The atoms, molecules and fibers of organizations

The increasing demands concerning the modifiability and connectivity of business processes cannot be met adequately anymore by relying on best practices only. There is an urgent need for a reference conceptual framework for studying, modeling, analyzing and designing business processes. The Language–Action Perspective (LAP), in particular Habermas’ theory of Communicative Action offers a sound and rigid foundation for such a framework. In this paper, the DEMO (Demo Engineering Methodology for Organizations)-framework is presented. It builds on the LAP-based theoretical foundation of the DEMO methodology. Several other LAP-based frameworks have been proposed in the past years. They are evaluated in a comparative review with the DEMO-framework. Several shortcomings of these frameworks are revealed and discussed. The practical applicability of the DEMO-framework is demonstrated using a small example.     

Object data MODEL = object-oriented + semantic models

The term ‘object-oriented database’ has been used to refer to different characteristics and mechanisms supported by semantic databases and (behaviorally) object-oriented ones. While this indicates the current confusion due to the lack of a common model, it also suggests the potential desirability of combining these two kinds of modeling facilities into the same nutshell. This paper summarizes from these two paradigms the features common to both, the features influenced/adopted by each other, and the features which are presented in one paradigm but missing from the other. After a brief analysis and discussion of these features, we conclude this paper by proposing our major theme: a complete object data model should take the union of the facilities offered by these two kinds of models.     

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

机械合金化的原理及在磁性材料研究中的应用

介绍了机械合金化的原理和描述机械合金化过程的理论模型。综述了机械合金化在磁性材料研究中的应用，并对目前研究中的存在的问题及发展前景进行了分析。     

Modeling video sources for real-time scheduling

What is the impact of the autocorrelation of variable-bit-rate (VBR) sources on real-time scheduling algorithms? Our results show that the impact of long term, or interframe, autocorrelation is negligible, while the impact of short term, or intraframe, autocorrelation can be significant. Such results are essentially independent of the video coding scheme employed. To derive these results, video sequences are modeled as a collection of stationary subsequences called scenes. Within a scene, a statistical model is derived for both the sequence of frames and of slices. The model captures the distribution and the autocorrelation function of real-time video data. In previous work, the pseudoperiodicity of the slice level auto-correlation function made it difficult to develop a simple yet accurate model. We present a generalization of previous methods that can easily capture this pseudoperiodicity and is suited for modeling a greater variety of autocorrelation functions. By simply tuning a few parameters, the model reproduces the statistic behavior of sources with different types and levels of correlation on both the frame and the slice level.     

Melt index prediction by weighted least squares support vector machines

Melt index is considered an important quality variable determining product specifications. Reliable prediction of melt index (MI) is crucial in quality control of practical propylene polymerization processes. In this paper a least squares support vector machines (LS‐SVM) soft‐sensor model of propylene polymerization process is developed to infer the MI of polypropylene from other process variables. Considering the use of a SSE cost function without regularization might lead to less robust estimates; the weighted least squares support vector machines (weighted LS‐SVM) approach of propylene polymerization process is further proposed to obtain a robust estimation of melt index. The performance of standard SVM model is taken as a basis of comparison. A detailed comparison research among the standard SVM, LS‐SVM, and weighted LS‐SVM models is carried out. The research results confirm the effectiveness of the presented methods. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 285–289, 2006     

A review of robust design methods for multiple responses

Problems in engineering design often involve determining design variable settings to optimize individual product performance for multiple criteria, which are often in conflict. We review mathematically rigorous techniques from the statistical literature for finding a vector x of design variable settings, which produces an optimal compromise solution among a group of prioritized response variables. The best compromise solution is typically gained by optimizing an objective function, which incorporates the prioritized demands of multiple responses. Since most multi-response objective functions are constructed by combining the functions used to optimize univariate responses, a review of the prominent univariate approaches is presented first. A multivariate approach from the engineering literature called the compromise Decision Support Problem is also reviewed. Finally a table comparing the relative merits of the different multivariate approaches summarizes the article in a concise and user-friendly fashion.     

Hydrocarbon exploration risk evaluation through uncertainty and sensitivity analyses techniques

The evaluation of the exploration risk in the oil industry is a fundamental component of the decision process related to the exploratory phase. In this paper the two basic components of the exploratory risk: trap geometry and trapped hydrocarbon quantities (fluid), are compounded in a single coherent uncertainty and sensitivity approach. The results clarify that the model geometry influences each Petroleum System Modeling step and that the geometric uncertainty is correlated with the fluid uncertainty. The geometric uncertainty evaluation makes use of geostatistical techniques that produce a number of possible realizations of the trap geometry, all compatible with available data. The evaluation of the fluid uncertainty, through a Monte Carlo methodology, allows us to compute the possible quantities of oil and gas, generated in a basin and migrated from the hydrocarbon source location to each single trap. The final result is the probability distribution of oil and gas for each trap in the basin, together with other useful indicators like: the hydrocarbon filling probability map, the closure probability map, the drainage area probability map, the spilling paths probabilities, the trap-filling scenarios.     

广义频率响应函数的鲁棒建模新方法

针对基于非线性谱分析的故障诊断技术的需要,研究了非线性系统的广义频率响应函数的鲁棒在线建模方 法。首先将次元分析算法应用于Volterra级数模型的辨识,并通过对此算法的修正,得到了一种改进的Volterra系统的鲁 棒辨识算法。对其鲁棒性能进行的分析表明,改进算法的鲁棒抗噪性能被明显提高。在此基础上,提出了一种广义频率响 应函数的两步鲁棒建模新方法。仿真实验表明,该方法具有良好的鲁棒收敛性能,能够有效解决GFRF的在线建模问题。