条材优化下料管理软件的研制与开发

本文介绍了一种实用管理软件的研制与开发的步骤 ,主要适用于制定和规划条材零件下料方案的工作。在此软件的计算下料方案模块内引进了单纯形算法 ,计算思路清晰 ,程序性能稳定。同时还附加了一些与制定条材零件下料方案相关的统计、查询功能 ,因此这套软件有很高的实用及推广价值     

条材优化下料管理软件的研制与开发

本文介绍了一种实用管理软件的研制与开发的步骤,主要适用于制定和规划条材零件下料方案的工作。在此软件的计算下料方案模块内引进了单纯形算法,计算思路清晰,程序性能稳定。同时还附加了一些与制定条材零件下料方案相关的统计、查询功能,因此这套软件有很高的实用及推广价值。     

离散小波变换算法剖析及其通用程序实现

针对小波变换工程应用软件开发需要,结合Mallat算法原理分析介绍了离散小波变换的主要功能步骤以及程序设计技术的关键问题.算法采用Delphi语言实现,大量数据验算表明,程序运算中间及最终结果和用Matlab小波工具箱编写的小波变换程序执行情况完全一致.主要算法均以子函数形式给出,便于研究人员把在Matlab中开发的小波变换应用算法成果转化为其它高级语言程序,构建独立的专用软件系统.     

用Delphi实现基于SAPI的电脑语音控制应用程序

介绍了语音功能的基本概念,讨论了开发具有语音控制功能应用程序的条件和步骤。并用Delphi实现了一个基于微软SAPI的语音控制应用程序,此程序可以用方便的扩展以实现更多语音控制功能。     

最短路径动态规划问题及C语言实现探讨

动态规划算法是一种研究多阶段决策问题的算法.用动态规划方法求最短路问题,要求所求问题具有明显的阶段。该文以动态规划理论为指导,研究了动态规划算法求解最短路径的基本原理及步骤,编写了基于动态规划算法的C语言程序,辅助完成最短路径的求解。     

将C算法转换为Verilog实现的一种方法

硬件设计借鉴软件设计的经验意义重大。首先简要介绍了状态机理论,并给出了用硬件来实现程序算法的步骤以及状态图的化简原则,然后列举了一个求最大公因数的例子来详细阐述这种方法以及实现步骤。给出的方法,通用性强,方便借鉴成熟的软件设计技术,可以大大提升数字系统的设计效率。     

大规模目标规划的算法及其实现

目标规划是在线性规划基础上发展起来的一种多目标规划方法,适合于解决有多重矛盾的、没有统一度量单位的多目标决策问題。开发大规模目标规划计算软件系统是现代经济管理决策分析的需要,是提高计算机利用率的重要途径。通过对国内外这类算法及软件的研究,认为在新算法不断涌现的今天,单纯形法对于目标规划仍具有特殊的意义。现有的目标规划单纯形算法不能适应大规模求解,对约束条件上百的问题就无能为力。因此,导出目标规划的修正单纯形算法,给出用它改进SANM·LEE程序的实验结果。经过改进的算法已解出约束条件二百左右的题目。进一步扩大解题规模还要在利用稀疏矩阵的特性和控制舍入误差上采用更复杂的技术。对于在这方面比较成熟和实用的两种大规模线性规划算法——Forrest & Tomlin算法和Bartel & Golub算法进行了讨论。在本文导出的目标规划修正单纯形算法基础上,可以直接把它们引入到目标规划中来。以IBM4300系列机和IBM PC/XT机为主要对象,探讨了以上算法计算机实现的必要条件和某些环节。提出交互作用大规模目标规划软件系统的计算机实现初步设想。     

确定单峰搜索区间的新算法-前进法

现有的一维寻优算法是以已知目标函数的单峰区间为前提的 ,而如何由变量的取值区间确定目标函数的所有单峰区间却很少有优化算法涉及。为了解决这一问题 ,提出了前进法 ,详细介绍了其基本原理和搜索步骤并绘制了程序流程图。最后 ,用一个简单的双峰函数检验了所提出的算法     

用VB开发WinCC的OPC客户机

主要介绍了如何用VB 6.0开发WinCC的OPC客户机程序,就具体实例来阐述OPC的设计方法、步骤及其应用.实现了上位机WinCC对串行设备的远程控制.该程序具有界面友好,人机交互功能方便;数据传输速率快,安全可靠性高的优点.在工业自动化中有很强的通用性和实用性.     

Pro/ENGINEER软件的二次开发技术

对 Ｐｒｏ／ＥＮＧＩＮＥＥＲ软件的用户化工具箱 Ｐｒｏ／ＴＯＯＬＫＩＴ的工作方式、接口技术及程序开发步骤进行了介绍,以方便Ｐｒｏ／ＥＮＧＩＮＥＥＲ软件的二次开发应用。     

Graphical method to solve combinatorial optimization problems

Proposed was a graphical method to solve decomposable problems of combinatorial optimization with the of use the Bellman optimality principle. In distinction to the dynamic programming algorithms based on the same principle, the graphical algorithm considers all possible system states by groups and not separately. This becomes possible if one takes into account the analytical form of the objective function, that is, handles the function “graph” and transforms it analytically at each stage. The graphical method enables one to reduce running time of solution of some problems and construct efficient approximation schemes. The results of numerical experiments corroborate efficiency of the of the graphical method.     

An Integer Linear Programming Problem with Multi-Criteria and Multi-Constraint Levels: a Branch-and-Partition Algorithm

In this paper, we propose a branch-and-partition algorithm to solve the integer linear programming problem with multi-criteria and multi-constraint levels (MC-ILP). The procedure begins with the relaxation problem that is formed by ignoring the integer restrictions. In this branch-and-partition procedure, an MC linear programming problem is adopted by adding a restriction according to a basic decision variable that is not integer. Then the MC-simplex method is applied to locate the set of all potential solutions over possible changes of the objective coefficient parameter and the constraint parameter for a regular MC linear programming problem. We use parameter partition to divide the (λ, γ) space for integer solutions of MC problem. The branch-and-partition procedure terminates when every potential basis for the relaxation problem is a potential basis for the MC-ILP problem. A numerical example is used to demonstrate the proposed algorithm in solving the MC-ILP problems. The comparison study and discussion on the applicability of the proposed method are also provided.     

A generalized scheme for mapping parallel algorithms

A generalized mapping strategy that uses a combination of graph theory, mathematical programming, and heuristics is proposed. The authors use the knowledge from the given algorithm and the architecture to guide the mapping. The approach begins with a graphical representation of the parallel algorithm (problem graph) and the parallel computer (host graph). Using these representations, the authors generate a new graphical representation (extended host graph) on which the problem graph is mapped. An accurate characterization of the communication overhead is used in the objective functions to evaluate the optimality of the mapping. An efficient mapping scheme is developed which uses two levels of optimization procedures. The objective functions include minimizing the communication overhead and minimizing the total execution time which includes both computation and communication times. The mapping scheme is tested by simulation and further confirmed by mapping a real world application onto actual distributed environments     

An interactive MOLP procedure using epsilon-constraints

Interactive multiobjective programming seeks to aid decision making in complex problems where it is difficult to explicitly state decision maker utility. A decision making aid is presented which uses a controlled pattern of objective attainments to generate new alternatives for decision maker selection. This procedure follows the concept of Steuer's algorithm, but avoids the need for filtering by use of constraints on objective attainment. In addition, the technique is not limited to original model corner points. The overall system seeks to obtain the benefits of Steuer's method, but requires only standard linear programming code, and adds the ability to identify improved solutions relative to Steuer's method when nonlinear utility exists.     

Genetic based fuzzy goal programming for multiobjective chance constrained programming problems with continuous random variables

Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous random variables in the objective functions and in chance constraints. The fuzzy goal programming formulation of the problem is developed first using the stochastic simulation-based genetic algorithm. Without deriving the deterministic equivalent, chance constraints are used within the genetic process and their feasibilities are checked by the stochastic simulation technique. The problem is then reduced to an ordinary chance constrained programming problem. Again using the stochastic simulation-based genetic algorithm, the highest membership value of each of the membership goal is achieved and thereby the most satisfactory solution is obtained. The proposed procedure is illustrated by a numerical example.     

A new effective dynamic program for an investment optimization problem

After a series of publications of T.E. O’Neil et al. (e.g. in 2010), dynamic programming seems to be the most promising way to solve knapsack problems. Some techniques are known to make dynamic programming algorithms (DPA) faster. One of them is the graphical method that deals with piecewise linear Bellman functions. For some problems, it was previously shown that the graphical algorithm has a smaller running time in comparison with the classical DPA and also some other advantages. In this paper, an exact graphical algorithm (GrA) and a fully polynomial-time approximation scheme based on it are presented for an investment optimization problem having the best known running time. The algorithms are based on new Bellman functional equations and a new way of implementing the GrA.     

ILAN Intelligent Local Area Network Planning Software

The objective of this research is to develop an analysis tool for evaluating a token bus topology, supporting the Manufacturing Automation Protocol (MAP). Furthermore this research describes an easy-to-use graphical interface for a complex simulation program. Specifically, a graphical user interface for MAP network simulation program is illustrated. Simulation of MAP sub-networks interconnected by bridges can be performed without further programming. This software, called ILAN, features a unique graphical user interface with message definition facility and an error-checking inference engine which advises users of any instances of duplicated or omitted information. ILAN's problem-definition phase is simplified by implementing a single graphical user interface for all inputs.

Two main procedures within ILAN consist of a local area network simulation and an intelligent graphical interface with a unique graphical message definition facility. The simulation procedure includes the ability to model different numbers of multiple MAP sub-networks interconnected by bridges and different numbers of associations between stations using the token bus protocol. Higher layers in the protocol stacks are modeled as queues having variable delays for different messages at different stations. In order to aid users with the difficulty of inputting parameters needed to simulate complex MAP networks, a graphical user interface is developed. By means of this graphical interface, users can describe the MAP hardware configurations as well as message definitions to be simulated. Sequences of messages to be transmitted can be displayed on the screen as users describe them.     

Improving probabilistic inference in graphical models with determinism and cycles

Many important real-world applications of machine learning, statistical physics, constraint programming and information theory can be formulated using graphical models that involve determinism and cycles. Accurate and efficient inference and training of such graphical models remains a key challenge. Markov logic networks (MLNs) have recently emerged as a popular framework for expressing a number of problems which exhibit these properties. While loopy belief propagation (LBP) can be an effective solution in some cases; unfortunately, when both determinism and cycles are present, LBP frequently fails to converge or converges to inaccurate results. As such, sampling based algorithms have been found to be more effective and are more popular for general inference tasks in MLNs. In this paper, we introduce Generalized arc-consistency Expectation Maximization Message-Passing (GEM-MP), a novel message-passing approach to inference in an extended factor graph that combines constraint programming techniques with variational methods. We focus our experiments on Markov logic and Ising models but the method is applicable to graphical models in general. In contrast to LBP, GEM-MP formulates the message-passing structure as steps of variational expectation maximization. Moreover, in the algorithm we leverage the local structures in the factor graph by using generalized arc consistency when performing a variational mean-field approximation. Thus each such update increases a lower bound on the model evidence. Our experiments on Ising grids, entity resolution and link prediction problems demonstrate the accuracy and convergence of GEM-MP over existing state-of-the-art inference algorithms such as MC-SAT, LBP, and Gibbs sampling, as well as convergent message passing algorithms such as the concave–convex procedure, residual BP, and the L2-convex method.     

DPOS: A metalanguage and programming environment for parallel processing

This paper describes and illustrates a structured programming metalanguage (DPOS) and graphical programming environment for generating and debugging high-level distributed MIMD parallel programs. DPOS introduces an innovative message-passing model and also recursive graphical definition of parallel process networks. It also provides programming and debugging at the meta language level that is portable across implementation languages. The initial development focus of DPOS is to provide a parallel development system for Lisp-based, symbolic and artificial intelligence programs as part of the MAYFLY parallel processing project. The DPOS environment also generates source code and provides a simulation system for graphical debugging and animation of the programs in graph form.