基于盲源分离的运动想象脑电信号特征提取方法的研究

运动想象脑电信号被广泛应用于脑机接口系统中。针对如何准确有效地提取运动想象脑电信号特征的问题，通过分析运动想象脑电信号时域、频域和头皮空间域的特征，提出了以小波变换为预处理，并利用二阶盲辨识算法和信息论特征提取算法相结合获取的空间滤波器，从时域、频域和头皮空间域对运动想象脑电信号进行特征提取的方法。实验结果表明，采用时域、频域和空间域提取特征的方法性能有明显提高，并且将二阶盲辨识算法和信息论特征提取算法相结合获取的空间滤波器能够反映更真实的大脑源活动。

多谐波互信息特征的自适应Prony提取方法

针对电力系统中多谐波源相互影响的谐波具有非持续性和突变性的特征,传统的prony算法具有误差较大、精度不高的缺点以及谐波信号采样值具有高维数据的特性,提出自适应Prony方法提取多谐波相互影响的信号互信息特征,通过方差修正量调整互信息提取的精度;对采样数据矩阵进行奇异值分解,秩后的矩阵中的元素值即为多谐波源互信息提取的数据。研究结果表明:该方法实现了多谐波源互信息特征的提取和谐波频谱的分析且精度较高,验证了该方法的正确性。

有色金属交易信息特定属性挖掘算法研究

有色金属交易网络系统中,需要对交易信息的特定属性信息进行准确挖掘,从而提取有用的信息特征,促进网络安全和管理效率。提出一种基于语义文本信息融合的有色金属交易信息特定属性挖掘算法。对交易信息的特定属性数据集进行特征空间重构,在重构的特征空间中进行语义文本信息融合,实现特征数据属性特征提取和准确挖掘。仿真结果表明,该挖掘算法效率较高,准确度较好。

电力系统中多谐波源的谐波分析方法

针对配电网谐波具有多样性、复杂性、非线性和突变性的特征,提出基于奇异值分解与快速独立分量分析的谐波互信息特征提取方法。该方法首先利用快速独立分量分析算法对谐波信号中各个频率成分进行分离提取并借助最小二乘法对谐波信号进行幅值和相位估计,然后通过采用互信息理论策略来保留谐波特征信息的表达和判别能力。通过MATLAB软件进行仿真实验,验证该互信息提取方法的可行性和有效性。

基于Duffing系统的信号非线性检测方法在转子碰摩故障检测中的应用

提出了一种基于Duffing系统的微弱信号非线性检测方法,与传统的基于随机共振的检测方法相比,该方法只需调节一个系统参数,就能够显著提高系统的辨识度,拓展了信号检测范围。将该方法运用于转子系统碰摩故障的早期检测,操作简便,易于实现,能够从较为复杂的背景噪声中得到转子碰摩故障的信息。

基于深度学习的复杂环境下车型精确识别方法

目前在输电线路通道监控中车辆的识别主要是依据车型信息来进行的,这就需要提取大量的特征信息,针对这一复杂问题,提出了深度学习的方法来实现复杂情况下车型的识别方法.该种方法主要是基于卷积神经网路的深度学习,通过实验验证了DCNN网络结构模型在识别车型上的准确率达到96.8%,同时还验证了神经网络中卷积核的大小、神经网络的层数以及特征空间的维数在识别过程中对其性能和分辨能力的影响程度.

Case study on the extraction of land cover information from the SAR image of a coal mining area

In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

基于大数据技术的网络资源管理系统设计

针对当前网络资源数据包转发准确性较低、转发效率较差,网络资源管理水平较差的问题,提出基于大数据技术的网络资源管理系统设计。建立网络资源管理信息化大数据分析模型,采用大数据融合技术进行网络资源管理的信息化调度,采用模糊信息特征提取网络资源管理的关联信息,通过数据挖掘结果重构网络资源管理信息化评估数据的频繁项集关联规则,提取网络资源管理信息化水平特征分布集。采用模糊关联特征检测方法进行网络资源信息分布式检测,结合嵌入式的B/S构架方法进行网络资源管理系统设计。仿真结果表明,采用该方法进行网络资源数据包转发的准确性较好、效率较高,具有一定有效性。

基于图像颜色信息的C-FAST特征检测和匹配算法

以效率较高的加速分割测试特征提取(FAST)算法为基础,添加原FAST算法不具备的尺度不变性和旋转不变性特征描述子,在特征检测和匹配时将颜色信息作为重要参考变量,提出了一种基于颜色信息改进FAST算法的图像特征检测和匹配算法(C-FAST)。改进后的算法效率较高,具有更高的检测和匹配精度,且在光照变化和噪声下均有很好的稳健性。使用公开数据集和常用图像对FAST算法、快速稳健特征(SURF)算法、基于颜色信息的尺度不变特征转换(CSIFT)算法及所提C-FAST算法进行了性能分析。结果表明,所提算法能有效可靠地完成图像的特征检测和匹配,对比原FAST算法,准确率提升30%。

Modification of evidence theory based on feature extraction

Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict information exists. Many modification methods have been developed which can be classified into the following two kinds of ideas, either modifying the combination rules or modifying the evidence sources. In order to make the modification more reasonable and more effective, this paper gives a thorough analysis of some typical existing modification methods firstly, and then extracts the intrinsic feature of the evidence sources by using evidence distance theory. Based on the extracted features, two modified plans of evidence theory according to the corresponding modification ideas have been proposed. The results of numerical examples prove the good performance of the plans when combining evidence sources with high conflict information.

No-Reference Quality Assessment of Enhanced Images

Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.

Introducing semantic information into motion graph

To improve motion graph based motion synthesis,semantic control was introduced.Hybrid motion features including both numerical and user-defined semantic relational features were extracted to encode the characteristic aspects contained in the character's poses of the given motion sequences.Motion templates were then automatically derived from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions.The data streams of motion documents were automatically annotated with semantic motion class labels by matching their respective motion class templates.Finally,the semantic control was introduced into motion graph based human motion synthesis.Experiments of motion synthesis demonstrate the effectiveness of the approach which enables users higher level of semantically intuitive control and high quality in human motion synthesis from motion capture database.

基于小波消噪的EMD模型在GPS振动信号处理中的应用

经验模式分解和小波分解是当前有效处理非平稳信号的两种时频分析方法,它们各具有其优缺点,适用于不同的应用。从对GPS振动信号的预处理、时域和频域处理入手,结果分析表明,对GPS振动信号先进行小波滤波消除随机噪声的干扰,再应用经验模式分解更有利于变形特征信息的分离和提取,提取的信号与振动平台记录数据更加吻合。

RGB-D语义分割:深度信息的选择使用

目的 在室内场景语义分割任务中,深度信息会在一定程度上提高分割精度。但是如何更有效地利用深度信息仍是一个开放性问题。当前方法大都引入全部深度信息,然而将全部深度信息和视觉特征组合在一起可能对模型产生干扰,原因是仅依靠视觉特征网络模型就能区分的不同物体,在引入深度信息后可能产生错误判断。此外,卷积核固有的几何结构限制了卷积神经网络的建模能力,可变形卷积(deformable convolution, DC)在一定程度上缓解了这个问题。但是可变形卷积中产生位置偏移的视觉特征空间深度信息相对不足,限制了进一步发展。基于上述问题,本文提出一种深度信息引导的特征提取(depth guided feature extraction, DFE)模块。方法 深度信息引导的特征提取模块包括深度信息引导的特征选择模块(depth guided feature selection, DFS)和深度信息嵌入的可变形卷积模块(depth embedded deformable convolution, DDC)。DFS可以筛选出关键的深度信息,自适应地调整深度信息引入视觉特征的比例,在网络模型需要时将深度信息嵌入视觉特征。DDC在额外深度信息的引入下,增强了可变形卷积的特征提取能力,可以根据物体形状提取更相关的特征。结果 为了验证方法的有效性,在NYUv2(New York University Depth Dataset V2)数据集上进行一系列消融实验并与当前最好的方法进行比较,使用平均交并比(mean intersection over union, mIoU)和平均像素准确率(pixel accuracy, PA)作为度量标准。结果显示,在NYUv2数据集上,本文方法的mIoU和PA分别为51.9%和77.6%,实现了较好的分割效果。结论 本文提出的深度信息引导的特征提取模块,可以自适应地调整深度信息嵌入视觉特征的程度,更加合理地利用深度信息,且在深度信息的作用下提高可变形卷积的特征提取能力。此外,本文提出的深度信息引导的特征提取模块可以比较方便地嵌入当下流行的特征提取网络中,提高网络的建模能力。