将其应用到指纹识别中是可行的。为改进BP神经网络计算速度较慢,梯度下降法不能处理一些不可微传递函数的问题,采用粒子群算法对BP算法进行优化,提高了指纹识别的速度和准确度。 相似文献
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Using an acoustic vector sensor (AVS), an efficient method has been presented recently for direction of arrival (DOA) estimation of multiple speech sources via the clustering of the inter-sensor data ratio (AVS-ISDR). Through extensive experiments on simulated and recorded data, we observed that the performance of the AVS-DOA method is largely dependent on the reliable extraction of the target speech dominated time–frequency points (TD-TFPs) which, however, may be degraded with the increase in the level of additive noise and room reverberation in the background. In this paper, inspired by the great success of deep learning in speech recognition, we design two new soft mask learners, namely deep neural network (DNN) and DNN cascaded with a support vector machine (DNN-SVM), for multi-source DOA estimation, where a novel feature, namely, the tandem local spectrogram block (TLSB) is used as the input to the system. Using our proposed soft mask learners, the TD-TFPs can be accurately extracted under different noisy and reverberant conditions. Additionally, the generated soft masks can be used to calculate the weighted centers of the ISDR-clusters for better DOA estimation as compared to the original center used in our previously proposed AVS-ISDR. Extensive experiments on simulated and recorded data have been presented to show the improved performance of our proposed methods over two baseline AVS-DOA methods in presence of noise and reverberation. 相似文献
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《Information processing & management》2022,59(2):102840
Scene segmentation is a very challenging task where convolutional neural networks are used in this field and have achieved very good results. Current scene segmentation methods often ignore the internal consistency of the target object, and lack to make full use of global and local context information which leads to the situation of object misclassification. In addition, most of the previous work focused on the segmentation of the main part of the object, however, there are few researches on the quality of the object edge segmentation. In this article, based on the use of flow information to maintain body consistency, the context feature extraction module is designed to fully consider the global and local body context information of the target object, refining the rough feature map in the intermediate stage. So, the misclassification of the target object is reduced. Besides, in the proposed edge attention module, the low-level feature map guided by the global feature and the edge feature map with semantic information obtained by intermediate process are connected to obtain more accurate edge detail information. Finally, the segmentation quality that contains the body part of the noise and the edge details can be improved. This paper not only conducts experiments on the classic FCN, PSPNet, and DeepLabv3+ several mainstream network architectures, but also on the real-time SFNet network structure proposed last year, and the value of mIoU in object and boundary is improved to verify the effectiveness of the method proposed in this paper. Moreover, in order to prove the robustness of the experiment, we conduct experiments on three complex scene segmentation data sets of Cityscapes, CamVid, and KiTTi, and obtained mIoU values of 80.52% on the Cityscapes validation data set, and 71.4%, 56.53% on the Camvid and KITTI test data set, which shows better results when compared with most of the state-of-the-art methods. 相似文献