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A parallel‐processor computer contains multiple CPUs that share such system resources as memory and disk storage. A parallel‐processor computer is expanded not by adding another computer, but by plugging another CPU into the computer. This technology offers expandability, compact size, high performance, high reliability, and moderate cost. The Sequent Balance Parallel‐Processor Computer is described in some detail. A fully configured Balance 21000 can execute 21 MIPS (million instructions per second). It implements the UNIX operating system, which has been widely adopted. As a result, many software packages for word processing and other applications are available from third‐party vendors. Performance tests conducted by CLSI, Inc. indicate that twenty concurrent users on a parallel‐processor system can perform CPU‐intense functions up to seven times faster than on a single‐processor system.

Contends that parallel production sub‐systems may be cost effective and, in some cases, they are the only technically feasible way to improve output quality. Operating in either a standby‐redundancy or an ongoing capacity, parallel production sub‐systems are likely to be most cost effective when placed in upstream operations, where the leverage effect on output quality is considerably higher. Although typical research in parallel systems assumes a known distribution to estimate the output reliability of the parallel configuration, explains how this study used a simulated production environment to develop a regression model for assigning parallel system components by monitoring their actual past performance, and was therefore distribution free. Applying variables monitored during a previous production run in which quality is measured in a binary manner (i.e. either as acceptable or unacceptable), the model was used to determine optimal pairs of parallel subsystems. Claims that this matching model was about 2.5 times more accurate than Markov analysis in predicting the output quality of a given pair of parallel systems. The inclusion of an additional variable in the regression resulted in the model explaining about 75 per cent of the output variability of the parallel configurations and thus could potentially predict quality in lieu of direct inspection.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

Joint reliability importance (JRI) of components is the effect of a change of their reliability on the system reliability. The authors consider two coherent multi-component systems – a series-in-parallel (series subsystems arranged in parallel) and a parallel-in-series (parallel subsystems arranged in series) system. It is assumed that all the components in the subsystems are independent but not identically distributed. The subsystems do not have any component in common. The paper aims to discuss these issues.

For both the systems, the expressions for the JRI of two or more components are derived. The results are extended to include subsystems where some of the components are replicated.

The findings are illustrated by considering bridge structure as a series-in-parallel system wherein some of the components are repeated in different subsystems. Numerical results have also been provided for a series-in-parallel system with unreplicated components. JRI for various combinations of components for both the illustrations are given through tables or figures.

Chang and Jan (2006) and Gao et al. (2007) found the JRI of two components of series-in-parallel system when the components are identical and independently distributed. The authors derive the JRI of m=2 components for series-in-parallel and parallel-in-series systems when components are independent but need not be identically distributed. Expressions are obtained for the above-mentioned systems with replicated and unreplicated components in different subsystems. These results will be useful in analyzing the joint effect of reliability of several components on the system reliability. This will be of value to design engineers for designing systems that function more effectively and for a longer duration.

This paper considers a series‐parallel and a parallel‐series system, and investigates theoretically their fundamental characteristics: it is shown that the reliability of the series‐parallel system with the same number of series and parallel tends to one as its number goes to infinity. It would be of great interest that the golden ratio plays a role in analyzing two systems. Further, an optimal number of units for a series‐parallel system with complexity is derived.

The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for text retrieval. We analyse parallel IR systems using a classification defined by Rasmussen and describe some parallel IR systems. We give a description of the retrieval models used in parallel information processing. We describe areas of research which we believe are needed.

The purpose of this paper is to build a seven‐degrees of freedom (DOF) parallel‐serial robot system which has the advantage of mechanical novelty and simplicity compared with the existing platforms, and to share the experience of converting a popular motion base to an industrial robot for use in full‐mission tank training processes of three armored arms.

By studying the concept of the robot system, a novel parallel‐serial robot with seven DOF driven by electrical servo motors is built. And the transmission modules and Hooke joints are explored and designed in detail. Then the inverse kinematics based on coupling compensation and time‐jerk synthetic optimization methods for trajectory planning of the simulator are presented and further discussed in order to satisfy the requirements of high stability and perfect performance. In advance, the feasibility and applicability of this triune parallel‐serial robot system are verified.

A prototyped test shows that the performance of the system is of a satisfaction with real‐time tracking any trajectories given by the visual system smoothly. Finally, the characteristics of the robot system are realized and verified by experiments and an industrial application.

The triune full‐mission tank training simulator developed in this paper has been used in the military industry and it has a great potential application.

This successful usage of the novel and simple parallel robot system in the military industry expands the range of its applications in real‐life task more operators training. And the proposal methods of inverse kinematics based on coupling compensation and trajectory planning enhanced the theoretical research of the parallel robot.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this paper is to research the impact of hybrid series‐parallel and parallel‐series system configurations on system performances based on system reliability and to develop a configuration model to meet the requirement of reconfigurable manufacturing system (RMS).

Based on the criterion of system reliability, a RMS configuration model is presented – the hybrid parallel‐series model with waiting system characteristics. The configuration model is evaluated from reliability, productivity, and cost by combining system engineering theory, Boolean algebra methodology with statistical analysis theory. The model reliability has been used to ameliorate by adopting the integrated algorithm based on Shrama and Misra optimization algorithm.

The need for application of this method and model – some constraints must be limited, the hybrid parallel‐series configuration is superior and the integrated algorithm is effective to RMS system configuration.

Cost constraints, equipment weight constraints, and function independency of equipment are main limitations.

The model and method have been used to ameliorate the reconfigurable automobile parts product line in SH automobile motor company of Shanghai. The operation result illustrates the validity of this configuration model and algorithm.

The new RMSs configuration model has been proposed. The new algorithm is proposed to ameliorate and optimize a reconfigurable product line with the integrated algorithm based on Shrama and Misra algorithm. The actual running effect is significant.