Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

基于单时相高分辨率遥感影像的震后道路提取研究

地震发生后,道路是生命救援及运输物资的重要通道,因此,快速有效地获取道路的信息是震后抢险救灾非常关键的一步。在震前影像及矢量数据等缺失的情况下,本文基于2014年8月3日云南鲁甸地震的单时相高分辨率World View-2遥感影像进行了震后毁损道路提取方法研究。在提出的一个震后毁损道路提取方法中,先在e Cognition软件中对影像进行多尺度分割和最邻近分类,然后在Matlab软件平台下对分类结果进行二值化,并用数学形态学技术去除偏大和偏小的地物,最后利用Hough变换进行道路检测得到毁损的路段。实验结果证明:该方法能够快速有效地提取出震后的山区道路毁损信息,可为地震应急提供信息支持。     

基于VRS的城市道路改建测量方案设计与实施

针对城市化发展中的道路改建工程,本文以城市虚拟参考站为基础,设计了相应的测量方案。对测量成果进行分析和效益评价,得出VRS技术在大中城市的基础测绘中有较好的应用。本文涉及的道路改建工程对类似的城市测绘工作具有借鉴意义。     

Understanding water pollution management: Evidence and insights from incorporating cultural theory in social network analysis

Social network analysis gives a rigid assessment of network structure while cultural theory helps explain management outcomes in more detail. The combination of these methods provides significant and alternative insights about the complexity of water pollution management. Applied in the case study of the Calumpang Watershed in the Philippines, two types of relations were evaluated, namely, resource sharing and cooperative activities. Factors affecting cooperation and the creation of bridges were assessed in social network analysis, while cultural theory was used to reveal the underlying views of key actors on the nature of the river and its management. Evidence showed that cooperation was significantly influenced by resource sharing regardless of institutional affiliation, while bridges were created by becoming the pools of information and resources in the network. Using cultural theory, this study found out that local government officials dominantly have a mixture of hierarchist and egalitarian views characterizing belief in both rule-bound and group-motivated actions to solve the water pollution problem. However, their egalitarian views on management did not manifest in the current network. The study concludes that collaborative partnerships for water pollution management can be facilitated by promoting resource flow, utilizing influential bridges in the network, and understanding the unconventional values of government actors. Combining social network analysis and cultural theory is recommended to generate a more critical assessment of the institutional complexity of pollution and other water issues in watersheds.     

传感器网络环境中基于采样数据分布式滤波的研究综述

近年来,由大量具有感知、计算和通信能力的微型传感器组成的传感器网络越来越广泛地应用在生产及生活的方方面面.另一方面,随着微电子及数字信号技术的发展,利用采样数据的离散化的数字控制器或滤波器被普遍使用.为了反映这个新兴领域的最新进展,本文对传感器网络环境中基于采样数据分布式滤波的研究展开了综述.首先,综述了传感器网络中几种分布式滤波方法的研究进展.然后,针对不同的采样方式,详细总结了采样数据系统的控制与滤波问题的研究工作.随后,对目前已有的传感器网络基于采样数据的分布式滤波的研究结果进行了介绍.最后,对传感器网络环境中基于采样数据的分布式滤波方面未来可能的研究课题进行了展望.     

基于路网相关性的分布式增量交通流大数据预测方法

针对城市道路拥堵问题的日益加剧的问题,智能化城市交通管理平台是缓解拥堵问题的有效方法,利用交通流大数据预测结果进行交通诱导,能够指导用户调整出行方案,有效缓解交通压力。研究了交通流大数据的分布式增量聚合方法,对海量交通流数据进行清洗统计,为交通流预测提供数据基础,基于交通流在路网中上下游路段的相关性分析,利用路口转弯率多阶分配将该相关性量化,构建基于路网相关性的空间权重矩阵,完成对于STARIMA模型的改进。通过应用试验证明,该方法能更准确的进行交通流预测,为交通诱导信息发布提供依据。     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Dynamic model of manoeuvrability using recursive neural networks

This paper presents a Recursive Neural Network (RNN) manoeuvring simulation model for surface ships. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. This model is used to test the capabilities of artificial neural networks in manoeuvring simulation of ships. Two manoeuvres are simulated: tactical circles and zigzags. The results between both simulations are compared in order to analyse the accuracy of the RNN. The simulations are performed for the Mariner hull. The data generated to train the network are obtained from a manoeuvrability model performing the simulation of different manoeuvring tests. The RNN proved to be a robust and accurate tool for manoeuvring simulation.     

多重贸易网络的空间演化特征及其影响因素——基于货物、服务和增加值贸易的比较

基于复杂网络分析与贸易增加值核算方法,借助ArcGIS、Ucinet和MATLAB等工具,对全球64个主要经济体货物、服务和增加值多重贸易网络的空间演化及其影响因素进行综合对比分析,结果表明：① 全球货物、服务和增加值贸易网络的紧密性和通达效率均呈现稳步提升趋势,服务贸易的网络化趋势更为显著;金融危机对网络的“互惠性”“异配性”产生较大影响;② 空间格局上均具有显著的“核心?边缘”等级性,区域层面上形成了亚太和欧盟地区两大“派系”,美、中、日、德为两大“派系”联系的枢纽节点;③ Top1网络表现出不同的演化特征,其中货物、服务和增加值贸易网络分别呈现“星型”“雪花型”和“星型+链式”空间组织结构。3类网络演化过程中中国的崛起态势令人瞩目;④ QAP（二次指派程序）分析显示,传统的GDP、地理距离、共同语言等因素对于3类网络的形成具有相似的较强解释力,要素禀赋差异和贸易制度是影响它们之间差异性的主要因素,但其显著性水平不高且趋于弱化。     

基于路网形态的上海住宿业空间布局特征

采用中心性模型、空间自相关理论、核密度法等,从城市路网形态角度分析上海市住宿业的空间格局特征及其与路网结构的空间响应关系。结果发现,上海市住宿业分布具有空间集聚性、类型分异性和距离衰减性,集聚核心与路网节点密度核心一致,主要围绕中心城区形成一级集聚核,星级酒店集聚分布在中心城区,经济型酒店和旅馆在一级集聚核外形成多个散状集聚核心,旅馆集聚依附性较为明显,民宿集聚度最高。不同类型的住宿设施分布存在较显著的空间相关性,对路网依赖性具有多元性,受路网影响的程度不同,道路连接值较高的区域交通可达性较好,住宿设施密度也相应较高。经济型酒店受到城市路网相对可达性和直线型交通效率的依赖最大,旅馆和民宿对于路网节点间通达性的依赖相对较大。住宿业发展要在产品偏好设计的前提下兼顾城市路网结构和消费需求进行合理选址,以实现综合效益最大化。