数据挖掘与人工智能技术

数据挖掘是人工智能中的一个重要分支。对人工智能的发展及其最基本的技术：知识表示、知识推理、搜索，以及数据挖掘进行了详细介绍。通过实例给出了数据挖掘的定义，最后论述了数据挖掘与人工智能的联系，并结合国内外在该领域的研究成果指出了其广阔的发展前景。     

DGPS/INS Data Fusion for Land Navigation

IntroductionLandnavigationhasledtothecreationofmanyresearchprojectsandplatformslikeTRAVELandOPSYDE,whicharepartoftheAUTORISproject(AutomaticsforanIntelligentandSafeRoad).TheTRAVELplatformconsidersthecreationofanautonomousconvoyofvehicleswhiletheOPSYDEprojectconsistsofdatafusiontechniquestosolvetheproblemofmultisensornavigationinordertoincreasetheaccuracyandreliability.Testplatformisinstalled,composedbyROBUCARelectricvehiclesandtheironboardsensorswherethemostimportantareGPSand…     

联机增量聚类算法研究

研究了联机数据挖掘系统中的并行和增量聚类算法,并给出了算法伪码。实验表明,联机增量聚类算法相对于传统的Apriori算法具有较大优势,同时证明了增量聚类算法及其联机数据挖掘系统的实用性。     

基于动态可调簇的无线传感网络能量感知数据存取算法

针对无线传感网中数据收集、存储和查询的需要,提出了一种基于动态可调簇的能量感知数据存取算法ACEDBA(Adjusted Cluster-based energy-aware Data Brokerage Algorithm).该算法将网络节点分成许多簇结构,簇内节点负责数据采集,簇首节点负责簇内数据的收集、汇聚和转发,数据可以集中存储,也可以分布存储在各簇首节点或本地存储.算法引入能量分级消耗机制,可动态选择和调整簇首节点,保证各簇首节点簇内数据存储和簇间数据转发查询的能量平衡.实验结果表明,与典型数据存取算法相比,该算法的节点能耗均衡、生命周期和查询成功率方面具有优势.     

基于小波蚊子的车联网性能评价方法

数据传输可靠性已成为车联网研究的热点问题,首先给出了车辆之间数据传输可靠性的评价方法,并结合小波变换和群蚊子算法对数据评估模型进行求解,同时通过引入分类机制和决策权来引导搜索方向,以此加快算法收敛速度.最后利用仿真实验平台深入研究了影响该算法的关键因素,并讨论了数据包大小、车辆间距、发送功率和车辆速度与数据成功发送率之间的关系.与其他算法比较,该算法显示出较好适应性.     

Interior Layers for Second-Order Singular Equations

Our aim in this article is to study singularly perturbed problems which display boundary layers in the interior of the domain. These interior boundary layers which supplement the usual boundary layers at the boundary, are generated by discontinuities in the data. Second-order linear elliptic one-dimensional and multi-dimensional problems are considered in this article.     

基于智能数据采集器的研究与实现

针对目前各行业数字化、智能化产品应用日趋广泛，数据的获取、传输、分析、应用尤为重要，如何避免采集设备接口单一、功能固定、实用性差等缺点，提出采用ARM嵌入式工控模块M9080-N20为基础，采用VK3266串口芯片扩展UART串行接口，提出基于73K222AU调制解调器、HR219307变压器实现采集器二线模拟通信，并支持以太网、CAN总线和UART串行通信，可以有效解决数据远程采集、传输和控制，适用于特殊环境设备故障检测、诊断和抢修。实验结果证明，智能数据采集器不仅具有多种型号设备故障诊断功能；还能实现野外快速通信组网，不同类型设备数据远程采集、传输、分析和控制功能，在军民融合产品上具有广阔的应用前景和实用价值。     

Fuzzy structure-activity relationships

While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.     

Polymer chain conformation of copolymers with different monomer size: C NMR spectroscopy and MALS study

Copolymer composition, distribution and molecular size of the comonomer influence the final properties of polymer materials. Such influence can be followed from the effect on the chain conformation in solution determined from the scaling law between radius of gyration and molecular weight.     

Characterization of the road surfaces in real time

The main factor in the propagation of traffic noise is the road surface, where the vehicles generate noise due to the contact between tire and pavement, in addition to the noise produced by the engine.