基于GML的异构WebGIS空间信息共享研究

设计了一个基于GML的空间信息共享模型,给出了该模型所需要的关键技术,以此来对现有的WebGIS空间信息共享技术进行改进.该模型充分利用了现有网络和空间信息资源,较好地解决了异构WebGIS数据源的联合使用问题,同时具有良好的可扩展性.     

空间信息网格的多级资源管理结构模型

在分析和比较各种资源管理模型的基础上,分析了空间信息资源的特点,提出了一种较为有效的SIG资源管理模型——基于市场的多级资源管理模型。描述了该资源管理模型的体系结构,讨论了如何有效地维护空间信息网格资源的异构性及可扩展性,同时保证资源利用的最大效益。     

基于网格的空间信息共享技术

分析了空间信息共享技术的现状,针对如何消除"信息孤岛"的问题,提出了基于网格技术的空间信息共享模型.设计了元数据注册服务、元数据查询服务和空间数据服务,并通过实验进行了关键技术验证.实验结果表明,该模型在技术上是可行的,能够集成分布式异构数据资源.     

分布式空间信息的对等协同计算机制研究*

海量空间信息的处理需要分布式协同工作的GIS平台支持。为解决空间数据源的异构和分布式网络中的计算能力共享问题,设计了分布式空间信息的协同计算模型,分析了分布式空间信息协同计算具备的基本特征;从空间数据分布存储模型、空间数据分布式计算协同和分布式空间数据并行索引等方面讨论分布式空间信息的协同计算技术体系,并提出现阶段可行的实现机制。分布式对等协同计算机制避免了集中式执行引擎带来的网络拥塞和单点失效问题,提高了海量空间信息资源和计算资源协作的可靠性和可用性。     

基于Web Services的多源异构空间信息集成框架

为解决企业分布式多源异构地理空间数据互操作的瓶颈问题,设计了基于Web Services的四层多源异构空间信息集成框架.利用Web Services技术,OGC地理信息服务实现规范,和W3C标准的可缩放矢量图形成像模型(SVG)的集成,通过构建空间数据转换服务集成器,在用户和服务之间提供一种透明的松耦合,跨越系统底层的异构性,进行异构空间数据的互操作和显示,从而实现了多源异构地理空间信息集成与共享.在国家地震网络计算应用系统(SECAS)建设中,基于以上框架采用.NET平台进行设计并得到了初步应用.     

基于Web Services的异构空间信息共享

异构集成技术正日益成为信息资源管理的一个热点技术。以地理信息共享的应用为背景,提出了基于Web Services的异构空间信息共享体系结构。通过引入Web Services技术架构,设计了一个异构空间信息共享系统。该系统可提供各信息源灵活、动态发布自身服务的功能,从而为快速构建异构集成系统提供了性能保证。     

基于GRWSM协议的分布式空间数据处理技术

当今分布式空间信息系统成为空间信息系统发展的方向,文章在基于OGSA标准研究的基础上,提出了一种新型的基于GRWSM协议的分布式空间信息对象模型;通过对当今的各种分布式通行协议的比较,认为GRWSM协议有标准成熟,使用方便,功能强大等特点.本模型运用GRWSM技术极大方便了不同系统平台的空间信息系统和各种异构的空间信息应用程序之间的交流,能实现真正的空间信息分布式处理和计算,并使空间信息系统的开放性得以实现.     

异构感知数据的动态适配接入方法

开放地理空间信息联盟的传感器网整合框架（ SWE）定义了异构传感器统一描述标准,却未定义底层的异构数据接入该框架的过程,导致不同类型的感知数据接入时需要人工提供不同的数据接入方式。针对以上问题,设计了异构传感器信息模型（ HSIM）,描述了传感器的自身属性、观测能力等特征,同时概括了传感器数据结构和数据传输协议的特征;提出了一种异构感知数据动态适配接入方法,该方法通过动态生成适配器实现异构感知数据的自动解析和接入SWE的过程。气象异构数据适配接入系统表明：该方法能够在较少的人工干预下实现物理传感器与虚拟传感器异构感知数据的自动解析与接入,为异构感知数据动态集成提供了高效率的解决方案。     

基于Portal的空间信息门户的构建技术研究及实现

从我国空间信息基础设施的现状与迫切需求出发,提出构建空间信息门户,以解决分布式网络环境下对异构空间信息的共享管理问题。探讨了Portal技术、Aiax技术在构建空间信息门户中的应用,最后以“数字合肥”的USDI共享交换平台建设实践,证明了该技术的可行性。     

基于SDN的异构空间信息网络传输策略

在空间信息网络大时空尺度和节点稀疏特性的环境下,传统网络的异构性会降低网络传输效率,星间通信过程易中断,无法充分利用已存在的异构节点,导致星间链路频繁切换,使得平均传输时延增大与服务质量降低。为此,以空间信息网络为背景、以软件定义网络架构为核心对Floodlight控制器模块进行改进,运用控制器实现IP包与ATM包之间相互转换的策略,提出一种基于网络有效性的路由算法。仿真结果表明,与传统空间信息网络模型架构和卫星网络架构相比,通过该算法改进后的SDHSN架构在单位时间片内网络平均传输时延降低约3.75%,吞吐量提升7.22%～11.49%。     

A label field fusion model with a variation of information estimator for image segmentation

This paper proposes a new and reliable segmentation approach based on a fusion framework for combining multiple region-based segmentation maps (with any number of regions) to provide a final improved (i.e., accurate and consistent) segmentation result. The core of this new combination model is based on a consensus (cost) function derived from the recent information Theory based variation of information criterion, proposed by Meila, and allowing to quantify the amount of information that is lost or gained in changing from one clustering to another. In this case, the resulting consensus energy-based segmentation fusion model can be efficiently optimized by exploiting an iterative steepest local energy descent strategy combined with a connectivity constraint. This new framework of segmentation combination, relying on the fusion of inaccurate, quickly and roughly calculated, spatial clustering results, emerges as an appealing alternative to the use of complex segmentation models existing nowadays. Experiments on the Berkeley Segmentation Dataset show that the proposed fusion framework compares favorably to previous techniques in terms of reliability scores.     

Modal consensus observers for distributed parameter systems

This article proposes a new type of a consensus protocol for the synchronization of distributed observers in systems governed by parabolic partial differential equations. Addressing the goal of sharing useful information among distributed observers, it delves into the details governing the modal decompositions of distributed parameter systems. Assuming that two different groups of sensors are available to provide process information to the two distributed observers, the proposed modal consensus design ensures that only useful information is transmitted to the requisite modal components of each of the observers. Without any consensus protocol, the observers capture different frequency content of the spatial process in differing degrees, as it relates to the concept of modal observability. Their modal components exhibit different learning behavior toward the process state. In the extreme case, it turns out that certain modal components of the distributed observers occasionally behave as naïve observers. To ensure that, both collectively and modal componentwise, the observers agree both with the process state and with each other, a modal component consensus protocol is proposed. Such a consensus protocol is mono-directional and provides only useful information necessary to the appropriate modal component of the distributed filters that behaves as a naïve modal observer. This protocol, when abstracted and applied to different state decompositions can be viewed as mono-directional projections of information transmitted and received by the participating distributed observers. Detailed numerical studies of advection PDE in one and two spatial dimensions are included to elucidate the details of the proposed modal consensus observers.     

一种研讨信息组织模型及其在研讨厅中的应用*

在群体研讨过程中会产生大量的研讨信息,群体共识则蕴藏在这些研讨信息之中,因此有必要对研讨信息进行分析处理。为此,提出一种面向群体共识涌现的研讨信息组织模型(DISM)。该模型根据Toulmin逻辑对专家发言进行结构化处理,将专家发言分解为根据、论证、模态限定和主张等几个部分。其中,模态限定是一个可量化的部件,反映了专家对主张的态度;根据和论证则为主张提供支持。该模型是对研讨信息的全面描述,可以有效处理研讨信息并支持共识达成,最后阐述了该模型在综合集成研讨厅中的应用。     

考虑个体累积共识贡献的犹豫模糊语言自适应共识模型

针对犹豫模糊语言信息下的多属性群决策问题,提出一种基于个体累积共识贡献的自适应共识决策模型.首先,利用犹豫模糊语言得分函数,基于经典的信息熵和相对熵理论,综合考虑同一属性下不同方案间的信息差异,以及各方案分别与正理想方案和负理想方案的信息差异,构建确定属性权重的优化模型;然后,提出个体累积共识贡献测度和全局共识测度,利用全局共识度进行共识控制,依据个体累积共识贡献度对专家权重进行自适应修正,构建一种新的犹豫模糊语言自适应共识过程.该过程的特点是对拥有较少合作的非全共识专家执行专家权重惩罚,而且专家权重的更新引起属性权重的自适应更新,反过来又影响个体共识贡献的累积.最后通过一个应急医疗设施选址的共识决策例子表明方法的可行性和有效性.     

2阶多智能体网络在切换拓扑下的群一致性

针对2阶多智能体网络的群一致性问题,提出了基于牵引控制方法的群一致性协议.考虑到网络模型具有切换拓扑结构,在模型中引入相应的虚拟领导者.对于网络中的每一个智能体,其一致性协议依赖于智能体邻居的状态及速度信息,并受到虚拟领导者的牵引控制;进一步地,来自虚拟领导者的牵引控制可以随时间发生变换.利用代数图论、线性矩阵不等式和李亚普诺夫稳定性理论,对网络进行群一致性分析,给出了切换拓扑下2阶多智能体网络达到群一致性的充分条件.最后,数值仿真验证了理论结果的有效性.     

基于重要度引导偏好识别修正的多粒度语言共识模型

研究多粒度语言偏好信息下的群体共识决策问题. 首先, 从个体和群体两个角度充分挖掘偏好信息下隐含的专家重要度信息, 基于个体一致度及个体与群体的相似度构建确定专家重要度的优化模型; 其次, 以专家重要度引导非共识偏好的识别和修正过程, 提出一种自适应的语言共识模型; 然后, 给出一种群决策方法, 确保在集结专家意见前群体达成一定程度的共识; 最后, 通过算例验证所提出方法的可行性和有效性.

     

Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information

To solve group decision-making problems we have to take in account different aspects. On the one hand, depending on the problem, we can deal with different types of information. In this way, most group decision-making problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts’ opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. On the other hand, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem and, as a result, they may present incomplete information. The aim of this paper is to present a consensus model to help experts in all phases of the consensus reaching process in group decision-making problems in an unbalanced fuzzy linguistic context with incomplete information. As part of this consensus model, we propose an iterative procedure using consistency measures to estimate the incomplete information. In addition, the consistency measures are used together with consensus measures to guided the consensus model. The main novelty of this consensus model is that it supports the management of incomplete unbalanced fuzzy linguistic information and it allows to achieve consistent solutions with a great level of agreement.     

An attitude-driven web consensus support system for heterogeneous group decision making

Consensus reaching processes are applied in group decision making problems to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been developed to facilitate consensus reaching processes. However, new trends bring diverse challenges in group decision making, such as the modelling of different types of information and of large groups of decision makers, together with their attitude to achieve agreements. These challenges require the capacity to deal with heterogenous frameworks, and the automation of consensus reaching processes by means of consensus support systems. In this paper, we propose a consensus model in which decision makers can express their opinions by using different types of information, capable of dealing with large groups of decision makers. The model incorporates the management of the group’s attitude towards consensus by means of an extension of OWA aggregation operators aimed to optimize the overall consensus process. Eventually, a novel Web-based consensus support system that automates the proposed consensus model is presented.     

Control of group of mobile autonomous agents via local strategies

This paper considers the formation control problem of multi-agent systems in a distributed fashion. Two cases of the information propagating topologies among multiple agents, characterized by graphics model, are considered. One is fixed topology. The other is switching topology which represents the limited and less reliable information exchange. The local formation control strategies established in this paper are based on a simple modification of the existing consensus control strategies. Moreover, some existing convergence conditions are shown to be a special case of our model even in the continuous-time consensus case. Therefore, the results of this paper extend the existing results about the consensus problem.     

A non-threshold consensus model based on the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making

This study proposes a non-threshold consensus model that combines the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making (MALGDM). First, the large-scale experts is classified into several clusters via the combination of the similarities of evaluation information, unit consensus cost, and adjustment willingness. Then, a more sensitive consensus measure method that combines the mean value and variance of the similarities among clusters is presented. Next, a comprehensive identification rule is put forward to determine the cluster with a low consensus level, low unit consensus cost, and high adjustment willingness for information adjustment. An optimization model that combines the minimization of the cost of the cluster and the maximization of the increase of the global consensus level is then constructed to obtain the adjusted information. Also, the adjustment willingness is considered in the constraints to limit the adjustment range. Moreover, instead of the use of a predefined threshold and a maximum number of iterations, a termination index is developed to terminate the consensus reaching process (CRP) to make the CRP more objective and rational. Finally, an application example is presented, and comparison and simulation analyses are performed to validate the feasibility and effectiveness of the proposed model.