Static fatigue in ceramic materials: influences of an intergranular glassy phase and fracture toughness

Static fatigue behaviour in various kinds of non-transforming ceramics has been investigated. It was found that static fatigue is closely related to the presence of a glassy phase between adjacent grains, as well as fracture toughness. Non-oxide ceramics, such as reaction-bonded silicon nitride which scarcely contains the glassy phase (group I), are insensitive to static fatigue, whereas non-transformation oxide ceramics, like alumina and non-oxide ceramics such as silicon nitride which contains the glassy phase (group II), are sensitive to static fatigue. However, static fatigue behaviour in the materials of group II also depends strongly on fracture toughness. K _IC. Namely, fatigue parameter n increases linearly as K _IC increases. From such a dependence the life time relation in the materials of group II is proposed as in terms of applied stress _S and K _IC.     

Linearity Analysis of Concurrent Logic Programs

Automatic memory management and the hiding of the notion of pointers are the prominent features of symbolic processing languages. They make programming easy and guarantee the safety of memory references. For the memory management of linked data structures, copying garbage collection is most widely used because of its simplicity and desirable properties. However, if certain properties about runtime storage allocation and the behavior of pointers can be obtaind by static analysis, a compiler may be able to generate object code closer to that of procedural programs. In the fields of parallel, distributed and real-time computation, it is highly desirable to be able to identify data structures in a program that can be managed without using garbage collection. To this end, this paper proposes a framework of linearity analysis for a concurrent logic language Moded Flat GHC, and proves its basic property. The purpose of linearity analysis is to distinguish between fragments of data structures that may be referenced by two or more pointers and those that cannot be referenced by two or more pointers. Data structures with only one reader are amenable to compile-time garbage collection or local reuse. The proposed framework of linearity analysis is constraint-based and involves both equality and implicational constraints. It has been implemented as part of klint v2, a static analyzer for KL1 programs.     

Multiple kernel learning with gaussianity measures

Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.     

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.     

《元朝秘史》电子文本检索系统的研制

本文概要地介绍13世纪《元朝秘史》的文献背景及原文所独有的复杂文本形式,通过对文本的内涵分析和版面分析,设计了关于《元朝秘史》电子检索系统的研制方案。其中主要解决了原文三行一体显示格式的还原问题,而且系统可以分别对原文汉字音写、汉语译文、汉字旁译、语音语法标注等不同部分进行检索和统计。检索输出结果包括研究者最重视的传统学术章节号、卷页码、在电子文本出现的具体位置。另外,系统对检索词采用了上下文检索技术,输出文本包括检索词的部分上下文内容。本系统基本满足历史、文学和语言研究的应用需求。     

Order and disorder in heterogeneous material microstructure: Electric and elastic characterisation of dispersions of pseudo-oriented spheroids

The paper deals with the electrical and elastic characterisation of dispersions of pseudo-oriented ellipsoids of rotation: it means that we are dealing with mixtures of inclusions of different eccentricities and arbitrary non-random orientational distributions. The analysis ranges from parallel spheroidal inclusions to completely random oriented inclusions. A unified theory covers all the orientational distributions between the random and the parallel ones. The electrical and micro-mechanical averaging inside the composite material is carried out by means of explicit results which allows us to obtain closed-form expressions for the macroscopic or equivalent dielectric constants or elastic moduli of the overall composite materials. In particular, this study allows us to affirm that the electrical behaviour of such a dispersion of pseudo-oriented particles is completely defined by one order parameter which depends on the given angular distribution. Moreover, the elastic characterisation of this heterogeneous material depends on two order parameters, which derive from the orientational distribution. The theory may be applied to characterise media with different shapes of the inclusions (i.e. spheres, cylinders or planar inhomogeneities) yielding a set of procedures describing several composite materials of great technological interest.     

On the computational power of insertion-deletion systems

Gene insertion and deletion are basic phenomena found in DNA processing or RNA editing in molecular biology. The genetic mechanism and development based on these evolutionary transformations have been formulated as a formal system with two operations of insertion and deletion, called insertion-deletion systems (Kari and Thierrin, 1996; Kari et al., 1997).We investigate the generative power of insertion-deletion systems (InsDel systems), and show that the family INS ¹ ₁ DEL ¹ ₁ is equal to the family of recursively enumerable languages. This gives a positive answer to an open problem posed in Kari et al. (1997) where it was conjectured contrary. This revised version was published online in June 2006 with corrections to the Cover Date.     

Static Coarse Grain Task Scheduling with Cache Optimization Using OpenMP

Effective use of cache memory is getting more important with increasing gap between the processor speed and memory access speed. Also, use of multigrain parallelism is getting more important to improve effective performance beyond the limitation of loop iteration level parallelism. Considering these factors, this paper proposes a coarse grain task static scheduling scheme considering cache optimization. The proposed scheme schedules coarse grain tasks to threads so that shared data among coarse grain tasks can be passed via cache after task and data decomposition considering cache size at compile time. It is implemented on OSCAR Fortran multigrain parallelizing compiler and evaluated on Sun Ultra80 four-processor SMP workstation using Swim and Tomcatv from the SPEC fp 95. As the results, the proposed scheme gives us 4.56 times speedup for Swim and 2.37 times on 4 processors for Tomcatv respectively against the Sun Forte HPC Ver. 6 update 1 loop parallelizing compiler.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

Human-Robot Interaction Through Gesture-Free Spoken Dialogue

We present an approach to human-robot interaction through gesture-free spoken dialogue. Our approach is based on passive knowledge rarefication through goal disambiguation, a technique that allows a human operator to collaborate with a mobile robot on various tasks through spoken dialogue without making bodily gestures. A key assumption underlying our approach is that the operator and the robot share a common set of goals. Another key idea is that language, vision, and action share common memory structures.We discuss how our approach achieves four types of human-robot interaction: command, goal disambiguation, introspection, and instruction-based learning. We describe the system we developed to implement our approach and present experimental results.