逻辑推理机及其并行性的开发

逻辑推理机的研制是第五代计算机发展的主要方向之一.其目标是设计高性能的并行逻辑推理机.本文介绍了这一崭新的非冯·诺依曼型计算机系统的主要特点.以PSI机为例讨论了串行推理机的结构,并对逻辑推理机中固有的"与"并行性、"或"并行性、"合一"并行性、"流"并行性及搜索并行性进行了分析,通过DADO PROLOG机器、Conery-Kibler PROLOG并行解释程序、并发PROLOG和PARLOG语言的典型实例分析了如何从逻辑程序设计语言、逻辑推理机体系结构及机器硬件实现三个层次来开发上述并行性.     

P—AND并行和无冲突约束技术

本文给出了P-AND并行的语义和从AND并行到P_AND并行的转换算法——切片算法,提出了用P-AND并行代替AND并行以解决其约束冲突问题的方法.这一方法已在PARLOG顺序编译系统中采用,取得了良好的效果.     

非阻塞监控器设计中的语言优化方法

本文研究在设计非阻塞监控器时所涉及到的语言的优化方法，首先给出了Ｌｍ（Ｇ）闭语言的计算公式，然后提出了能控Ｌｍ（Ｇ）闭子语言的逐级优化途径，对于目前获得的部分优化语言的封闭解进行了总结，并对各语言类之间的关系进行了分析。     

C语言教学探讨

现在许多高校的计算机基础专业课使用的都是《C语言程序设计》,C语言的讲授的重点不是掌握语言的语法、语句,更是一种编程思想。如何在有限的教学时间内教授好C语言程序设计,使学生能够更好的掌握C语言的精髓,是C语言教学工作者关系的问题。本文从明确课程目标,实施教学改革,培养学生思维模式、一题多解、调动学生学习积极性等多方面论述了C语言教学的探讨,以此提高C语言的教学的有效性。     

基于MATLAB的静态场边值问题有限差分法的研究

有限差分法是求解静态场边值问题的一个非常有效的数值解法,它是将求解区域划分为有限个网格点,用一组差分方程代替原来一组微分方程.该方法求得的是近似解而不是精确解.求解区域内网格划分大小关系着计算的精确度,网格尺寸划分越小,计算精确度越高;反之,精确度越低.本文运用MATLAB语言使得有限差分法求解区域内电势分布的解法变得简单、易行,摆脱了传统方法使用C语言较复杂的缺陷.通过仿真验证了算法和程序的有效性.     

Optimized Parallel Execution of Declarative Programs on Distributed Memory Multiprocessors

In this paper,we focus on the compiling implementation of parlalel logic language PARLOG and functional language ML on distributed memory multiprocessors.Under the graph rewriting framework, a Heterogeneous Parallel Graph Rewriting Execution Model(HPGREM)is presented firstly.Then based on HPGREM,a parallel abstact machine PAM/TGR is described.Furthermore,several optimizing compilation schemes for executing declarative programs on transputer array are proposed.The performance statistics on transputer array demonstrate the effectiveness of our model,parallel abstract machine,optimizing compilation strategies and compiler.     

一种基于TMS320 C6713的捷联惯导系统设计

为适应目前捷联惯性导航系统（ SINS）实时性好、速度快、精度高、小型化、低功耗发展需求,设计一种DSP＋FPGA的捷联惯导系统平台,采用FPGA完成传感器数据采集与控制;采用高性能TMS320C6713 DSP为核心处理器完成航姿解算;介绍了FPGA与DSP数据交互关系;DSP程序采用C语言和汇编语言编写,FPGA设计采用VHDL语言描述;实验证明：设计可行,姿态角误差在0.05°范围内,精度符合设计要求。     

用C语言实现微分运算精确解

用C语言实现微分运算精确解的算法和程序结构,并给出了实例。     

基于线性权重最优支配的高维多目标优化算法

在高维多目标优化问题中,Pareto支配关系存在非支配解随优化目标数增加呈指数级增长和种群选择压力下降等问题。针对这些问题,基于线性权重聚合函数和支配关系两种比较多目标解方法的思想,提出一种线性权重最优支配关系（LWM-dominance）,并理论证明了LWM非支配解集是Pareto非支配解集的子集,同时保留了种群中重要的角解。进一步地,基于LWM支配关系,实现了一个高维多目标进化优化算法,基于该算法的实验验证了LWM支配关系的性质。在随机解空间中的实验结果表明LWM支配关系适用于5~15个目标的高维多目标优化问题,通过DTLZ1~DTLZ7高维多目标优化问题进化过程中LWM非支配解集与Pareto非支配解集规模的对比实验,结果表明优化目标数为10和15时非支配解的比例平均下降了约17%。     

三维实体布局的八叉树语言及优化算法

本文设计了一个八叉树操作语言，并利用该语言实现了三维实体布局的优化算法，该算法能从任一初始布局可行解迅速收敛到一个局部最优解上，从而明显改善初始布局方案。文章最后给出了一个简单的应用实例。     

An abstract machine for the implementation of PARLOG on uniprocessors

An abstract machine that supports the parallel logic programming language PARLOG is presented. This abstract machine is designed for the efficient execution of PARLOG on conventional uniprocessors and is thus named the Sequential PARLOG Machine (SPM). The machine’s architecture and instruction set are described and the principles of compilation of PARLOG programs to sequences of abstract machine instructions explained. The machine supports systems programming in PARLOG and in particular PARLOG’s powerful control metacall, which permits programs to initiate, monitor and control subcomputations.     

PARLOG and its applications

The key concepts of the parallel logic programming language PARLOG are introduced by comparing the language with Prolog. Some familiarity with Prolog and with the concepts of logic programming is assumed. Two major application areas of PARLOG, systems programming and object-oriented programming, are illustrated. Other applications are briefly surveyed     

A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem

Multi-objective shortest path (MOSP) problem aims to find the shortest path between a pair of source and a destination nodes in a network. This paper presents a stochastic evolution (StocE) algorithm for solving the MOSP problem. The proposed algorithm is a single-solution-based evolutionary algorithm (EA) with an archive for storing several non-dominant solutions. The solution quality of the proposed algorithm is comparable to the established population-based EAs. In StocE, the solution replaces its bad characteristics as the generations evolve. In the proposed algorithm, different sub-paths are the characteristics of the solution. Using the proposed perturb operation, it eliminates the bad sub-paths from generation to generation. The experiments were conducted on huge real road networks. The proposed algorithm is comparable to well-known single-solution and population-based EAs. The single-solution-based EAs are memory efficient, whereas, the population-based EAs are known for their good solution quality. The performance measures were the solution quality, speed and memory consumption, assessed by the hypervolume (HV) metric, total number of evaluations and memory requirements in megabytes. The HV metric of the proposed algorithm is superior to that of the existing single-solution and population-based EAs. The memory requirements of the proposed algorithm is at least half than the EAs delivering similar solution quality. The proposed algorithms also executes more rapidly than the existing single-solution-based algorithms. The experimental results show that the proposed algorithm is suitable for solving MOSP problems in embedded systems.     

Heuristic search in PARLOG using replicated worker style parallelism

Most concurrent logic programming languages hide the distribution of processes among physical processors from the programmer. For parallel applications based on heuristic search, however, it is important for the programmer to accurately control this distribution. With such applications, an inferior distribution strategy easily leads to enormous search overheads, thus decreasing speedup on parallel hardware.

To solve this problem, various language extensions for concurrent logic languages have been proposed, such as mapping notations and priorities. We present an alternative approach that does not require any new language features. Our solution is to use the replicated workers paradigm in a concurrent logic language (PARLOG). This paradigm has thus far mainly been used in parallel procedural languages, such as Linda and Orca. We show that it is just as useful for logic languages. We have implemented two parallel applications, the Traveling Salesman Problem and alpha-beta search, using this approach. Also, we have done some performance measurements of these programs on a multiprocessor. These experiments show that significant speedups can be obtained in this way.     

Memory-saving memetic computing for path-following mobile robots

In this paper, a recently proposed single-solution memetic computing optimization method, namely three stage optimization memetic exploration (3SOME), is used to implement a self-tuning PID controller on board of a mobile robot. More specifically, the optimal PID parameters minimizing a measure of the following error on a path-following operation are found, in real-time, during the execution of the control loop. The proposed approach separates the control and the optimization tasks, and uses simple operating system primitives to share data. The system is able to react to modifications of the trajectory, thus endowing the robot with intelligent learning and self-configuration capabilities. A popular commercial robotic tool, i.e. the Lego Mindstorms robot, has been used for testing and implementing this system. Tests have been performed both in simulations and in a real Lego robot. Experimental results show that, compared to other online optimization techniques and to empiric PID tuning procedures, 3SOME guarantees a robust and efficient control behaviour, thus representing a valid alternative for self-tuning control systems.     

Granularity Analysis for Exploiting Adaptive Parallelism of Declarative Programs on Multiprocessors

1IntroductionAutomaticparallelexecutionofdeclarativelanguageprograms(e.g.functionprogramsandlogicprograms)isattractive,asitmakestheuseofparallelcomputersveryeasy,andtheprogrammerneednotbeconcernedwiththespecificsoftheunderlyingparallelarchitecture.However,ifseveralprocessorsareexecutingconcurrently,exploitingadaptiveparallelismishardduetonon-determinismoftaskgranularityanddatadependenciesamongtasks.TheearlysolutionproposedbyConeryandKibler[2]usesanorderingalgorithmtodeterminedependenciesatrun…     

An agent based approach for the implementation of cooperative proactive S-Metaheuristics

This paper introduces several cooperative proactive S-Metaheuristics, i.e. single-solution based metaheuristics, which are implemented taking advantage of two singular characteristics of the agent paradigm: proactivity and cooperation. Proactivity is applied to improve traditional versions of Threshold Accepting and Great Deluge Algorithm metaheuristics. This approach follows previous work for the definition of proactive versions of the Record-to-Record Travel and Local Search metaheuristics. Proactive metaheuristics are implemented as agents that cooperate in the environment of the optimization process with the goal of avoiding stagnation in local optima by adjusting their parameters. Based on the environmental information about previous solutions, the proactive adjustment of the parameters is focused on keeping a minimal level of acceptance for the new solutions. In addition, simple forms of cooperation by competition are used to develop cooperative metaheuristics based on the combination of the four proactive metaheuristics. The proposed metaheuristics have been validated through experimentation with 28 benchmark functions on binary strings, and several instances of knapsack problems and travelling salesman problems.