城市扩展元胞自动机多结构卷积神经网络模型

传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。     

基于神经-模糊方法的单料烟感官质量评价专家系统

作者通过对单料烟评吸的结果与理化测定的指标参数进行分析 ,结合专家经验并采用神经 -模糊方法 ,提出一种基于单料烟的理化指标对各感官参数进行分类、分级 ,建造单料烟感官质量评价专家系统的方法。实验表明 ,该系统具有学习与知识提取能力 ,在卷烟产品质量管理新产品开发中具有指导意义     

多模式自适应水下无线通信网络框架研究

对当前典型的水下无线通信网进行分析,针对水声、光、射频3种通信模式在水下无线通信中的优缺点,提出基于软件无线电技术的多模式自适应水下无线通信网络的概念及其框架结构,并对其中的自适应调制解调方式展开研究.结合MAC层协议,提出一种跨层的自适应调制解调解决方案,即通过收发双方的握手信息携带当前信道状态,由发射方根据握手信息,判断双方通信距离,预计信道未来状态,结合需要传输的数据量,自适应选择合适的通信模式和调制方式,并利用握手信号通知接收方,从而实现在通信网络范围内数据或指令的快速可靠传输.     

Pipe laying simulation with an active reel drive

Dynamic and quasi-static models for analysis of a pipe lay spread are presented in the paper. Depending on the type of a pipe, spooling it on a big drum (reel) may cause plastic deformations. When the pipe is reeled out at sea, again it undergoes plastic deformations. In order to model such a nonlinear behaviour, a model, which includes both elasto-plastic material characteristics and large deformations, is required. Discretisation of the pipe is performed by means of the Rigid Finite Element Method (RFEM), which allows us to study static and dynamic problems, taking into account required properties of the model. Different sea conditions are simulated and analysed. It is shown how operational conditions can limit the ability of laying operation by a dedicated vessel, due to an unstable system response. The second part of the paper presents an upgrade of a passive reel drive by application of an active drive. In the model used for control applications, the pipe is modelled by a neural network. This allows us to perform a real-time calculations. In this fast-response calculation model, nonlinear aspects of the system are taken into account. The improvements in a pipe laying vessel performance are presented by some results of numerical simulations. The conclusions are also formulated.     

Remote sensing of water depths in shallow waters via artificial neural networks

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.     

经济地理学视角下创新网络与区域增长研究述评

创新是引领经济发展的第一动力,创新与区域增长之间的关系成为经济地理学者关注的重点领域。文章通过梳理近年来有关创新网络测度、创新网络与区域增长关系、创新网络作用于区域增长方式等方面的文献发现：网络资本可以弥补社会资本在解释企业创新结网经济价值方面的不足,区域增长呈现出网络化特征已成为学界共识,知识流与邻近性能较好地解释创新网络与区域增长的关系机理。然而,当前研究对社会资本如何促进区域增长,网络资本与区域增长关系模型如何构建,不同类型的邻近性与知识对区域增长的影响有何差异等问题尚不明确,建议重视网络资本对区域增长的作用并实证检验二者的关联,同时还应重视创新网络中企业家精神、创新个体心理行为特征等因素对区域增长的影响。     

中国道路网引起的景观破碎格局及其对保护区的影响

道路的修建往往带来景观破碎、生态系统功能受损等后果。有关道路网络的生态影响格局及其影响因素,是当前道路生态学、地理空间分析等领域共同关注的问题。本文利用2015年全国道路网络数据,采用定量空间分析方法,刻画了当前中国铺装道路所引起的陆表景观破碎的格局特征,并分析了道路网络对自然保护区的干扰及与多变量的影响关系。结果表明：① 全国铺装道路的生态影响面积占陆地面积的10%,将陆表切割成3万多个斑块,并呈现小斑块数目众多,大斑块数目较少的分布特点;同时,陆表破碎化程度呈现明显的东西分异规律,空间格局与人口分布、经济发展水平等影响因素的地理分布规律趋同;② 全国约58%的自然保护区受到道路干扰,保护区的受干扰情况存在高等级保护区受干扰弱、低等级保护区次之、国家公园受干扰程度最强等差异特征,且国家公园处于受干扰高于未设保护区域的状态;③ 主要人类活动因素与保护区受干扰程度呈正相关,保护区面积、地形因素与受干扰程度呈负相关,具有面积小、受保护等级低、位于平原地区、气候适宜等特征的保护区更易受到道路干扰,处于已经受到人类活动破坏,生态脆弱的状态。因此,中国道路建设应追求社会发展与生态保护双平衡,同时道路干扰受到自然以及人文要素的共同作用,在相关影响机制研究和生态保护政策制定中应进行综合考虑。     

桩板组合结构的技术特点及应用

介绍了桩板组合结构的组成和特点,及其在实际工程中的应用。该结构可作为滩海地区进海路路堤、人工岛护壁、保滩促淤丁坝和离岸堤主体结构。     

海洋环境无线传感器网络监测体系与应用技术研究

针对目前海洋环境监测中急需解决的主要技术问题.对海洋环境无线传感器网络监测的体系结构进行了研究,通过对Crossbow无线传感器网络的拓扑结构和软件架构进行分析与试验.提出其技术优势和有待解决的应 用问题.