基于数据融合和小波分析的结构损伤诊断

基于数据融合和小波分析理论，提出一种新的结构损伤诊断方法。采用改进的一致性算法融合多传感器的测量数据，克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点，对支持矩阵进行模糊化处理，避免了人为定义阈值而产生的主观误差。利用小波分析的降噪和多尺度分辨能力对多传感器的数据进行分析处理，从而对结构损伤作出诊断识别。通过数值算例，验证了该方法可以充分利用所有传感器的有效信息，能够在部分传感器性能降低（如受到噪声影响），甚至是完全失效的情况下，对结构损伤作出正确诊断。     

采用多传感器数据融合技术的消防报警系统

介绍了多传感器数据融合消防报警系统，然后在分析信号特征和各种算法的基础上提出数据融合的思想。根据多传感器的不同性质，进行多级别、多方面处理，以求更可靠更准确的结果。并提出了基于传感器数据融合的火灾报警的基本框架，最后讨论了传感器数据融合的方法和管理实施。     

复杂系统多传感器数据融合技术及应用研究

复杂系统多传感器数据融合是一门新兴的技术,它通过对来自多个传感器的数据进行多级别、多方面、多层次的处理从而产生出单个传感器所不能获得的更有意义的信息。数据融合在军事领域和民用领域都有很大的发展和应用前景。本文论述了复杂系统多传感器数据融合的基本原理、功能模型、层次结构、融合方法等,并指出了多源传感器数据融合研究中存在的问题及研究方向。最后,分析了多平台多传感器数据融合在某海军舰艇平台上的一个应用实例。     

贝叶斯估计动态汽车衡分时段数据融合研究

数据融合是一个多级、多层面的数据处理过程,主要完成对来自多个信息源的数据进行自动检测、关联、相关、估计及组合等处理。该文通过分析动态汽车衡的称重原理,对两路称重传感器不同时段的输出数据进行分析,提出基于贝叶斯估计的数据融合方法。实验检定表明:采用这种融合方法的动态汽车衡称重误差小,克服了动态汽车衡由于车辆振动、路面不平和传感器灵敏度分散性、传感器线性度误差等因素对称量结果的影响,准确度高。     

多传感器数据融合研究

数据融合技术又称多传感器数据融合或分布式传感,即对多类多源和多平台传感器数据进行组合,提供有关空间信息综合态势的一种数据处理技术。数据融合可分为:信号级数据融合、像素级数据融合、特征级数据融合和符号级数据融合。本文讨论了红外图像、可见光图像、多谱图像、雷达图像等的数据融合问题以及各级数据融合的方法。     

数据融合在压力检测中的应用

论述了提高压力检测精度的多传感器数据融合方法.该方法利用RBF神经网络进行异类传感器融合,消除了环境温度和工作电压波动对单个压力传感器测量的影响;利用加权融合估计算法,对同类压力传感器进行融合,提高了压力测量的精度,实现了液压系统压力的动态测量.     

多传感器航迹数据融合的机载火控系统

为研究机载火控系统多传感器数据融合的方法。采用了基于多目标多传感器航迹数据融合的改进的卡尔曼滤波算法，运用假设检验理论，充分考虑了位置数据互联的正确性和目标信号的历史信息，使其性能不随目标数目的增大而变差。计算机仿真表明：所建立的系统操作方便，运行可靠，性能价格比高；所采用的算法具有处理速度快、精度高等优点。     

基于灰色关联度的多传感器数据融合方法研究

针对多个传感器对某一特性指标多次测量的数据融合问题,提出一种基于灰色关联度的多传感器数据融合新方法。该方法将各传感器测得的数据视为一个行为序列,利用灰色关联度对不同传感器测得数据之间的接近程度进行度量,并通过灰色关联矩阵全面衡量数据间的综合接近程度,然后根据非负对称矩阵的性质求得各传感器测得数据在数据融合表达式中的权重,从而实现多传感器数据的融合。仿真结果表明应用所提出方法对雷达数据进行处理,可有效降低跟踪误差,提高测量精度。     

灰色关联和D-S证据理论在变速箱齿轮故障诊断中的应用

利用信息融合方法进行故障诊断是当前故障诊断的趋势.本文针对变速箱齿轮的状态特点,提出了利用灰色关联和D-S证据理论进行诊断的方法并用实际数据加以验证.首先对每个振动传感器各个方向上的信息分别用灰色关联分析得到故障分类,将多个传感器各个振动方向的分类的结果利用D-S证据理论融合做出故障决策,计算结果表明融合结果比单一传感器的诊断结果可信度得到提高.     

基于模糊数据融合的健康评价方法

研究基于多传感器信号的船舶用柴油机冷却系统的健康状况评价问题, 提出了船舶用柴油机冷却系统健康状况的定量综合评价指标--健康度, 将基于模糊集的数据融合方法用于健康度的计算, 该方法利用层次分析法来确定各个传感器监测参数的重要程度系数, 利用模糊综合评判对多传感器数据进行融合计算得到系统的健康度, 以此为基础, 建立了船舶用柴油机冷却系统的健康评价体系, 给出了系统的健康状况等级. 实验结果表明该方法能够对其健康状况进行定量的、合理的评价.     

A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data

Traditional distributed denial of service (DDoS) detection methods need a lot of computing resource, and many of them which are based on single element have high missing rate and false alarm rate. In order to solve the problems, this paper proposes a DDoS attack information fusion method based on CNN for multi-element data. Firstly, according to the distribution, concentration and high traffic abruptness of DDoS attacks, this paper defines six features which are respectively obtained from the elements of source IP address, destination IP address, source port, destination port, packet size and the number of IP packets. Then, we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment. The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features, and obtain multi-element fusion feature (MEFF) value. Finally, the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series. Experimental results show that the information fusion method proposed can effectively fuse multi-element data, reduce the missing rate and total error rate, memory resource consumption, running time, and improve the detection rate.     

基于任务的神经网络多传感器数据融合新方法

面向遥操作机器人系统对传感和控制的具体要求及条件,提出一种基于任务的神经网络多传感器数据融合新方法。根据遥操作过程中的不同任务,利用优化的神经网络算法,对双视觉、六维力／力矩、接近觉（数字／模拟）、指端力、关节角度等多种传感器信息进行融合决策,并实时地将融合结果反馈回上层控制系统。     

Data fusion algorithm for pulsed eddy current detection

A weighted data fusion algorithm based on matching pursuit (MP)-wavelet packet (WP) atomic decomposition and its applications in pulsed eddy current (PEC) non-destructive testing systems for estimation of feature parameters is presented. MP-WP atomic decomposition is used to estimate each noise-free pulse response from its noisy observation of a single-sensor PEC probe and obtain the peak value parameter from each estimated response. A weighted data fusion algorithm, on the basis of minimum mean square error (MMSE), is applied to fuse each obtained peak value together to get final optimum parameter estimation. Based on the difference of each noisy pulse response and its estimation, the variance of noise in each pulse response can be computed, respectively. Accordingly, the weight of each pulse response for data fusion is calculated by the variance of its noise. Finally, the peak value parameter is estimated by the utilised data fusion algorithm. In terms of MMSE, this weighted fusion presents an optimum estimation of the feature parameter of multi-pulse responses of PEC sensor, compared with normal averaging process.     

A quantitative method for evaluating the performances of hyperspectral image fusion

Hyperspectral image fusion is a key technique of hyperspectral data processing. In recent years, many fusion methods have been proposed, but there is little work concerning evaluation of the performances of different image fusion methods. In this paper, a method called quantitative correlation analysis (QCA) is proposed, which provides a quantitative measure of the information transferred by an image fusion technique into the output image. Using the proposed method, the performances of different image fusion methods can be compared and analyzed directly based on the images of before and after performing the fusion. The correlation information entropy, based on the developed QCA, is also proposed and testified by numerical simulations. Typical hyperspectral data are applied to the proposed method. The results show that the method is effective, and its conclusions agree with the classification results in applications.     

区域特征耦合DS证据理论的图像融合算法

目的提高融合图像视觉质量。方法提出区域多特征与改进的DS证据理论规则的聚焦图像融合算法。首先,引入二代Curvelet变换,对源图像进行分解,获取图像的粗尺度系数、细尺度系数;然后,根据区域中粗尺度系数的绝对值,构造最大值融合规则,完成粗尺度系数的融合;再联合区域方差、信息熵以及区域能量等特征,提取细尺度层的区域特征,并通过定义概率约束条件,改进DS证据理论的融合规则,增强DS合成规则的可信度,对图像的细尺度系数进行有效融合,使得融合图像保留更多的细节信息;最后,通过逆Curvelet变换完成图像的融合。结果与当前的图像融合算法相比,在对聚焦图像融合时,文中算法的融合图像具有更丰富的细节信息,其视觉质量更高,且融合时耗较短。结论所提算法考虑了像素之间的互相关性,进一步优化了图像融合质量,可用于遥感探测与包装印刷检测等领域。     

Fusion of MRI and CT images using guided image filter and image statistics

In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.     

Data acquisition, analysis and applications of multi-sensor integration

This paper presents some key techniques for multisensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multisensor, spacetime benchmark for sensor data, and multisensor data fusion and mining. Firstly, synchronization control of multisensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization subcontrollers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send spacetime information to the synchronization subcontroller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, spacetime benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. landborne intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co., Ltd. and has been successfully put into use. The latter is an ongoing research.     

基于小波变换的工程图样融合

对于分块扫描输入的工程图样必须进行拼接处理,以得到一张完整的工程图样。作者介绍了一种图样的融合方法:首先对子图样进行匹配,然后采用二维小波变换对几幅工程子图样进行小波分解,对分解后的图样提取小波系数,进行融合(拼接)处理,得到一幅完全包含子图样信息和内容完整的图样。给出了基于小波变换进行工程图样融合的具体步骤以及应用MATLAB软件进行工程图样融合的过程。     

A Novel Sensing and Data Fusion System for 3-D Arm Motion Tracking in Telerehabilitation

In this paper, we present a novel sensing and data fusion system to track 3-D arm motion in a telerehabilitation program. A particle filter (PF) algorithm is adopted in the system to fuse data from inertial and visual sensors in a probabilistic manner. It is able to propagate multimodal distributions of system states based on an ldquoimportance samplingrdquo technique by using sets of weighted particles. To avoid the problem of conventional PF algorithms that suffer from particle degeneracy and perform poorly in a narrow distribution situation, we adopt two strategies in our system, namely state space pruning and an arm physical geometry constraint. Experimental results show that the proposed PF framework outperforms other fusion methods and provides accurate results in comparison to the ground truth.     

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.