江河入海流量在线监测不确定度评估方法研究与应用

鉴于国内目前缺少流量在线监测不确定性评估方面的研究,针对江河入海流量在线监测过程中可能产生的各种不确定性,本文采用不确定度概念对不确定性进行评估,给出了不确定度的主要来源、各来源不确定度的评估方法,以及江河入海流量在线监测总不确定度评估模型,并应用该模型对辽河入海流量在线监测的不确定度进行了评估。     

基于国产高分遥感的人工种植红树林种间分类方法研究——以广西茅尾海红树林为例

红树林的种间结构组成对红树林生态系统的健康和发展至关重要,而红树林种间分类问题一直以来都是基于遥感手段的红树林监测中的难点。针对该问题,以人工种植为特点的广西茅尾海红树林遥感种间分类为例,基于面向对象的分类思想,提出了一种现场样本与分割对象相结合的红树林种间分类方法。利用GF-2 PMS1高分辨率卫星遥感影像数据,开展了广西茅尾海红树林湿地典型植被精细分类和空间分布研究,并将分类结果与基于像素和传统面向对象SVM分类方法进行了对比。结果显示:总体上,面向对象分类方法更适合用于茅尾海红树林湿地典型植被分类;对于局部混生明显的区域使用基于像素SVM分类方法效果会更好;传统面向对象分类方法中将整个影像分割对象单元作为训练样本可能会在某种程度上造成负面影响。因此,使用文中提出的样本选择新方法进行面向对象分类精度最高,总体精度达到了93.13%,Kappa为0.89。     

锁块式水下采油树井口连接器性能分析

水下采油树井口连接器是连接采油树和井口的关键设备,对深海水下勘探开发及采油树的安装连接具有不可或缺的重要意义。以一种锁块式水下连接器为例,介绍其结构组成和工作原理,通过有限元建立连接器的三维模型,分析连接器在下放安装工况、连接锁紧工况、正常生产工况和钻完井工况下的连接性能,得出井口与采油树本体接触面作用力、VX钢圈接触应力和锁块受力情况随外部载荷的相互关系。结果表明,VX钢圈在整个过程中都能够保持良好密封,井口与采油树本体接触面未发生分离,弯矩对连接器连接性能影响很大,在额定弯矩作用时连接器部分锁块已发生弹塑性失效。     

长条状粗颗粒在垂直管路中最小输送速度试验研究

随着陆地金属资源的日益枯竭,深海矿产资源已经成为各国的重要战略目标。在深海采矿过程中,海底锰结核的形状除了圆球状,还有长条状等,颗粒形状对固液两相流管道输送特性具有很大影响。基于固液两相流垂直管道提升输送试验系统,探究不同工况下长条状颗粒在垂直上升流中最小输送速度的变化规律及特性,并得到了长条状群体颗粒最小输送速度计算公式。结果表明:长条状颗粒在垂直管道中上升过程中,颗粒中心轴与输送方向趋向于垂直,使颗粒在管道截面的投影面积最大化;长条状单颗粒的最小输送速度随着颗粒长径比增大而减小;在不同长径比工况下,随着管段颗粒平均浓度减小,长条状群体颗粒的最小输送速度均增大,且随着长径比增大,群最小输送速度减小。     

一种新型鲸鱼式潮流能发电装置的设计与试验研究

为了进一步提高潮流能的利用率,提出了一种新型鲸鱼式潮流能发电装置。借鉴风机叶片设计方法及水平轴水轮机的设计原理,利用结构力学、流体力学和CFD相关方法,对该装置的螺旋桨叶、导流筒和固定桩等部件进行了设计研究,从获能效果、装置可靠性和发电功率等方面进行了优化设计,并通过900 W样机试验验证了设计的有效性。试验结果显示,海流高潮期装置最大发电功率可达到980 W,一天内大约有4个缓潮期,此间发电功率明显下降,但持续时间不长,总体平均发电功率在800 W上下;同时潮流能轮机启动流速在0.41 m/s左右,有着良好的低速启动性能。     

浅水多波束测深仪高分辨率波束形成方法研究

常规波束形成技术以其稳健性好、计算量低等特点得到广泛应用,但其空间分辨率受阵元个数限制,不能突破瑞利限。因此,常规波束形成技术应用于高精度浅水多波束测深仪时,存在空间分辨率不足和旁瓣干扰等问题。对比研究了最小方差无失真(MVDR)及多重信号分类(MUSIC)波束形成在浅水多波束测深仪中的应用,给出了浅水多波束测深仪的常规波束形成、MVDR和MUSIC处理方法,并结合能量法和相位法两种底检测测深算法,处理了iBeam8120浅水多波束测深仪外场数据,验证了本文方法的性能。     

含沙浑水体的黏度测量及应用分析

含沙浑水体的高速冲击会对海底构筑物造成破坏,在计算浑水体对构筑物的作用力时,黏度是一项重要参数。本文利用落球试验和流变仪测试试验,测定了不同浓度含沙浑水体的黏度,给出其起始黏度与动力作用后的稳定黏度。结果表明:含沙量在大于400g/L时,浑水体为可用赫巴模型描述的非牛顿流体,并可简化为宾汉体;在含沙量小于400g/L时,浑水体仍可用宾汉体模型描述,在忽略较小的初始剪切应力时,可简化为牛顿流体。浓度大于400g/L的浑水体的起始黏度约为稳定黏度的100倍。文中讨论了含沙浑水体起始黏度与稳定黏度在工程计算应用中的适用情况。     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

自动化监测系统在深基坑监测中的可靠性分析

深基坑监测对于城市工程的安全建设具有重要的指导意义。为了探究自动化监测系统在城市深基坑监测中的可行性,根据全站仪的测量原理利用Matlab进行三维仿真,从理论上证明了自动化监测系统的平面精度能够达到规范要求。采用尺度变形分析及水平位移对比分析,论证了在城市深基坑监测中利用自动化监测系统进行水平位移监测的可靠性。     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.