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    Design of Large Data Acquisition System
    浅析大型数据采集系统的设计
    (2) The HMMs’ parameters are further trained by the method of iterative learning from a large data set;
    (2)通过迭代学习的方法在大样本下进一步训练这些隐马尔可夫模型参数;
    The computation results show that parallel genetic algorithm is fit for data reduction problem with large scale and large data sample point.
    计算结果表明,并行遗传算法适合于求解问题规模较大及大数据样本点的数据约简问题。
    A Novel Classification Method in Large Data
    一种新的海量数据分类方法
    The Large Data Direct Classifying Method Based on Hyper Surface
    基于超曲面的海量数据直接分类方法
    This system applies Active Server Pages (ASP) technique and other related Web techniques to carry out composite data query based on the database with large data size and complicated data structure.
    本系统应用ASP和其它相关Web技术,在数据量纷繁复杂的数据库的基础上,实现数据综合查询,数据的分析、控制、数据处理等技术与采用Web技术的信息查询和发布系统有机地结合在一起,并考虑了应用的安全性与系统的高效性。
    Aiming at the needs of large data, high transport and complex operation, a real- time video- based processing system using SAA7115 as its capture A/D, TMS320DM642 as its core,SAA7105 as its display D/A ,is designed, and FPGA is used to control the display to achieve advanced function.
    针对疲劳检测算法中大数据量、高速传输、复杂运算的实际需要,设计了以SAA7115为视频采集A/D、DSPTMS320DM642为核心处理器、SAA7105为视频输出D/A,并以FPGA控制输出来实现增强显示功能的实时视频处理系统。
    The system has the characteristics of high automation degree,large data collection capacity and strong system reliability,etc.
    该系统具有自动化程度高,数据采集量大,系统可靠性强等特点。
    With Bisecting K-means algorithm,experiments show that the hierarchy of ontology concept is more refined and has lower time-complexity. Bisecting K-means algorithm is especially suited for handling large data sets.
    实验结果表明,Bisecting K-means算法产生的本体概念的层次更加精炼,时间复杂度较小,特别适合用于处理大型数据集.
    Parameter learning for Bayesian networks with large data set
    面向大规模数据集的贝叶斯网络参数学习算法
    A Feature Selection Method Fitting for Large Data Set
    一种适合大规模数据集的特征选择方法
    Extracting deeply knowledge, optimal operation condition and manageable pattern from large data of production and management, namely, process industrial data mining, is one of the most important technologies to realize online monitoring, fault diagnosis, safety estimation, product management, marketing analysis and prediction and so on of process industry. In addition, it can provide more effective decision support for industrial safety operation and efficient manufacture.
    从大量的生产、管理数据中挖掘深层次的生产知识、最优操作条件和管理模式,即流程工业数据挖掘,是实现流程工业过程的在线监测、故障诊断、安全评估、生产管理、市场分析与预测等的关键技术之一,为流程工业的稳定操作、高效生产提供更有效的决策支持。
    To the great of large data problem of robotic graphics, a new technique of partition coding and transportation for the robot object is presented, which using projection method and kinematical principle to implement the quickly partition and coding of the robot object.
    针对机器人图像中大数据量问题,本文提出了把机器人对象重点分割进行编码和传输的方法,运用投影方法和机器人运动学原理,能够快速实现机器人对象的有效提取和编码。
    It is a new effective way that takingthe advantage of database technology to improve or design new efficientalgorithms suitable for large data sets, which is being explored by manyscholars.
    充分利用数据库技术所具有的对数据库中数据操作的优势,来改进或设计新的适合于大数据集的高效数据挖掘算法,是许多学者正在探索的一个有效途径。
    1. Based on the analysis of the time cost and space cost as well asthe necessity of using discernibility matrix to obtain core attributes inattribute reduction algorithm, propose an improved attribute reductionalgorithm based on Rough set and database technology. Experimentsshow that its efficiency on large data sets is much faster than some otherattribute reduction algorithms based on memory and is easy to realize anduse.
    1.对属性约简算法中基于分辨矩阵求取核属性的时空代价以及必要性进行了分析,基于Rough集的有关理论和数据库技术对基于粗糙集的属性约简算法进行了改进,实验表明在大数据集上该算法的效率大大高于一些基于主存的属性约简算法,且易于实现和使用。
    On the other hand,,RST has the features of handling and reducing large data sets while has lower classification accuracy than SVMs.
    粗集理论则具有处理和约简大数据量的优势,但分类精度不如SVM方法。
    This system applies Active Server Pages (ASP) technique and other related Web techniques to carry out composite data query based on the database with large data size and complicated data structure. Such techniques as data analysis, control and processing are integrated with the information query and distribution system using Web technique, taking the consideration of application's security and the system's performance. Design of this solution is implemented according to users' need directly.
    本系统应用ASP和其它相关Web技术,在数据量纷繁复杂的数据库的基础上,实现数据综合查询,数据的分析、控制、数据处理等技术与采用Web技术的信息查询和发布系统有机地结合在一起,并考虑了应用的安全性与系统的高效性。
    With the expansive applications and exponent growth, people are eager to get newly tools and technologies more and more to extract interest information from large data source intelligently. KDD/DM is a Intelligent Data Analysis(IDA) technology proposed for that intention.
    数据库的广泛应用和数据量的飞速增长,使人们迫切地感到需要新的技术和工具以支持从大量的数据中智能地、自动地抽取出有价值的知识或信息,数据库知识发现就是为解决上述问题而提出的智能数据分析(Intelligent Data Analysis,IDA)技术。
    However, the large data and information load contained by image signal put much difficulty in real-time image processing, though they are definitely helpful to realize machine intelligence in the future.
    然而,图像信号本身巨大的信息量和数据量在为实现机器智能提供重要支持的同时,同样也给我们要进行的对图像本身的实时处理和理解带来了困难;
    5.T0 the disposal of super large data set, the building of cluster systems is put forward and the algorithms of task disassemble and computation are designed.
    5.针对超大数据集的处理,提出构建集群系统通过并行计算来完成任务和相应的任务分解方法和计算方法。
 

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