Search

The effect of radiation on the morphology and the effect of morphology on crosslinking of melt-crystallized polyethylene were studied with DSC, WAXD, SAXS and gel fraction determination. The increase of “a” dimension in unit cell of polyethylene crystal and the integral invariant of small angle scattering could be better explained by “lamellae core damage” mechanism than by “lamellae surface damage” mechanism. The dependence of G-value of crosslinking on irradiation temperature and crystallinity were also studied. There existed the contribution of crystalline region to crosslinking. Crosslinking might occur within lamellae.

Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models and technique tools. Brain big data is one of the most typical, important big data collected using powerful equipments of functional magnetic resonance imaging, multichannel electroencephalography, magnetoencephalography, Positron emission tomography, near infrared spectroscopic imaging, as well as other various devices. Granular computing with multiple granular layers, referred to as multi-granular computing (MGrC) for short hereafter, is an emerging computing paradigm of information processing, which simulates the multi-granular intelligent thinking model of human brain. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of information and even knowledge from data. This paper analyzes three basic mechanisms of MGrC, namely granularity optimization, granularity conversion, and multi-granularity joint computation, and discusses the potential of introducing MGrC into intelligent processing of brain big data.

This paper presents the RIDAS system, a toolkit for pattern recognition and data mining within the framework of rough set theory. The system comprises both a general C++ class library as well as a GUI interface running under Windows 9X/NT/2000/XP, and has been constructed using well-proven object-oriented design patterns with maintainability, extensibility and flexibility and ease of use as principal design parameters. We also compare it with other rough set based intelligent data analysis systems and give two examples to show how it works.

Rough set theory is emerging as a new tool for dealing with fuzzy and uncertain data. Attribute reduction is one of the most important parts in rough set theory. The attribute core of a decision table is often the start point and key of many information reduction procedures. Hu's method (1995) for computing the attribute core based on the discernibility matrix was wrong because of the neglect of some factors. The error was corrected by Dongyi Ye, but the complexity of his algorithm was too high. In this paper, we present a new algorithm based on object combination. The algorithm corrects Hu's error and has less complexity than Ye's and Hu's methods.

Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: ($i$) a content-aware sparse attention module based on optimal transport, and ($ii$) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.

All existing architectures and learning algorithms for Generalized Congruence Neural Network (GCNN) seem to have some shortages or lack rigorous theoretical foundation. In this paper, a novel GCNN architecture (BPGCNN) is proposed. A new error back-propagation learning algorithm is also developed for the BPGCNN. Experimental results on some benchmark problems show that the proposed BPGCNN performs better than standard sigmoidal BPNN and some improved versions of BPNN in convergence speed and learning capability, and can overcome the drawbacks of other existing GCNNs.

The degree of subdivision of the decision attribute value influences upon the accuracy of approximation classification, the approximation quality of rules, the core attributes and the information entropy in decision systems based on rough set theory. The finer the decision attribute discretization of a decision table is, the less the accuracy of approximation classification, the approximation quality of rules, and information entropy are on any condition attribute set. Meanwhile, if the attribute values of decision attributes are divided into finer values, then the core attributes set obtained from the finer decision table must include the core attributes set obtained from the previous decision table. These conclusions are proved theoretically. So the discrete degree of decision attributes should be chosen properly. The research is helpful to attribute reduction and enhancing confidences of decision rules.

In the research of knowledge acquisition based on rough sets theory, attribute reduction is a key problem. Many researchers proposed some algorithms for attribute reduction. Unfortunately, most of them are designed for static data processing. However, many real data are generated dynamically. In this paper, an incremental attribute reduction algorithm is proposed. When new objects are added into a decision information system, a new attribute reduction can be got by this method quickly.

The inertia can be considered a useful tool that is added to help in the generation of chaos in neural systems. So it can be added to the standard Hopfield equation. This paper is concerned with a study of the influence of a time delay occurring in a two-inertial neuron system .It is found that as the time delay increases beyond a critical value, the equilibrium position of the inertial two-neuron system becomes unstable and may have Hopf bifurcation. Using the time delay as a bifurcation parameter, Hopf bifurcation is studied by using theory of retarded functional differential equations, then necessary and sufficient conditions for Hopf bifurcation to occur are derived.

ISBN : 978-1-60651-015-5; International audience; Cet article présente nos idées en termes de recherche d'information utilisant différents modèles en aprticulier reposant sur la logique floue. Cela nous permet de déterminer le degré d'adéquation d'une réponse à une requête.