测量噪声相关情况下的多传感器数据融合

对于测量噪声相关的多传感器测量模型,利用Cholesky分解和单位下三角阵的求逆方法,将其转化为测量噪声互不相关的等价的多传感器伪测量模型,然后基于Markov估计,提出了一种测量噪声相关情况下多传感器数据融合的新方法。与直接利用原始传感器测量值的Markov估计数据融合方法相比,两者的计算精度相同,但新方法的计算复杂度却大大降低。数值仿真实验进一步验证了新方法的有效性。     

基于多尺度Kalman数据融合滤波

本文通过分析基于小波变换的动态系统模型,提出一种基于小波多尺度的Kalman数据滤波方法,本文利用小波的多尺度特点,把初始估计序列多尺度分解,并在不同尺度层上进行Kalman滤波估计,再利用小波重构来融合各层的估计信息,把标准Kalman滤波只在单一尺度和时间轴上对状态估计值和误差协方差进行数据更新,改进为基于小波变换的尺度轴和时间轴上的双向数据更新,该算法将小波多尺度分解去噪和Kalman滤波相结合,对实际中含较强噪声的动态系统的状态估计效果较好.算法也可用于多分辨率多传感器数据融合.     

基于模糊贴近度的多传感器数据融合测量

依据现代测量中多传感器数据融合需求,提出了一种基于模糊贴近度的数据融合新方法,研究了它在测量中的应用过程。测量应用实例验证了其在工程中的可行性,体现了稳定性、可靠性高的传感器在测量数据融合中的“优越性”,运算过程简洁、快速、有效,便于实时测量操作。     

基于多传感器数据融合的热处理炉温度测量方法

温度是制约热处理性能的主要指标,对热处理炉温度的准确、可靠测量是目前亟待解决的技术问题。本提出了一种基于多传感器参数估计数据融合的热处理炉温度测度方法,给出数据融合算法。实际应用结果验证了算法的准确性。     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Smart-City-based Data Fusion Algorithm for Internet of Things

Increasingly, Wireless Sensor Networks (WSNs) are contributing enormous amounts of data. Since the recent deployments of wireless sensor networks in Smart City infrastructures, significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation. Using wireless sensor nodes, a Smart City environment may now be shown for the benefit of residents. The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society, including the elderly and others. Weak, Quality of Service (QoS) and poor data performance are common problems in WSNs, caused by the data fusion method, where a small amount of bad data can significantly impact the total fusion outcome. In our proposed research, a WSN multi-sensor data fusion technique employing fuzzy logic for event detection. Using the new proposed Algorithm, sensor nodes will collect less repeated data, and redundant data will be used to increase the data's overall reliability. The network's fusion delay problem is investigated, and a minimum fusion delay approach is provided based on the nodes’ fusion waiting time. The proposed algorithm performs well in fusion, according to the results of the experiment. As a result of these discoveries, It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.     

气门密封面两种熔焊工艺焊层的组织、性能比较

对真空高频熔焊和等离子堆焊两种工艺下成型的汽车发动机Co-Cr-W合金气门密封面焊层进行分析比较.结果表明:二种堆焊工艺合金焊层都与基体形成良好冶金结合,等离子堆焊的细小树枝晶组织比真空高频感应焊层等轴晶组织细密;550 ℃下在RFT-Ⅲ型高温磨损试验机上等离子焊层耐磨性优于真空高频感应焊层;在10%HNO3水溶液20 h腐蚀试验中,等离子焊层的耐腐性能比高频感应焊层好.等离子堆焊比真空高频熔焊更适合汽车发动机气门密封面表面处理.     

结合数据融合和数据挖掘技术的信息智能处理平台

数据融合技术能利用不同传感数据的冗余信息实现互补以提高信息处理的正确性；数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识，用于建模和优化。本文研究结合数据融合和数据挖掘技术的信息智能处理平台，阐述了其功能和组成，知识表达和建模、知识推理和决策，并介绍了其在目标检测识别和医疗监护等领域的应用。     

斯米尔诺夫检验法在数据级信息融合数据关联中的应用

在信息融合系统中，为了确保进入融合中心的信息是满足融合需要的有效信息，在融合之前要对来自多个信息源的数据进行数据关联。作为信息融合前的信号预处理过程，利用数理统计中的斯米尔诺夫检验法对多个同类传感器测得的加速度响应信号进行数据关联，去掉故障传感器或受到严重干扰的通道数据，确保进入融合中心的数据能够有效反映被测对象的状态。通过某二级齿轮箱实验台实测数据的验证，证明该方法能够有效地检验出同类传感器信号是进行故障诊断的有效信号。     

Theory and Implementation of NDT Data Fusion

Abstract

Scientific measurements from single or multiple sensors are usually incomplete and uncertain. A process making use of the concept of data fusion has been developed to try to encompass this problem by combining information from multiple sensors. The objective to synergistic use of information from multiple sources is to reduce uncertainty and increase the confidence level of a measurand. The implementation of data fusion to the field of NDT is relatively new. This paper summarizes the achievements of current research on data fusion applied to NDT. A theoretical data fusion strategy is described and experimental results generated from weld inspection are presented.     

Digital Techniques for Strain Measurement Using Electro-optic Holography

Abstract

Some new approaches to the digital analysis of the fringes obtained in electro-optic holography are presented. The technique of phase stepping supplemented by Fourier filtering, for the elimination of noise in wrapped-phase maps, is discussed first. The problem introduced by discontinuities at the object boundary and a fringe extrapolation technique to overcome this problem are discussed next. Finally, a technique to compute the phase derivatives directly from the wrapped-phase map is outlined.     

CAD图形数据数字水印技术综述

数字水印技术正成为网络环境下CAD图形的版权保护和侵权检测研究的热点。综述了目前国内外针对CAD图形水印技术所做的相关工作,分析了CAD图形在数据表示及使用方式上的特点、CAD图形嵌入数字水印的要求以及CAD图形数据信息冗余的类型;按空域、变换域及参数变换分类方法,概括了目前典型的针对CAD图形数据的数字水印算法,讨论了其优缺点。指出了对CAD图形数字水印技术还存在的问题以及对该领域未来可能的研究方向和研究重点进行了展望。     

移相干涉技术在活塞外径测量中应用

基于传统接触式测长仪器存在不足,通过对长度干涉仪测量原理方法的分析研究,提出采用移相干涉技术进行瞄准定位的方法,并应用在活塞外径测量系统中.通过实验验证:采用双移相干涉测头结构的活塞外径测量系统,对活塞表面瞄准定位的重复性较好,活塞外径系统测量不确定度达到0.1μm,移相干涉在高精度基准活塞外径测量中成功应用,具有较好的推广价值.     

多传感器数据融合测试系统自测方法研究

针对数据融合测试系统在测试中的重要作用,论文研究数据融合测试系统的自测方法.提出一种基于原位自测法的数据融合测试系统自测体系框架结构;给出测试系统的自测控制过程的详细描述,其中包括自测控制过程的自测方法与自测控制过程的统计方法等;结合多雷达数据融合测试系统实施例对自测系统的功能与性能进行分析说明.试验表明,通过对数据融合测试系统的自测,可进一步保证了测试结果的准确性和有效性.     

A Method for Judicious Fusion of Inconsistent Multiple Sensor Data

One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious measurements from sensors must be identified and eliminated since their incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a unified sensor fusion strategy based on a modified Bayesian approach that can automatically identify the inconsistency in sensor measurements so that the spurious measurements can be eliminated from the data fusion process. The proposed method adds a term to the commonly used Bayesian formulation. This term is an estimate of the probability that the data is not spurious, based upon the measured data and the unknown value of the true state. In fusing two measurements, it has the effect of increasing the variance of the posterior distribution when measurement from one of the sensors is inconsistent with respect to the other. The increase or decrease in variance can be estimated using the information theoretic measure "entropy." The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data obtained from all sensors were fused simultaneously, and a decentralized or sequential Bayesian scheme that proved useful for identifying and eliminating spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious sensor measurements and eliminate them from the fusion process, thus leading to a better overall estimate of the true state. The proposed strategy was also validated with the help of experiments performed using stereo vision cameras, one infrared proximity sensor, and one laser proximity sensor. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace     

用于判别连接状态的机器人手爪多传感器数据融合

舱外移动机器人在空间站工作时 ,其手爪与工件及空间站的安全连接至关重要。分别用BP神经网络和径向基函数神经网络 ,对机器人手爪上的力传感器、接近觉传感器和位移传感器的信息进行了融合 ,得到一个关于机器人手爪连接状态的正确判别