面向对象方法OOSA,OOA,OMT的比较研究

本文进一步概括了面向对象的思想实质。接着从基本术语,开发过程及其文档、建模角度、应用特性等方面对三大流行方法ＯＯＳＡ、ＯＯＡ、ＯＭＴ进行分析比较,又归纳和总结了六种主要的面向对象方法的特点。     

飞航导弹面向对象仿真建模方法研究

该文针对面向对象Ｏ－Ｏ建模技术的特点,详细分析了飞航导弹数学模型,提取了共性部分,并加以分类。在分析导弹数学模型的基础上,建立了导弹型号库、型号子模型库、公用子模型库、以ＦｏｘＰｒｏ２．５ｂ Ｆｏｒ Ｗｉｎｄｏｗｓ为开发工具,进行了面向对象仿真建模软件设计,并利用Ｗｉｎｄｏｗｓ环境设计了一套面向对象建模的人机交互界面。     

面向对象BOM建模与设计

本文提出了物料清单ＢＯＭ（Ｂｉｌｌ ｏｆ Ｍａｔｅｒｉａｌ）面向对象建模与设计方法。零部件对象成为ＢＯＭ的设计维护基础，本文着重讨论了ＢＯＭ的面向对象模型和概念表达方法，最后探讨了面向对象ＢＯＭ（ＯＯＢＯＭ）在ＣＩＭ环境下的意义。     

面向对象分析方法综述

面向对象软件开发生命期包括：ＯＯＡ（面向对象分析），ＯＯＤ（面向对象设计）、ＯＯＩ（面向对象实现）等阶段，在考察了ＯＯＡ现状的基础上，这篇文章总结和评价了当前已有的ＯＯ分析和规格说明方法。     

OOCP∶面向对象的化工过程计算机辅助开发环境（Ⅰ）－面向对象方法建立过程系统模型

从过程系统的角度说明了面向对象概念，完成了对化工过程的多层次抽象，建立了近２０个化工类，并提出了用面向对象方法建立化工单元和过程系统面向方程模型的思想。文中介绍了化工类库的分类方法，类库定义、类库组织形式和面向对象建模机制等，可以看出，面向对象方法能快速、方便地建立新的化工单元和系统的面向方程模型，克服了传统设计方法中面向方程法模拟流程难以建模和利用已有模型产生新单元模型的缺点，为迅速、有效地建立化工模型探索了一条新途径。这也是作者提出的基于Ｗｉｎｄｏｗｓ的面向对象化工过程计算机辅助开发环境─ＯＯＣＰ的核心内容。     

责任驱动设计方法在MIS开发中的应用

本文在综合了ＲＤＤ与ＯＭＴ两种面向对象的方法优点,提出了在ＭＩＳ系统的子系统级别进行动态行为建模的思想,并运用此方法有效地进行了一企业的电缆信息管理系统改造。     

面向对象建模技术的实践与体会

面向对象建模技术（ＯＭＴ）是一种比较成熟的面向对象（ＯＯ）软件开发方法。本文结合青鸟Ⅱ型系统的需求文档分析工具（ＲＤＡＴ）的开发，描述了如何运用ＯＭＴ方法来开发具体的系统，并着重突出了实践过程及开发中的一些经验。     

VE中虚拟器件仿真与建模方法的研究*

结合实际项目的开发,采用面向对象和组件思想提出了一种面向对象和基于组件的虚拟器件仿真与建模方法。面向对象的仿真与建模方法具有良好的直观性、可扩展性和可重用性。基于组件的仿真与建模思想来源于面向对象,但与面向对象的方法相比,基于组件的仿真与建模分析的粒度较大,可重用性更好。     

面向对象软件工程开发的管理

本文基于面向对象建模技术（以下简称ＯＭＴ方法），明确了面向对象软件工程开发中各阶段的概念及任务，提出了评价各阶段产品的标准，并讨论了一种面向对象软件工程开发中的人员分工方法。     

一个用面向对象方法开发应用系统的实例

结合财政业管管理系统具体说明怎样利用面向对象的方法来分析和设计应用系统。简要地描述系统的需求,Ｃｏａｄ和Ｙｏｕｒｄｅｎ的面向对象的分析方法和面向对象的设计方法,用该方法对邮政业务管理系统ＯＯＡ模型的５个层次进行具体分析,给出了一个子系统的ＯＯＡ模型,讨论了ＯＯＤ模型的４个组成部分的设计。     

OMR抽象模型

在计算机领域反映现实世界中的事物,离不开有效模型的支持。这个模型一方面是现实世界中具体事物的抽象,另一方面又便于计算机实现。提出的OMR模型反映了现实世界的运行规律,用对象、消息和关系三要素对现实事物进行抽象和描述,是辅助计算机实现的有效工具。为了从形式化和数学化的角度描述OMR模型,又探讨了对象内Petri网和对象间Petri网,从而使OMR模型进一步丰满。     

FOM设计过程中的几个问题

FOM表是对联邦运行期间各联邦成员之间所交换信息的一种规范化描述，是提高模型之间互操作性和可重用性的关键技术之一，同时也是联邦开发的难点所在。该文首先对FOM的概念进行了讨论，分析了FOM中的对象与OOAD中的对象之间的异同。然后，我们在联邦开发实践的基础上，对FOM的开发过程进行了系统的总结，指出了在FOM开发过程中应该注意的问题。     

面向属性（对象）多粒度概念格之间的关系

受粗糙集中多粒度标记信息系统研究的启发,对于多粒度标记的形式背景,不同粒度标记下形成的概念格之间存在着一些联系,这在形式概念分析理论中属于热点研究问题。为了研究不同粒度标记下形成的概念格之间的关系,首先讨论了不同粒度标记下的生成的极值算子之间的关系;其次通过极值算子之间的关系,对不同粒度标记下形成的面向对象的概念格之间的关系进行了研究;再利用面向属性的概念格和面向对象的概念格之间的互补关系,对不同粒度标记下形成的面向属性的概念格之间的关系进行了研究,实现了在已知细粒度标记下的形式背景、粗粒度标记下的形式背景,通过细粒度标记下的知识推断出粗粒度标记下的知识;最后在此基础上提出了相应的概念格生成的方法。     

基于BHO(Browser Helper Object)的网址过滤实现

网址过滤(Filtering URL)是信息安全方的一个重要课题,研究了浏览器(IE)辅助对象BHO(Browser HelperObject)的工作机制,并通过使用BHO(Browser Helper Object)对象过滤可疑网址这个试例来全面介绍BHO(Browser Helper Object)对象组件的工作机制及使用Delphi7.0开发实现过程,同时也给出了使用BHO过滤网址的一种有效简便方法.     

Convex Object(s) Characterization and Segmentation Using Level Set Function

Journal of Mathematical Imaging and Vision - In practice, the objects of interest have some shape priors, which would be destroyed by occlusions, distortions and noises. Therefore, the...     

Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)

We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as “ambiguous” or “not ambiguous” to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid “ground truth” foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47% of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.     

Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation

We present a method to learn probabilistic object models (POMs) with minimal supervision, which exploit different visual cues and perform tasks such as classification, segmentation, and recognition. We formulate this as a structure induction and learning task and our strategy is to learn and combine elementary POMs that make use of complementary image cues. We describe a novel structure induction procedure, which uses knowledge propagation to enable POMs to provide information to other POMs and “teach them” (which greatly reduces the amount of supervision required for training and speeds up the inference). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1], [2] and use this to train a POM-mask, defined on regional features, which yields a combined POM that performs segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large data sets for classification and segmentation with comparison to other methods. Inference takes five seconds while learning takes approximately four hours. In addition, we show that the full POM is invariant to scale and rotation of the object (for learning and inference) and can learn hybrid objects classes (i.e., when there are several objects and the identity of the object in each image is unknown). Finally, we show that POMs can be used to match between different objects of the same category, and hence, enable objects recognition.     

CORBA:全面的分布式对象计算(下)

文章是［１］的续篇,讨论ＯＲＢ和对象适配器（Ｏｂｊｅｃｔ　Ａｄａｐｔｅｒ）对服务对象的管理,及ＯＲＢｓ间的互操作性问题。并指出ＣＯＲＢＡ和ＪａｖａＢｅａｎｓ结合的趋势,最后比较ＣＯＲＢＡ与ＲＭＩ、ＤＣＯＭ的性能。     

用振子神经网络实现二值图象中的多目标分割

利用基于视觉模型建立起来的局部激励、全局抑制振子神经网络 (OSNN)实现二值图象中数字及字母的目标 /背景分割 ;从理论上阐述了该网络实现目标 /背景分割的机理 ,并分析了如何合理选择参数以利于成功分割。     

Object Tracking-Based “Follow-Me” Unmanned Aerial Vehicle (UAV) System

The applications of information technology (IT) tools and techniques have, over the years, simplified complex problem solving procedures. But the power of automation is inhibited by the technicality in manning advanced equipment. To this end, tools deliberately combating this inhibition and advancing technological growth are the Unmanned Aerial Vehicles (UAVs). UAVs are rapidly taking over major industries such as logistics, security, and cinematography. Among others, this is a very efficient way of carrying out missions unconventional to humans. An application area of this technology is the local film industry which is not producing quality movies primarily due to the lack of technical know-how in utilizing these systems. This study therefore aim to devise an autonomous object tracking UAV system that would eliminate the complex procedure involved in stabilizing an aerial camera (aerial bot) midair and promote the creation of quality aerial video shooting. The study adopted Unified Modeling Language (UML) tools in modeling the system’s functionality. The traditional Server-Client model architecture was adopted. The OpenCV library employed proved highly efficient in aiding the tracking procedure. The system provided a usable web controller which provides easy interaction between the pilot and the drone. Conclusively, investments in UAVs would enhance creation of quality graphic contents.