高速贴片机优化软件的设计与开发

SMT生产线要达到最大的产量,必须要考虑生产线的效率。贴片机是SMT生产线中的关键设备,因此提高贴片机的生产效率具有十分重要的意义。本文以松下贴片机为例,介绍了贴片机程序优化软件的设计与开发中的思想、方法和经验,希望对从事CAM软件研究的工程人员有一定帮助。     

宝浪油田宝北区块低渗透油藏层系优化研究

针对宝浪油田宝北区块低孔低渗油藏地质特征以及油层层数多、分布井段长、部分零散层动用难度大等问题 ,研究了宝北区有效厚度、油层层数、单井产能及单井控制地质储量等层系划分与组合的技术经济界限 ,并在此基础上进行了宝北区层系划分与组合优化论证 ,最终优选出宝北区开发层系     

炼油厂成品油罐区调度优化问题研究

研究了以集合论为基础的调度优化模型。给出了该离散型优化问题的求解算法。根据优化结果，在G2实时智能平台上开发了包含监视子系统和调度子系统的调度支持系统。在中国石化股份有限公司长岭分公司的试用结果表明，该调度系统可以提高炼油厂的生产效率，从而提高炼油厂的经济效益。     

半合成发动机油基础油调配的仿真研究

采用矿物油、聚α-烯烃、多元醇酯和高压加氢BS光亮油调配半合成发动机油基础油,并测定不同配比下基础油的油膜强度、摩擦系数和粘度指数,通过双隐层BP神经网络对基础油调配进行仿真研究,建立了半合成发动机油基础油调配配方优化模型,经过优化训练后,最终得出综合性能优良的配方组合。按49.9957∶4.3693∶42.4578∶3.1772的配比调配基础油,可以使基础油的油膜强度、摩擦系数和粘度指数分别达到56 kg,0.065和112,经实验后仿真结果与实验结果(56 kg;0.065598;112)极其吻合。     

换热器设计中适宜流体用量的确定

利用换热器的操作线来分析讨论流体用量对传热过程的操作费用和设备费用的影响，找出了换热器设计和选型中适宜流体用量的优化确定方法，简便，快捷，具有一定的适用性。     

亚麻织物无醛免烫整理工艺研究

采用多元羧酸聚合型无醛免烫整理剂处理亚麻织物，确立了整理剂、催化剂、添加剂用量等最佳工艺条件，整理后亚麻织物的免烫性能得到了明显的改善。     

On the determination of optimal transition profile of a stepped bar in tension or in torsion by BEM

The objective is to have uniformly distributed tangential stresses on the transition profile of a stepped bar subjected to tensile and torsional loading using an axisymmetric boundary element formulation. The transition curve is represented by the Langragian interpolation polynomial with progressive degrees to avoid shape distortion during the optimization procedure. The calculated result is compared with that obtained by fluid dynamics given in the literature. It is seen that the transition curve obtained in this paper is reasonable.     

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods. Recommended by: Patrick Valduriez     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.