空间数据挖掘及其在海洋地理信息系统中的应用

空间数据挖掘是数据挖掘中的一个重要门类,空间数据挖掘的意义是在空间数据库或数据仓库的支持下,提取、推理出未知、有价值、可利用的关系、模式或趋势,从而为决策系统提供支持。随着海洋测量技术和电子地图技术的持续发展,以及计算机存储容量的不断扩大,使得海洋地理信息系统中出现了海量数据,如何对这些数据进行有效利用,从而进一步发现价值,是当前海洋地理信息系统研究的主要课题之一。本文介绍空间数据挖掘技术的主要原则,分析空间数据挖掘的实际效果,并研究空间数据挖掘在海洋地理信息系统中的应用,为今后海洋地理信息系统的完善和提高提供依据。     

基于AIS的商船雷达性能分析方法

利用模糊综合评价将雷达数据和AIS数据进行融合形成两者的共同航迹,实现信息互补和位置的精确定位。首先将雷达和AIS测得不同坐标系下的位置信息变换到同一个平面坐标系中;其次统一传感器的信息,由于二者对同一目标所采集到的时间不同;然后综合考虑航行速度、航行角度和航行距离来确定航迹;最后实现信息融合,通过实验验证基于AIS的商船雷达性能分析是可行的。     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

公路工程施工资料的管理流程探讨

公路工程施工资料收集、整理、编制工作是贯穿于公路建设工程始终的一项重要工作，鉴于此，阐述公路工程施工资料收集、整理、编制的方法，对相关管理流程做了梳理，以促进公路工程施工资料管理。     

基于优化模型及多重数据处理的舰船污损监测法

针对目前舰船污损监测受环境和航行状态限制而无法实现连续监测的问题,提出了一种基于优化模型并对数据进行多重处理的舰船污损监测法。首先对轴功率与航速关系监测模型进行优化,将现有一维模型提高到更大航速区间内成立的二维模型。然后采集相关数据,根据其采集条件进行数据筛选及数据修正的多重处理,提高监测的连续性。其次建立污损阀值,与计算结果对比分析得到监测结论。最后将实船应用的监测结论与船体污损情况对比,验证了该方法的可行性和有效性。     

Using automated walking gait analysis for the identification of pedestrian attributes

Collecting microscopic pedestrian behavior and characteristics data is important for optimizing the design of pedestrian facilities for safety, efficiency, and comfortability. This paper provides a framework for the automated classification of pedestrian attributes such as age and gender based on information extracted from their walking gait behavior. The framework extends earlier work on the automated analysis of gait parameters to include analysis of the gait acceleration data which can enable the quantification of the variability, rhythmic pattern and stability of pedestrian’s gait. In this framework, computer vision techniques are used for the automatic detection and tracking of pedestrians in an open environment resulting in pedestrian trajectories and the speed and acceleration dynamic profiles. A collection of gait features are then derived from those dynamic profiles and used for the classification of pedestrian attributes. The gait features include conventional gait parameters such as gait length and frequency and dynamic parameters related to gait variations and stability measures. Two different techniques are used for the classification: a supervised k-Nearest Neighbors (k-NN) algorithm and a newly developed semi-supervised spectral clustering. The classification framework is demonstrated with two case studies from Vancouver, British Columbia and Oakland, California. The results show the superiority of features sets including gait variations and stability measures over features relying only on conventional gait parameters. For gender, correct classification rates (CCR) of 80% and 94% were achieved for the Vancouver and Oakland case studies, respectively. The classification accuracy for gender was higher in the Oakland case which only considered pedestrians walking alone. Pedestrian age classification resulted in a CCR of 90% for the Oakland case study.     

江阴大桥结构健康监测系统升级改造及初步数据分析

江阴大桥结构安全监测系统建于1999年,由于种种原因系统需要进行升级改造,以江阴大桥结构健康监测系统升级改造工程为背景,剖析了原系统的故障成因及新系统的解决方案,介绍了升级改造后的系统构成、传感器布置及系统软件功能等,并对系统试运行期间的部分数据进行了分析。     

主曲线方法在交通安全数据分析中的应用

针对交通安全数据的非参数性,将主曲线方法应用于交通安全数据分析.通过未知分布数据集的HS主曲线算法对交通安全数据进行分析后,得到一条通过数据集合中间的曲线,来描述该数据集合的特征,以此作为执行管理标准的依据.研究表明,交通安全数据集的主曲线能够较好的描述出数据本身所具有的非线性关系,可以作为执行管理标准的依据.     

公路建设项目中降雨因子对土壤流失量的影响分析

降雨因子对水土流失量起着决定性的作用。通过在汉十高速公路上设置实验小区，用径流小区法进行了水土流失监测，并根据数理统计原理，对水土流失量及降雨量的数据进行分析，建立回归方程，研究了降雨因子与水土流失量之间的关系，并对其结果从溅蚀和径流冲刷两个方面进行了理论分析。     

桥梁结构状态健康监测系统差错控制研究

数据可靠度问题是制约桥梁结构健康监测系统进一步发展的瓶颈。而数据通信领域利用差错控制思想来保证数据的可靠性,已获得成功应用。目前的桥梁结构健康监测系统尚无数据差错控制的思想,也无相关的理论和模型。从系统的角度提出桥梁结构健康监测系统差错控制的基本思想和模型,以及针对信息获取环节、数据传输环节、安全评价环节的差错控制机制,从而彻底解决桥梁结构健康监测系统的数据失真问题,提高系统的可靠性,有效降低虚/漏报警率,并在具体实践中得以成功运用。