Predictive Elastoplastic Damage Constitutive Law: Establishment of Equivalence Relation between Intrinsic and Extrinsic Material Parameters

The purpose of the current work is the development and application of a new identification method of material parameters of elastoplastic damage constitutive model under large strains. A relationship relating the intrinsic and extrinsic parameters of a reference material is built and transformed in equivalence relation. Extrinsic parameters concern the shape of their experimental tensile force/elongation curve, however, intrinsic parameters deal with Swift hardening law coupled with an isotropic damage variable. The relationship is carried out from a statistical characterization of a material reference (standard-steel E24). It based on multiple linear regression of a data set obtained according to a full factor design of numerical simulations of mechanical tensile tests. All materials satisfying this equivalence relation belong to the same equivalence class. This is motivated by observing that gathered materials must behave somewhat like the reference material. The material parameters can be immediately identified by only one task by running the found relationship. The current method facilitates the identification procedure and offers a substantial savings in CPU time. However it just needs only one simulation for the identification of similar behavior instead of the few hundred required when using other methods.

COMBINED ALGORITHM FOR THE ESSENTIAL GRAPH OF BAYESIAN NETWORK STRUCTURES

Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combined algorithm according to the three rules for finding the essential graph of a given directed acyclic graph. Moreover, the complexity and advantages of this combined algorithm over others are also discussed. The aim of this paper is to present the proof of the correctness of the combined algorithm.

Learning Bayesian networks using genetic algorithm

A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

Secret sharing scheme with inherited characteristic

To assure the shareholders can look for their ＂legal＂ attorneys to renew the secret, once the secret sharing scheme is initialized, a secret sharing scheme with inherited characteristic is constructed. In this scheme, each shareholder can produce a new share by his algorithm, which is equivalent to the primary one. Together with other shares, the primary secret can be renewed. Since this scheme is constructed not by replacing the primary share with a new share produced by the dealer in his primitive secret sharing scheme, so no matter how much shares the shareholder produces, these shares can not be gathered together to renew the secret in this scheme. Compared with the existing secret sharing schemes, this scheme provides more agility for the shareholders by investing each of them a function but not affect its security.

Variational Approach for the Adapted Solution of Backw ard Stochastic Differential Equations with Locally Lipschitz Diffusion Coefficients

One existence integral condition was obtained for the adapted solution of the general backward stochastic differential equations(BSDEs). Then by solving the integral constraint condition, and using a limit procedure, a new approach method is proposed and the existence of the solution was proved for the BSDEs if the diffusion coefficients satisfy the locally Lipschitz condition. In the special case the solution was a Brownian bridge. The uniqueness is also considered in the meaning of "F0-integrable equivalent class" . The new approach method would give us an efficient way to control the main object instead of the "noise".

The Homotopical Proof of Π₁(<i>S</i>, <i>x_o</i>) as a Fundamental Group in a General Interval

The aim of this study is to establish that, the equivalent class which is made up of homotopic loops is a group with respect to in the general interval [m,n]. The study proved from homotopical point of view that is associative, has an identity and inverse function. The study established with proof that is a fundamental group in [m,n] ,.

FUZZY ORDER EQUIVALENT CLASS WITH UNCERTAINTY

It is a new research topic to create a rational judgment matrix using the cognition theory because of the construction of judgment matrix in AHP involving the decision-maker's cognitive activities. Owing to the presence of uncertain information in the decision procedure, the improper use of the uncertain information will doubtless cause weight changes. In this paper, we add a feedforward process prior to constructing the judgment matrix so that the decision maker can use both the certain and uncertain information to get the initial uncertain rough judgment matrix, and then convert it into a fuzzy matrix. Consequently, it will be better for decision maker to obtain the rough set of order equivalent classes through the decision graph. According to the qualitative analysis, the decision maker can easily construct the final judgment matrix instructed by the rough set created earlier.

A transformation algorithm for incompatible information systems based on variable precision values

The variable precision rough set （VPRS） model extends the basic rough set （RS） theory with finite uni- verses and finite evaluative measures. VPRS is concerned with the equivalence and the contained relationship between two sets. In incompatible information systems, the inclusion degree and/3 upper （lower） approximation of the inconsistent equivalence class to the decision equivalence classes may be affected by the variable precision. The analysis of an example of incompatible decision table shows that there is a critical point in fl available-values region. In the new/3 range limited at the critical point, the incompatible decision table can be converted to the coordination decision table reliably. The method and its algorithm implement are introduced for the critical value search. The examples of the inconsistent equivalence class transformation are exhibited. The results illustrate that this algorithm is rational and precise.