Admissible Constraints Research Articles

GTRACE: Mining Frequent Subsequences from Graph Sequences

In recent years, the mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. However, to the best of our knowledge, no method has been proposed for finding frequent subsequences of graphs from a set of graph sequences. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules for graph transformations, their admissibility constraints, and a union graph. Then we propose an efficient approach named “GTRACE” for enumerating frequent transformation subsequences (FTSs) of graphs from a given set of graph sequences. The fundamental performance of the proposed method is evaluated using artificial datasets, and its practicality is confirmed by experiments using real-world datasets.

Fully Discrete Finite Element Approximations of a Polymer Gel Model

This paper proposes and analyzes some fully discrete (multiphysics) finite element methods for a displacement-pressure model which describes swelling dynamics of polymer gels under mechanical constraints. By introducing an “elastic pressure” we first present a reformulation of the original model. We then propose a time-stepping scheme which decouples the PDE system at each time step into two subproblems, one of which is a Stokes-like problem for the displacement vector field (of the solid network of the gel) and the other is a diffusion problem for a “pseudopressure” field (of the solvent of the gel). To make such a multiphysics approach feasible, it is vital to find admissible constraints to resolve the uniqueness issue for the Stokes-like problem and to construct a good boundary condition for the diffusion equation so that it also becomes uniquely solvable. The key to the first difficulty is to discover certain conserved quantities for the PDE solution, and the solution to the second difficulty is to use the Stokes-like problem to generate a boundary condition for the diffusion problem. The time-stepping scheme allows one to use any convergent Stokes solver (and its code) together with any convergent diffusion equation solver (and its code) to solve the polymer gel model. In the paper, the Taylor–Hood mixed finite element method combined with the continuous linear finite element method are chosen as an example to present the ideas and to demonstrate the viability of the proposed multiphysics approach. It is proved that, under a mesh constraint, both the semidiscrete (in space) and fully discrete methods enjoy some discrete energy laws which mimic the differential energy law satisfied by the PDE solution. Optimal order error estimates in various norms are established for both semidiscrete and fully discrete methods. Numerical experiments are also presented to show the performance of the proposed approach and methods.

Bayesian data reduction approaches in polarization imaging

The Stokes-Mueller formalism provides a coherent framework that permits obtaining polarization parameters from raw radiance acquisitions through a linear observation model. In polarization imaging, this observation model is extended to the whole image pixels where classical pixel-wise data reduction approaches are used. The whole system's optimization relies on defining well behaved "polarization measurement matrix" that reduces the impact of noises and system errors. The richness of the spatial distribution of the information is not considered this way. Furthermore, even for a well calibrated imaging polarimeter, errors and noises, inherently related to the physics of the measurement, may be amplified that prevent the physical admissibility of the desired quantities (Stokes vectors, Mueller matrices). General Bayesian approaches to estimate polarization parameters in Stokes and Mueller imaging yield a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides robust estimates of polarization channels in regard to image noises. These approaches are evaluated by using synthetic simulated data and real measurements. We show that these methods can be used as a valuable tool to analyze physical contents of observed scenes in the framework of Stokes and Mueller imaging.

Deadbeat Control for Linear Systems with Input Constraints

A new deadbeat control scheme for linear systems with input constraints is presented. Input constraints exist in most control systems, but in conventional dead-beat control, logical strategy to handle it has not been studied enough. The proposed controller in this paper adjusts the number of steps for dead-beat tracking on-line, in order to achieve delayed deadbeat-tracking performance and satisfy any admissible input constraint. Increasing the number of steps for dead-beat tracking and formulating the corresponding degree of freedom into null-space vectors make it possible to obtain delayed dead-beat tracking, and minimize the inevitable delay, respectively. LMI feasibility problems are solved to numerically obtain the solution and minimize the unavoidable step-delay. As a result, calculation effort is reduced compared to LMI-optimization problem. The proposed schemes can be readily numerically implemented. Its practical usefulness is validated by simulation for 6-axis robot model and experimental results for DC-motor servoing.

Deadbeat Control for Linear Systems with State Constraints

This paper presents a deadbeat control scheme for linear systems with state constraints. The proposed controller increases the number of steps on-line for the deadbeat tracking performance, satisfying given admissible state constraints. LMI conditions are given to minimize the unavoidable step delay. The proposed schemes can be easily developed by using LMI approach, and are validated by numerical simulation.

A study on relativistic lagrangian field theories with non-topological soliton solutions

We perform a general analysis of the dynamic structure of two classes of relativistic lagrangian field theories exhibiting static spherically symmetric non-topological soliton solutions. The analysis is concerned with (multi-) scalar fields and generalized gauge fields of compact semi-simple Lie groups. The lagrangian densities governing the dynamics of the (multi-) scalar fields are assumed to be general functions of the kinetic terms, whereas the gauge-invariant lagrangians are general functions of the field invariants. These functions are constrained by requirements of regularity, positivity of the energy and vanishing of the vacuum energy, defining what we call models. In the scalar case we establish the general conditions which determine exhaustively the families of admissible lagrangian models supporting this kind of finite-energy solutions. We analyze some explicit examples of these different families, which are defined by the asymptotic and central behaviour of the fields of the corresponding particle-like solutions. From the variational analysis of the energy functional, we show that the admissibility constraints and the finiteness of the energy of the scalar solitons are necessary and sufficient conditions for their linear static stability against small charge-preserving perturbations. Furthermore we perform a general spectral analysis of the dynamic evolution of the small perturbations around the statically stable solitons, establishing their dynamic stability. Next, we consider the case of many-components scalar fields, showing that the resolution of the particle-like field problem in this case reduces to that of the one-component case. The study of these scalar models is a necessary step in the analysis of the gauge fields.

A Bayesian approach for polarimetric data reduction: the Mueller imaging case

In this paper, we extend to the Mueller imaging framework a formerly introduced Bayesian approach dealing with polarimetric data reduction and robust clustering of polarization encoded images in the piecewise constant case. The extension was made possible thanks to a suitable writing of the observation model in the Mueller context that relies on the system's coherency matrix and Cholesky decomposition such that the admissibility constraints are easily captured. This generalization comes at the cost of nonlinearity with respect to the parameters that have to be estimated. This estimation-clustering problem is tackled in a Bayesian framework where a hierarchical stochastic model based on a Markov random field proposed by Potts is used. This fully unsupervised approach is extensively tested over synthetic data as well as real Mueller images.

Polarimetric data reduction: a Bayesian approach

In this paper, we introduce a general Bayesian approach to estimate polarization parameters in the Stokes imaging framework. We demonstrate that this new approach yields a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides a robust clustering of Stokes images in regard to image noises. The proposed approach is extensively evaluated by using synthetic simulated data and applied to cluster and retrieves the Stokes image issuing from a set of real measurements.

Synthesis of petri net supervisors enforcing general constraints

This paper deals with the synthesis of Petri net supervisor enforcing the more expressive constraints including marking terms, firing vector terms and Parikh vector terms. The method is developed to handle uncontrollable and unobservable transitions existing in the constraints. The “greater-than or equal” general constraints can also be transformed into “less-than or equal” Parikh constraints. An example is analyzed to show how the problem is solved. General constraint is first transformed into Parikh vector constraints, and Matrix-Transformation is proposed to obtain the admissible constraints without uncontrollable and unobservable transitions. Then the supervisor can be constructed based on constraints only consisting of Parikh vector terms. The method is proved to be more concise and effective than the method presented by Iordache and Moody especially when applied to large scale systems.

Paying for Primary Schools: Admission Constraints, School Popularity or Congestion?

School quality is capitalised in house prices if access to schools is rationed by residential location. We generate empirical predictions from three different theoretical approaches linking house prices to school performance, distance to school and capacity. These are respectively based upon admission constraints, school popularity and congestion effects. We find that test‐score‐based school performance significantly increases property prices, but only the best one in ten schools generate higher than average prices close by, and that prices are higher close to popular, over‐capacity schools. We conclude that the empirical evidence is more in line with the school popularity model.

Partage équitable de ressources communes. (1) Un modèle général et son application au partage de ressources satellitaires

Many industrial or research activities are so expensive that it is often benefitable for the involved agents to cofund the construction or the purchase of a common required resource. This resource will then be exploited in common, therefore in a shared way. The rules for resource sharing should take account of the possibly antagonistic preferences: each agent wants to maximize its own satisfaction, whereas, from the collective point of view, efficient decisions - i.e., decisions which both are equitable and exploit the resources in an optimal way - are looked for. We give in this article a formal model for indivisible goods resource sharing without monetary compensations and with arbitrary admissibility constraints. The model is applied to a real-world case, namely sattelite resource sharing.

Range restricted scattered data interpolation using convex combination of cubic Bézier triangles

The construction of range restricted bivariate C 1 interpolants to scattered data is considered. Sufficient nonnegativity conditions on the Bézier ordinates are derived to ensure that the nonnegativity of a cubic Bézier triangular patch. The gradients at the data sites are modified if necessary to ensure that the nonnegativity conditions are fulfilled. The interpolating surface is piecewise a convex combination of three cubic Bézier triangular patches with the same set of boundary Bézier ordinates. Its construction is local and is easily extended to include as upper and lower constraints to the interpolant surfaces of the form z = C ( x , y ) where C ( x , y ) is a constant, linear, quadratic or cubic polynomial. Moreover, C 1 piecewise polynomial surfaces consisting of polynomial pieces of the form z = C ( x , y ) on the triangulation of the data sites are also admissible constraints. A number of numerical examples are presented graphically.

On the existence of a regulated production function

This paper examines the generalization of the regulated production function. It characterizes the set of admissible regulatory constraints that are compatible with the existence of a regulated production function in a sufficiently weak framework to encompass the usual rate-of-return constraints a la Averch and Johnson and value constraints.

Systems with persistent disturbances: predictive control with restricted constraints

This paper addresses predictive state regulation of linear discrete-time systems subject to persistent bounded disturbances and to state and/or control constraints. It is well known that the joint presence of constraints and disturbances can drive a predictive controller to infeasibility and instability. Here it is shown how robustness against persistent bounded disturbances can be enforced by inserting in the predictive controller suitable constraint restrictions. The robust predictive controller obtained in this way guarantees, for all admissible disturbances, constraint fulfillment and asymptotic state regulation, i.e. convergence of the state to a minimal robust invariant set, provided that the initial state is feasible. Simulation results demonstrate the effectiveness of the proposed approach.

Petri net supervisors for DES with uncontrollable and unobservable transitions

A supervisor synthesis technique for Petri net plants with uncontrollable and unobservable transitions, that enforces the conjunction of a set of linear inequalities on the reachable markings of the plant, is presented. The approach is based on the concept of Petri net place invariants. Each step of the procedure is illustrated through a running example involving the supervision of a robotic assembly cell. The controller is described by an auxiliary Petri net connected to the plant's transitions, providing a unified Petri net model of the closed-loop system. The synthesis technique is based on the concept of admissible constraints. Procedures are given for identifying all admissible linear constraints for a plant with uncontrollable and unobservable transitions, as well as methods for transforming inadmissible constraints into admissible ones. A technique is described for creating a modified Petri net controller that enforces the union of all of these control laws. The method is practical and computationally inexpensive in terms of size, design time, and implementation complexity.

The jump behavior and stability analysis for 2-D singular systems

In this paper, we discuss the jump behavior and stability problems for 2-D linear shift-invariant singular systems under the standard boundary conditions. It is shown that once a boundary condition or the input is inadmissible in the classical sense, a group of non-causal or backward jumps of the system states will be incited. This interpretation releases the conventional admissibility constraints on the boundary conditions and inputs. Based on this observation, a systematic stability theory is developed for 2-D singular systems. The well-known basic stability theorem for the 1-D singular systems or 2-D regular systems is thus extended to the 2-D singular case.

On degeneracy of linear reconstruction from three views: linear line complex and applications

This paper investigates the linear degeneracies of projective structure estimation from line features across three views. We show that the rank of the linear system of equations for recovering the trilinear tensor of three views reduces to 23 (instead of 26) when the scene is a linear line complex [LLC] (a set of lines in space intersecting at a common line). The LLC situation is only linearly degenerate, and one can obtain a unique solution when the admissibility constraints of the tensor are accounted for. The line configuration described by an LLC, rather than being some obscure case, is in fact quite typical. It includes, as a particular example, the case of a camera moving down a hallway in an office environment or down an urban street. Furthermore, an LLC situation may occur as an artifact such as in direct estimation from spatio-temporal derivatives of image brightness. Therefore, an investigation into degeneracies and their remedy is important also in practice.

An Open Automated Framework for Constraint Solver Extension: the SoleX Approach

In declarative programming languages based on the constraint programming paradigm, computations can be viewed as deductions enhanced with the use of constraint solvers. However, admissible constraints are restricted to formulae handled by solvers and thus, declarativity may be jeopardized. We propose a domain-independent scheme to extend constraint solvers so that they can handle alien constraints, i.e., constraint involving new function symbols. This mechanism, called SoleX, consists of a set of symbolic rule-based transformations: they add and deduce syntactical as well as semantic information related to alien constraints, complete the computation domain, and purify constraints in order to allow solvers to cope with alien constraints. These transformations can be seen as elementary solvers, and thus, SoleX is a collaboration of these several solvers with the initial solver. Some extensions of computation domains have already been studied to demonstrate the broad scope of SoleX potential applications.

Polyhedral Set Constrained Control for Discrete-Time Systems with Unknown Additive Disturbances

Abstract We consider the problem of the control of discrete-time linear systems affectected by unknown but bounded disturbances in the presence of both control and state constraints The goal is produce a feedback control law assuring that at each time the system state and control belong to their assigned constraint sets for every admissible disturbance Polyhedral constraints are condidered. The problem is solved by the evaluation of the maximal U-D invariant set contained in the state constraint set that is used on-line to produce the control

Admissible large perturbations in structural redesign

A new algorithm for structural redesign by perturbation is developed. It allows for large changes between baseline and objective structures and can satisfy both modal and static displacement objectives. It is faster and more accurate than previously developed perturbation resizing methods because it advances incrementally to the objective structure using large admissible perturbations. The computer code developed is used as postprocessor to general or special purpose finite element codes, to improve upon the design of structures with unacceptable modal and/or static displacement response. Depending on the relation betweem the number of redesign goals, allowable structural changes, and admissibility constraints, the desired redesign may be feasible - either locally optimal or unique - or may not exist. In the latter case, a minimum-error inadmissible design is produced. Several numerical examples are used to study the effects of definition and relaxation of the redesign admissible domain and demonstrate the accuracy of the new redesign algorithm. An offshore tower with repeated eigen-values and 192 degrees of freedom is also redesigned subject to frequency, static displacement goals, and admissibility constraints. 22 refs.