基于Web 2.0的边与节点同时增长网络模型

模拟了Web 2.0网络的发展过程并研究其拓扑结构,分析某门户网站实际博客数据的度分布、节点度时间变化,发现与先前的无标度网络模型有所差别. 根据真实网络的生长特点,提出了边与节点同时增长的网络模型,包括随机连接及近邻互联的网络构造规则. 仿真研究表明,模拟的网络更接近实际,在没有优先连接过程时,模型能得到幂率的度分布; 并且网络有更大的聚类系数以及正的度相关性.     

A new model to estimate significant wave heights with ERS-1/2 scatterometer data

A new model is proposed to estimate the significant wave heights with ERS-1/2 scatterometer data. The results show that the relationship between wave parameters and radar backscattering cross section is similar to that between wind and the radar backscattering cross section. Therefore, the relationship between significant wave height and the radar backscattering cross section is established with a neural network algorithm, which is, if the average wave period is ≤7s, the root mean square of significant wave...     

Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

BP神经网络在区域似大地水准面精化中的应用

研究BP神经网络算法在区域似大地水准面精化中的应用,阐述了神经网络的结构及其算法,根据焦作市D级GPS网控制点的数据,利用MATLAB进行编程计算,拟舍得到厘米级的似大地水准面模型,并得到了有益的结论。     

一种加窗速度平滑伪距的定位算法

海面传感器网络的跟踪定位受到海浪以及海杂波的影响,难以满足协同观测、协同通信以及协同环境遥感等任务的精度需求,针对这一问题文中提出了加窗径向速度平滑伪距的定位方法(WVSP),并利用MonteCarlo仿真技术分析了该算法应对海杂波和链路测距粗差的定位精度。仿真处理的结果表明:与常用的直接伪距定位法、速度平滑伪距定位法相比较,WVSP能够有效地削弱测距链路中粗差的影响,降低了海杂波的干扰,使定位精度收敛。     

Adaptive Neural Network Control with Control Allocation for A Manned Submersible in Deep Sea

This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China.The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module.A control energy cost function is used as the optimization criteria of the control allocation module,and weighted pseudo-inverse is used to find the solution of the control allocation problem.In the presence of bounded unknown disturbance and neural networks approximation error,stability of the closed-loop control system of manned submersible is proved with Lyaponov theory.The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.     

深度卷积网络支持下的遥感影像井盖部件检测

数字城市管理发展中城市部件调查是一项重要的任务,但是城市井盖部件信息获取存在人工调绘效率低、精度难以保证等缺陷,影响城市井盖部件的及时更新。因此本文利用深度卷积神经网络模型,通过小卷积核、尾部裁剪和保持输入大小等改进边缘检测网络（HED）并增加两层卷积运算提取目标,提出HED-C网络模型,实现了端到端的井盖部件目标检测。试验结果表明,利用HED-C模型井盖部件召回率可达96.58%,查准率可达97.93%,相较Faster R-CNN、YOLO和SSD网络模型,综合性能有了较大提高。     

利用全卷积网络提取Sentinel-2影像高低层建筑区

面向Sentinel-2多光谱数据,依据影像地物空间结构和地表实际功能类型将建筑区分为高层建筑区和低层建筑区,构建了一种基于全卷积网络模型的高、低层建筑区快速提取技术。在此基础上,以雄安新区及其周边为试验区,选取2017年3月获取的4景Sentinel-2多光谱数据进行试验验证和分析。结果表明：本文技术能够实现大范围区域内高层和低层建筑区的快速提取,总体平均提取精度达到95.30%,其中高层建筑区平均提取精度为99.22%,低层建筑区平均提取精度为91.38%,该技术明显优于现有基于纹理结构的高低层建筑区提取方法。通过对提取结果进行统计分析发现：约4.4×10⁴ km²的研究区内高层和低层建筑区分别约为94和7351 km²;雄安新区中心三县内高层和低层建筑区分别约为1.25和312.24 km²。本文技术具有很好的推广性,结合Sentinel-2数据大幅宽高频次观测特点和更多类型建筑区样本,可以实现大范围地表多类型建筑区动态监测。     

对遥感科学应用的一点看法

本文提出遥感科学发展至今,需要进一步拓展和深化应用的一点思考。遥感已经无处不在,遥感学者不能停留在对传统的航空和卫星遥感数据的处理和分析,应该把现在无处不在的地面摄像、录音甚至是味觉传感数据以及其他社会经济大数据应用起来。遥感学者走出自己的熟悉领域实现跨学科发展,才能取得更大的科学突破。最后对遥感在实现联合国可持续发展目标中的作用进行展望,以期对年轻学者有一定帮助。     

高分二号遥感影像提取冬小麦空间分布

精细的农作物空间分布数据对于资源、环境、生态、气候变化和粮食安全问题均具有重要的意义,卷积神经网络已经成为从遥感影像中提取农作物空间分布数据的主要方法,但提取结果中的种植区域边缘往往比较粗糙。本文以高分二号遥感影像为数据源,选择冬小麦为提取目标,利用RefineNet模型和最大后验概率模型构建冬小麦遥感提取模型WWRSE(Winter Wheat Remote Sensing Extraction),获取精细的冬小麦空间分布数据。WWRSE模型利用RefineNet网络提取像素的语义特征,使用改进的SoftMax模型生成像素的类别概率向量;以类别概率向量的最大分量与次大分量的差值作为置信度,根据置信度将类别概率向量分为可信和不可信两组,可信组直接使用最大分量对应的类别标签作为相应像素的分类结果;结合最大后验概率模型确定不可信组像素的分类结果。利用随机梯度法对WWRSE模型进行训练。选择SegNet、DeepLab、RefineNet作为对比模型进行实验,WWRSE提取结果的精度为92.9%,比SegNet提高了13.8%,比DeepLab提高了10.9%,比RefineNet提高了8.6%。实验结果表明WWRSE模型在提取冬小麦空间分布数据方面具有一定的优势。WWRSE模型提取的结果能够为大范围冬小麦种植面积统计提供依据。