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蔡静颖 《电子技术与软件工程》2021,(5):190-192
本文主要以计算机数据挖掘技术的开发与应用分析为重点进行阐述,首先分析计算机数据挖掘技术和大数据之间的关联,其次介绍计算机挖掘技术的开发措施,最后从市场营销过程中应用挖掘技术、金融投资过程中应用数据挖掘技术、人事管理过程中应用数据挖掘技术、信息档案开发管理过程中应用数据挖掘技术、在网络病毒防御系统开发中应用数据挖掘技术几个方面深入说明并探讨计算机数据挖掘技术的应用途径,旨意在为相关研究提供参考资料。 相似文献
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简要介绍了数据挖掘的概念和典型的处理过程,分析了通信对抗情报侦察系统在复杂电磁环境下实现电磁态势监视和电子对抗情报支援的情报处理需求。提出了应用数据挖掘技术的通信对抗情报侦察系统总体框架。结合不同种类和不同平台侦察传感器获得的多源情报侦察数据类型和特点,详细论述了采用数据挖掘从海量的多维侦察数据中获取侦察情报的方法。 相似文献
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电子商务运营过程中,存在着海量的数据,将会增加数据处理的难度,需要注重Web数据挖掘的应用,提高数据的处理水平。基于此,文章先从客户群体、站点设置、个性化分析三个方面对Web数据挖掘在电子商务中的需求进行分析,对数据处理需求进行明确,确保技术运用具有针对性;再从挖掘目标、数据模型等方面对技术实现进行分析,实现数据挖掘技术的有效运用;最后,分析Web数据挖掘的具体应用,对电子商务进行处理,保障数据挖掘目标的实现。 相似文献
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随着信息采集技术的不断发展,采集手段不断多样化,导致大量舰机活动数据存放在计算机中,人们关注的往往是其蕴含信息的实时性,而数据的累计价值无法得到再次利用。如何有效、充分地利用这些数据成为一个迫切需要解决的问题。提出一种将数据挖掘应用到舰机活动数据的实用技术,包括关联规律数据挖掘和序列数据挖掘。通过挖掘不但可以有效缩减数据库的存储数据量,更可以根据挖掘结果建立挖掘知识库,通过长时间的累计,将能有效区分普遍规律及异常,从而充分利用海空活动目标数据的价值,提高辅助决策能力,为活动目标数据处理提出了新的方向。 相似文献
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随着数据挖掘技术的成熟,其中关联规则在大规模数据中的应用成为了目前的热点.为了提高在大规模数据下进行数据挖掘的效率,在MapReduce中通过引入归并函数Fusion来提高执行剪枝任务的效率并进行了优化研究,提出了一种基于云理论的关联规则Apriori算法,详细论述了实现的过程以及关键技术.通过实验表明,该方法取得了良好的实验效果,克服了Apriori算法耗时多、识别率低下等问题,是实现在大规模数据进行数据挖掘的实用工具. 相似文献
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远距离目标检测具有重要的军事应用价值,而多传感器能提供互补和冗余信息,在远距离目标检测中具有独特的优势。针对可见光和红外热图像序列中的远距离目标,提出了一种基于信息融合的目标检测方法。该方法通过帧间差累积,在两种传感器的图像中确定了运动目标区域,定义了运动目标区域可信度度量,利用融合的规则确定了最后的运动区域。在运动区域中利用简单的分割方法将目标提取出来,最后经过特征级融合得到了检测结果。本方法不需要进行背景估计,多传感器信息的冗余增强了检测结果的可靠性,实验结果证明了本文方法的有效性。 相似文献
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The recognition and detection of space debris has become one of significant research fields recently. Compared with natural images, effective information are very few contained in star images. In the past years, the gray values of star points and the continuity of sequential star images are utilized by numerous algorithms to carry out the recognition and detection through fusion of consecutive star images, which have been achieved good performance. However, with the rapid increase of star image data, those algorithms seem to be inadequate in recognition ability. In this paper, we propose one novel approach based on the full information vectors of star points to recognize moving targets with the machine learning method which is never utilized in space debris recognition field. Besides gray values, we further deeply excavate the characteristics of each star point in a single frame by the equal probability density curve of Gaussian distribution. The elliptical pattern characteristic vectors of star points can be input into the machine learning method for classification of static stars and moving targets in a single frame. Finally, trajectories of moving targets can be determined within 3 frames by the full information vectors. Therefore, traditional processing methods are abandoned and the proposed brand new approach redefines the recognition technical route of space debris. The experimental results demonstrate that moving targets can be successfully recognized in a single frame and the coverage rate of moving targets can reach 100%. Compared with other traditional methods, the proposed approach has better performance and more robustness. 相似文献
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An accurate and efficient Synthetic Aperture Radar (SAR) raw data generator is of con- siderable value for testing system parameters and verifying imaging algorithms. Nevertheless, the ex- isting simulator cannot exactly handle the case of the fast moving targets in high squint geometry. As for the issue, the analytical expression for the two Dimensional (2-D) signal spectrum of moving targets is derived and a fast raw echo simulation method is proposed in this study. The proposed simulator can accommodate the moving targets in tile high squint geometry, whose processing steps of the simulation are given in detail and its computational complexity is analyzed. The simulation data for static and moving targets are processed and analyzed, and the results are given to validate the effectiveness of the proposed approach. 相似文献
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高径向速度目标会产生严重的距离走动并伴随方位失配,方位压缩会使其散焦并弥散在SAR图像中,不易被检测。该文针对高径向速度目标的检测问题,提出一种基于单通道机载SAR的检测方法。该方法通过抽取等效双通道,利用相干对消抑制杂波,并去除动目标的频谱分裂,再运用Dechirp处理和Hough变换积累目标在距离单元内和距离单元间的能量,以获得更大的积累增益。与传统的混合积累方法相比,该方法在抑制杂波的基础上,更好地积累高径向速度目标的能量,从而有效提高该类目标的检测性能。仿真数据和实测数据均验证了该方法的有效性和优越性。 相似文献
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一种基于粒子滤波的自适应运动目标跟踪方法 总被引:6,自引:0,他引:6
该文提出了一种基于粒子滤波的自适应运动目标跟踪方法。均值漂移算法是一种最优梯度下降法,通过迭代来搜索目标,从而实现对运动目标的跟踪。而粒子滤波是一种在非线性和非高斯情形下进行跟踪的强有力方法。该文首先对图像的直方图进行改进,提出了一种基于统计直方图分布的目标模型,然后通过这个模型将这两种方法有效地结合起来。根据跟踪的过程,自适应地调整参数,能够较好地处理图像序列中由于光线变化或遮挡所带来的影响。实验证明,该文所提出的方法与均值漂移方法相比,即使在复杂的情形下,也能够准确地对目标进行跟踪。 相似文献
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雷达应用领域中的一个重要问题是测向测速,精确地测定多普勒频率和方位角能够实现对目标回波信号的高分辨率处理,从而进行目标跟踪。本文提出一种超分辨的二维谱估计方法,它利用的是阵列处理技术,对天线接收数据进行本征分析,将信号数据张成一个空间并对其进行分解,根据正交原理分割成信号子空间和噪声子空间,构造出噪声本征矢量,利用信号子空间与噪声子空间的正交性,在空间谱上形成极值点,从而达到对方向和速度的二维高分辨率估计。该方法应用于相控阵雷达信号检测,可以精确地测定多普勒频率和方位角度,提取多个空间运动目标的二维信息。文章的最后给出了计算机模拟成像结果,验证了算法的有效性。 相似文献
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Distributed fusion architectures and algorithms for target tracking 总被引:15,自引:0,他引:15
Liggins M.E. II Chee-Yee Chong Kadar I. Alford M.G. Vannicola V. Thomopoulos S. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1997,85(1):95-107
Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given. 相似文献