GIS环境下的地图综合

讨论了地图自动综合的基本概念、数据模型和常用函数；试将两类不同性质的地理信息分开存储，提出了主从式地理数据库模型，试图解决新数据模型中引入的数据一致性问题；最后给出了一个用规则库来实现的地图综合方法。     

地图数据库层次—关系模型及其实现

本文讨论我们设计并实现的地图数据库管理系统ＭＯＢ的体系结构，数据模型、数据类型及其实现技术。     

适合地图数据库应用的可扩充数据模型研究

数据库地理信息系统（ＧＩＳ）的基础。复杂的ＧＩＳ应用要求它对用户定义的数据类型和操作具有扩充能力。该文针对用于ＧＩＳ的可扩充数据模型存在的问题。构造了一个适合地图数据库应用的可扩充数据模型。它以嵌套关系模型为基础。引入高阶算子定义操作。这些算子不但增强了代数操作的能力，而且利用它们能方便地扩充用户定义的几何操作、地图量算和几何变换。该模型具有较强的扩充能力，可以作主设计可扩充地图数据库系统的基础。     

基于TEN的3D GIS数据模型及其生成算法

四面体网格(简称TEN)是三维GIS中的一个重要的数据模型，文中首先提出了一种基于TEN的三维GIS数据模型；然后介绍了其中TEN的生成算法；最后本文总结了基于TEN的三维数据模型的优缺点。     

电子地图与地理信息系统

概述了地理信息系统（ＧＩＳ）的发展状况；着重论述了ＧＩＳ系统在地图管理中的重要作用，克服了传统地图管理中的种种局限性；对电子地图和传统地图的优缺点进行了比较，指出了电子地图对地图管理的优越性；给出了当前我国ＧＩＳ系统开发的几个焦点     

基于类型系统的元数据模型

本研究了程序设计语言的类型系统与数据模型的；认识到类型间关系是表示数据模型的一种。基地这一思想，提出了ＰＣＴ类型描述语言及其基于ＰＣＴ的元数据模型，以描述多种数据模型，ＰＣＴ将Ｃ＋＋类型系统与一阶谓词演算相结合，可以形式化描述特写数据模型所规定的多方面规范。     

地图数据库管理系统MDB

本文讨论我们设计并实现的地图数据库管理系统ＭＤＢ的体系结构、数据模型、数据类型及其实现技术。     

基于对象数据模型的高速公路GIS-T系统的研究与实现

针对传统GIS系统无法表达高速公路管理中对象之间的复杂关系这一问题,对GIS数据模型进行了研究,建立了基于符合OpenGIS抽象规范的几何数据模型的高速公路地理对象数据模型,实现基于对象数据模型的高速公路GIS-T系统来增强传统GIS系统的数据表达能力.能力提高后的系统,在高速公路管理者来看完全符合高速公路管理规范的,在二次开发用户或再次扩展系统功能程序员来看完全采用面向对象的,在面对地图操作的用户来看几乎表现真实世界的地理实体和逻辑对象的.     

基于组件技术的地图处理与态势信息系统

论文剖析了对地图处理和态势信息系统进行组件化的必要性,对该组件的主要功能进行了阐述。提出了面向对象整体MilitaryGraphicsystem(军事图形系统,以下简称MGS)数据模型,并针对整体MGS软件的实现在系统数据组织、存储结构与访问机制方面进行了较为深入的探讨,最后简单介绍了不同比例尺、不同地图种类的地图如何显示,对在组件上已显示的地图进行标绘并保存,以及如何分层显示实现方法,分层显示等功能实现情况。     

PowerDesigner和ModelMaker

PowerDesigner是SYBASE公司开发的数据库建模工具，它将物理数据模型，概念数据模型以及新增加的对象建模功能很好地结合在一起，并同时支持数据库建模的正向和逆向工程，能直接根据数据模型生成包括SQL SERVER在内的多种创建数据库脚本；     

K-匿名隐私保护模型中不确定性数据的建模问题研究

建模是不确定性数据管理的基础,K-匿名隐私保护模型中不确定性数据有其特殊性:它是人为泛化后的不确定性数据,泛化后的每个实例还原成泛化前元组的概率是相等的。由于其特殊性,以往针对非人为造成不确定性的数据建模方法已经不能简单地用于描述K-匿名隐私保护模型中不确定性数据。为了描述K-匿名隐私保护模型中不确定性数据,本文提出几种针对它的新建模方法:Kattr模型使用attrib-ute-ors方法来描述K-匿名数据中准标识符属性值的不确定性;Ktuple模型把K-匿名表不确定属性值看成是一个关系值,对关系值使用tuple-ors方法来描述;Kupperlower模型把K-匿名表泛化值范围分开成两个字段:上限和下限;Ktree模型根据K-匿名表是对普通表通过泛化树泛化而形成这一特性逆向拆分成树形结构。由这几种模型及它们之间的组合构成了一个描述K-匿名隐私保护模型中不确定性数据的模型空间。并且,本文讨论了模型空间里各种模型的完备性和封闭性等性质。     

A comprehensive survey on regularization strategies in machine learning

In machine learning, the model is not as complicated as possible. Good generalization ability means that the model not only performs well on the training data set, but also can make good prediction on new data. Regularization imposes a penalty on model’s complexity or smoothness, allowing for good generalization to unseen data even when training on a finite training set or with an inadequate iteration. Deep learning has developed rapidly in recent years. Then the regularization has a broader definition: regularization is a technology aimed at improving the generalization ability of a model. This paper gave a comprehensive study and a state-of-the-art review of the regularization strategies in machine learning. Then the characteristics and comparisons of regularizations were presented. In addition, it discussed how to choose a regularization for the specific task. For specific tasks, it is necessary for regularization technology to have good mathematical characteristics. Meanwhile, new regularization techniques can be constructed by extending and combining existing regularization techniques. Finally, it concluded current opportunities and challenges of regularization technologies, as well as many open concerns and research trends.     

Positional error modeling for line simplification based on automatic shape similarity analysis in GIS

Automatic generalization is a process for representing geographical objects with different degrees of detail on a digital map. The positional error for each geographical object is propagated through the process and a generalization error is also introduced by the generalization. Previous research has focused mainly on measuring the generalization error. This paper presents an analytical model for assessing the positional error in the generalized object by considering both error propagation from the original data and the generalization error. The analytical model provides a shape dissimilarity value that indicates the shape difference between the original data with a positional error and its simplified version. This model is able to objectively and automatically determine the applicability of the generalized data for further applications to geographical information system (GIS) problems. It can also deal with a large amount of data in GIS. Therefore, the analytical model presented, which provides a more comprehensive shape measure for assessing positional error in data derived from the generalization, is valuable in the development of automatic generalization.     

Cross-Domain Gated Learning for Domain Generalization

Domain generalization aims to improve the generalization capacity of a model by leveraging useful information from the multi-domain data. However, learning an effective feature representation from such multi-domain data is challenging, due to the domain shift problem. In this paper, we propose an information gating strategy, termed cross-domain gating (CDG), to address this problem. Specifically, we try to distill the domain-invariant feature by adaptively muting the domain-related activations in the feature maps. This feature distillation process prevents the network from overfitting to the domain-related detailed information, and thereby improves the generalization ability of learned feature representation. Extensive experiments are conducted on three public datasets. The experimental results show that the proposed CDG training strategy can excellently enforce the network to exploit the intrinsic features of objects from the multi-domain data, and achieve a new state-of-the-art domain generalization performance on these benchmarks.

     

Snort入侵检测系统中的规则泛化模型

针对Snort系统不能检测新的入侵行为的缺点,提出一种基于规则泛化的Snort入侵检测系统的改进模型。该模型结合Snort规则的特征和数据挖掘中的知识,提出聚类泛化和最近邻泛化两种新的规则泛化方法来改进规则,增强Snort的检测能力,从而达到识别更多入侵行为的目的。实验结果表明：在不显著增加误报率的前提下,采用规则泛化的Snort能够检测出原来系统不能发现的入侵行为,提高检测率达8.2%。     

Latent grouping models for user preference prediction

We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves predictions in the case of new users. We show that if either users and documents or both are new, two-way generalization becomes necessary. We demonstrate the benefits of grouping of users, grouping of documents, and two-way grouping, with artificial data and in two case studies with real data. We have introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare the model against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure. We compute the posterior of both models by Gibbs sampling. The Two-Way Model predicts relevance more accurately when the target consists of both new documents and new users. The reason is that generalization over documents becomes beneficial for new documents and at the same time generalization over users is needed for new users.     

Design of stochastic discrete time linear optimal regulators Part I. Relationship between control laws based on a time series approach and a state space approach

Distinct approaches to sampled data control system design use either a state space model or a ‘ controlled autoregressive moving average ’ (CARMA) model, sometimes known as Åström's representation, One reason for the current interest in the CARMA model is that it is a useful basis for self-tuning controllers as its parameters can be readily estimated on-line. Moreover, simple transfer function controllers can be derived using κ-step-ahead prediction theory. On the other hand, these controllers can be interpreted as minimizing a single stage cost function in state space terms, and the corresponding performance can sometimes be poor. This paper explores the relationship between the κ-stop-ahead prediction approach and the state space approach, and is a generalization of the earlier work of Caines to include control weighting and time delay on the control. Two forms of state space model are used (‘ explicit ’ and ‘ implicit’ time delay models) and a new representation of the steady state Kalman filter is shown to be required in the generalization ; this filter is put into a computationally convenient form. The technical machinery created in this paper allows for a straightforward generalization to the control of systems described by CARMA models by minimizing an N-stage cost function ; this generalization will be discussed in paper II     

基于二代小波变换的信号去噪及其软测量建模

化工生产过程中采集到的数据信号通常具有随机性和非平稳性,附加了各种噪声,以至于影响数据建模的拟合效果和泛化性能.本文基于二代小波分析的特点,提出了一种对信号数据进行小波变换阈值去噪的方法.该方法可去除大部分高频随机噪声,提取真实信号,进而提高数据的置信度.将该方法与支持向量机相结合并应用于双酚A反应过程质量指标软测量模型中.仿真结果表明,该方法能有效恢复数据的真实性,提高数据建模的拟合精度与泛化性能.     

变三角形滤波法一种新的图形概括模型

简要介绍了两种传统的图形概括模型的基本原理和方法,并针对它们所存在的一些缺陷,结合计算机制图综合的特点,提出了一种新的图形概括模型－变三角形滤波法。该模型易于理解,操作简便,可做为一个实用的制图综合数学模型。     

基于结构误差的图卷积网络

针对图卷积网络选取交叉熵作为损失函数在小样本数据集上可能导致模型训练过拟合、模型泛化能力不强等问题,提出了基于结构误差的图卷积网络。将改进的基于结构误差的支持向量机(support vector machine, SVM)作为图卷积网络的分类器,能够降低模型过拟合的风险。在SVM的泛化误差理论基础上,对SVM的损失函数进行改进,所提出的方法在最大化异类样本间隔的同时限制同类样本间的间隔,提升了模型的泛化能力。首先计算特征空间中特征向量到中心点的平均距离,由它近似替换最小包含球的半径,然后由新的损失函数指导模型的学习。在基于骨骼的行为识别领域的NTU RGB+D60和NTU RGB+D120数据集上的实验证明,相比于传统的图卷积网络模型,所提出的方法能够明显提升识别准确率并且具有更好的泛化性能。