Guaranteed set computation with subpavings

International audienceThis paper is about the approximate representation of compact sets using subpavings, i.e., unions of non-overlapping boxes, and about computation on these sets, with particular attention to implementation issues. Some basic operations such as evaluating the intersection or union of two subpavings, or testing whether a box belongs to a subpaving are first presented. The binary tree structure used to describe subpavings then allows a simple implementation of these tasks by recursive algorithms. Algorithms are presented to evaluate the inverse and direct images of a set described by a subpaving. In both cases, a subpaving is obtained that is guaranteed to contain the actual inverse or direct image of the initial subpaving. The effectiveness of these algorithms in characterizing possibly nonconvex on even nonconnected stes is finally illustrated by simple examples

A comparative study of texture analysis algorithms in textile inspection applications

Nowadays, quality control is an important problem for fabric manufacturers. Typically these operations have been carried out by humans operators. However, this method has numerous drawbacks such as low precision, performance and effectiveness. Therefore, automatic inspection systems have increased substantially in the last decade. This work evaluates the performance of some texture measures in textile defect detection applications. For classification a method based on leaving-one-out is used. Our study has been carried out using a large database of samples to take into account a wide spectrum of fabrics and multiple defects of different nature reported by specialized works and publications. A ranking with the effectiveness of best algorithms is presented for every type of fabric. In addition, the computation time of algorithms is compared.This work is partially backed by the European Community (FEDER project)

Guard-Function-Constraint-Based Refinement Method to Generate Dynamic Behaviors of Workflow Net with Table

In order to model complex workflow systems with databases, and detect their data-flow errors such as data inconsistency, we defined Workflow Net with Table model (WFT-net) in our previous work. We used a Petri net to describe control flows and data flows of a workflow system, and labeled some abstract table operation statements on transitions so as to simulate database operations. Meanwhile, we proposed a data refinement method to construct the state reachability graph of WFT-nets, and used it to verify some properties. However, this data refinement method has a defect, i.e., it does not consider the constraint relation between guard functions, and its state reachability graph possibly has some pseudo states. In order to overcome these problems, we propose a new data refinement method that considers some constraint relations, which can guarantee the correctness of our state reachability graph. What is more, we develop the related algorithms and tool. We also illustrate the usefulness and effectiveness of our method through some examples

Solving Sparse Integer Linear Systems

We propose a new algorithm to solve sparse linear systems of equations over the integers. This algorithm is based on a $p$-adic lifting technique combined with the use of block matrices with structured blocks. It achieves a sub-cubic complexity in terms of machine operations subject to a conjecture on the effectiveness of certain sparse projections. A LinBox-based implementation of this algorithm is demonstrated, and emphasizes the practical benefits of this new method over the previous state of the art

Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization

It is known that Pareto dominance has its own weaknesses as the selection criterion in evolutionary multi-objective optimization. Algorithms based on Pareto dominance can suffer from slow convergence to the optimal front, inferior performance on problems with many objectives, etc. Non-Pareto criterion, such as decomposition-based criterion and indicator-based criterion, has already shown promising results in this regard, but its high selection pressure may lead the algorithm to prefer some specific areas of the problem’s Pareto front, especially when the front is highly irregular. In this paper, we propose a bi-criterion evolution framework of Pareto criterion and non-Pareto criterion, which attempts to make use of their strengths and compensates for each other’s weaknesses. The proposed framework consists of two parts, Pareto criterion evolution and non-Pareto criterion evolution. The two parts work collaboratively, with an abundant exchange of information to facilitate each other’s evolution. Specifically, the non-Pareto criterion evolution leads the Pareto criterion evolution forward and the Pareto criterion evolution compensates the possible diversity loss of the non-Pareto criterion evolution. The proposed framework keeps the freedom on the implementation of the non-Pareto criterion evolution part, thus making it applicable for any non-Pareto-based algorithm. In the Pareto criterion evolution, two operations, population maintenance and individual exploration, are presented. The former is to maintain a set of representative nondominated individuals, and the latter is to explore some promising areas which are undeveloped (or not well-developed) in the non-Pareto criterion evolution. Experimental results have shown the effectiveness of the proposed framework. The bi-criterion evolution works well on seven groups of 42 test problems with various characteristics, including those where Pareto-based algorithms or non-Paretobased algorithms strugg- e