面向对象与MVC框架的融合

随着面向对象思想的确立,使得人们认识事物的角度发生了重大变化,用面向对象的思想来分析事物,模拟人们对客观事物认识方式的反应。21世纪软件业的发展是在信息计算机的大背景下不断发展的。人们对软件功能的需求在不断变化,使得原有的软件无法维持业务功能的新需求,这使得软件的重用性和可扩展性受到严重限制,这就必然推动软件开发的革新要有新的认识,这使得面向对象的软件开发思想在软件领域不断发展。在Web软件开发中面向对象的开发思想有了很成熟的发展,特别是以java面向对象的语言为基础的软件开发,以java语言为开发的经典架构MVC的设计框架充分体现了面向对象的思想。本次就java的MVC三大框架与面向对象的思想进行深入探讨。     

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。     

基于UML的面向对象系统分析与设计

该文描述了面向对象方法的思想及主要的几种设计方法。探讨了基于UML进行面向对象的系统分析及设计思想,提出了一种实用的基于UML的分析设计过程,并论述了UML的应用及基于UML的Rational ROSE面向对象设计过程。     

面向对象方法与大学生计算机素养

20世纪90年代,面向对象技术以其显著的优势成为计算机软件领域的主流技术,该技术在软件开发中得到广泛的应用。面向对象技术不仅仅是一种编程语言,更重要的是现代软件设计主流思想,蕴涵着系统论的整体性和普遍联系的基本思想。通过面向对象方法的学习,可以提高软件从业人员的宏观思维素质,对于软件体系思想薄弱的非计算机专业的学生来说,具有重要的意义。     

对面向对象概念建模方法用于系统开发的一些思考

从方法学的角度对面向对象思想方法进行了评述,讨论了面向对象方法的局限性。论文指出面向对象思想方法并非消除系统开发的实质性困难的万能方法。作为系统集成开发策略的一方面,面向对象思想方法应该与其它方法互相协调。文章最后还讨论了面向对象方法在描述问题域时的不足。     

面向对象协同感知机制的研究与应用

为解决传统的协同感知模型不适合直接移植到分布式协同3D虚拟实验室的问题,结合分布式协同3D虚拟实验室自身的特点,提出了面向对象的协同感知模型。用面向对象的编程思想结合面向接口的编程思想实现了分布式协同3D虚拟实验室的面向对象协同感知机制。实验结果表明,面向对象的协同感知机制能使分布式协同3D虚拟实验室环境中的协同者及时、准确的感知到其它协同者的行为和场景状态的变化。     

面向对象方法与大学生计算机素养

20世纪90年代，面向对象技术以其显著的优势成为计算机软件领域的主流技术，该技术在软件开发中得到广泛的应用。面向对象技术不仅仅是一种编程语言，更重要的是现代软件设计主流思想，蕴涵着系统论的整体性和普遍联系的基本思想。通过面向对象方法的学习，可以提高软件从业人员的宏观思维素质，对于软件体系思想薄弱的非计算机专业的学生来说，具有重要的意义。     

面向对象思维引导过程中的问题求解

面向对象思想抽象性强,首次接触程序设计的学生难于理解,且面向对象程序设计的教学普遍重代码实现、轻设计思想。针对上述问题,文章分析面向对象思想的思维构建过程,以Java程序设计语言教学为例,探讨如何通过问题求解方式,引导学生运用面向对象思维分析具体问题。     

面向对象技术与可视化开发环境

本文首先阐述了面向对象的思想，并充分说明了这种思想产生的根源和它对软件开发过程的影响，接着就可视化开发和面向对象技术在Delphi集成环境中的应用作了比较详细的解说。     

论面向对象与逻辑系统的结合

本文着重讨论了面向对象的基本思想和逻辑程序设计思想的关系,比较了各自的优缺点和相互补充的可能,从新的角度论述了将面向对象程序设计与逻辑程序设计相结合的一种可行途径,最后,提出了一系列具体的实现方法。     

A complete and equal computational complexity classification of compaction and retraction to all graphs with at most four vertices and some general results

A very close relationship between the compaction, retraction, and constraint satisfaction problems has been established earlier providing evidence that it is likely to be difficult to give a complete computational complexity classification of the compaction and retraction problems for reflexive or bipartite graphs. In this paper, we give a complete computational complexity classification of the compaction and retraction problems for all graphs (including partially reflexive graphs) with four or fewer vertices. The complexity classification of both the compaction and retraction problems is found to be the same for each of these graphs. This relates to a long-standing open problem concerning the equivalence of the compaction and retraction problems. The study of the compaction and retraction problems for graphs with at most four vertices has a special interest as it covers a popular open problem in relation to the general open problem. We also give complexity results for some general graphs. The compaction and retraction problems are special graph colouring problems, and can also be viewed as partition problems with certain properties. We describe some practical applications also.     

Bi-level and Multi-Level Programming Problems: Taxonomy of Literature Review and Research Issues

This paper presents taxonomy of detailed literature reviews on bi-level programming problems (BLPPs), multi-level programming problems (MLPPs) and associated research problems, while providing detail of solution techniques at the same time. In this taxonomy of review, we classified the multi-level programming problems into two types: (i) General multi-level programming problems (MLPPs) (ii) multi-level multi-objective programming problems (ML-MOPPs) which are further sub classified based on the algorithmic and optimality studies. Bi-level programming problems (BLPPs) are considered as special cases of multi-level programming problems with two level structures. The present literature review includes approximately all prior and latest references on BLPPs, and MLPPs, related solution methodologies. The general related concepts are briefly described while associated references are included for further investigations. The aim of this paper is to provide an easy and systematic road map of currently available literature studies on BLPPs and MLPPs for future researchers.     

Robust static and dynamic output-feedback stabilization: Deterministic and stochastic perspectives

Three parallel gaps in robust feedback control theory are examined: sufficiency versus necessity, deterministic versus stochastic uncertainty modeling, and stability versus performance. Deterministic and stochastic output-feedback control problems are considered with both static and dynamic controllers. The static and dynamic robust stabilization problems involve deterministically modeled bounded but unknown measurable time-varying parameter variations, while the static and dynamic stochastic optimal control problems feature state-, control-, and measurement-dependent white noise. General sufficiency conditions for the deterministic problems are obtained using Lyapunov's direct method, while necessary conditions for the stochastic problems are derived as a consequence of minimizing a quadratic performance criterion. The sufficiency tests are then applied to the necessary conditions to determine when solutions of the stochastic optimization problems also solve the deterministic robust stability problems. As an additional application of the deterministic result, the modified Riccati equation approach of Petersen and Hollot is generalized in the static case and extended to dynamic compensation.     

Conformant plans and beyond: Principles and complexity

Conformant planning is used to refer to planning for unobservable problems whose solutions, like classical planning, are linear sequences of operators called linear plans. The term ‘conformant’ is automatically associated with both the unobservable planning model and with linear plans, mainly because the only possible solutions for unobservable problems are linear plans. In this paper we show that linear plans are not only meaningful for unobservable problems but also for partially-observable problems. In such case, the execution of a linear plan generates observations from the environment which must be collected by the agent during the execution of the plan and used at the end in order to determine whether the goal had been achieved or not; this is the typical case in problems of diagnosis in which all the actions are knowledge-gathering actions.Thus, there are substantial differences about linear plans for the case of unobservable or fully-observable problems, and for the case of partially-observable problems: while linear plans for the former model must conform with properties in state space, linear plans for partially-observable problems must conform with properties in belief space. This differences surface when the problems are allowed to express epistemic goals and conditions using modal logic, and place the plan-existence decision problem in different complexity classes.Linear plans is one extreme point in a discrete spectrum of solution forms for planning problems. The other extreme point is contingent plans in which there is a branch point for every possible observation at each time step, and thus the number of branch points is not bounded a priori. In the middle of the spectrum, there are plans with a bounded number of branch points. Thus, linear plans are plans with zero branch points and contingent plans are plans with unbounded number of branch points.In this work, we lay down foundations and principles for the general treatment of linear plans and plans of bounded branching, and provide exact complexity results for novel decision problems. We also show that linear plans for partially-observable problems are not only of theoretical interest since some challenging real-life problems can be dealt with them.     

Implicit branching and parameterized partial cover problems

Covering problems are fundamental classical problems in optimization, computer science and complexity theory. Typically an input to these problems is a family of sets over a finite universe and the goal is to cover the elements of the universe with as few sets of the family as possible. The variations of covering problems include well-known problems like Set Cover, Vertex Cover, Dominating Set and Facility Location to name a few. Recently there has been a lot of study on partial covering problems, a natural generalization of covering problems. Here, the goal is not to cover all the elements but to cover the specified number of elements with the minimum number of sets. In this paper we study partial covering problems in graphs in the realm of parameterized complexity. Classical (non-partial) version of all these problems has been intensively studied in planar graphs and in graphs excluding a fixed graph H as a minor. However, the techniques developed for parameterized version of non-partial covering problems cannot be applied directly to their partial counterparts. The approach we use, to show that various partial covering problems are fixed parameter tractable on planar graphs, graphs of bounded local treewidth and graph excluding some graph as a minor, is quite different from previously known techniques. The main idea behind our approach is the concept of implicit branching. We find implicit branching technique to be interesting on its own and believe that it can be used for some other problems.     

Extending NC and RNC algorithms

A technique is presented by which NC and RNC algorithms for some problems can be extended into NC and RNC algorithms, respectively, that solve more general parametric problems. The technique is demonstrated on explicit bounded degree circuits. Applications include parametric extensions of the shortest-path and spanning-tree problems and, in particular, the minimum-ratio-cycle problem, showing all these problems are in NC.     

Complexity of common subsequence and supersequence problems and related problems

The article examines polynomial-time and intractable longest common subsequence and subword problems and shortest common supersequence and superword problems, both old and new. The results provide a more complete complexity characterization of these problems. Some applications are discussed, as well as the dual problems of common nonsubwords, nonsuperwords, nonsubsequences, and nonsupersequences.Translated from Kibernetika, No. 5, pp. 1–13, September–October, 1989.     

Real-time optimal control and observation

A survey of the results obtained by authors and their coworkers (mainly in 2000–2003) on realization of modern principles of optimal control and observations in real time was made. Optimal control problems and the problems of observation of deterministic systems, problems under the conditions of set-membership uncertainty; perfect and imperfect measurements, feedforward, feedback, and combined loop cases, inertial and inertialess controls; and special and dynamical regulators for indirect control, linear piecewise linear, and nonlinear models are considered consecutively. The presentation focuses mainly on linear models, since the optimal control of nonlinear systems is based on linear or piecewise linear approximations of the initial model. For the realization of the positional solutions of the optimal control and observation problems, fast dual methods taking into account the dynamical nature of the problems being investigated are justified. The application of the results on optimal control in real time for the solution of classical control problems (problems of regulating, stabilizing, tracking, etc.) is discussed. The presentation is accompanied by computational examples.     

数学与策略

数学，是研究现实世界的空间形式和数量关系的科学，是处理客观问题的强有力的工具，几乎在一切自然科学领域中都起着基础性的作用。策略，是指解决问题所采取的方法。它包括解决各种问题及问题的方方面面的方法。该文讨论的策略，是指利用计算机编程解题时所采取的行之有效的方法，即编程策略。     

Disjunctive optimization,max-separable problems and extremal algebras

The paper was motivated by solution methods suggested in the literature for solving linear optimization problems over (max,+)- or (max,min)-algebras and certain class of so called max-separable optimization problems. General features of these optimization problems, which play a crucial role in the optimization methods were used to formulate a general class of optimization problems with disjunctive constraints and a max-separable objective function and suggest a solution procedure for solving such problems. Linear problems over (max,+)-algebras and the max-separable problems are contained in this general class of optimization problems as special cases.