Spacecraft formation reconfiguration with collision avoidance

In this paper we present a behavioral control solution for reconfiguration of a spacecraft formation using the Null-Space Based (NSB) concept. The solution is task based, and aims to reconfigure and maintain a rigid formation while avoiding collisions between spacecraft. A model of relative translation is derived, together with a passivity-based sliding surface controller which globally stabilizes the equilibrium point of the closed-loop system. The NSB control method is implemented by giving each task different priorities and then calculating desired velocity and a Jacobian matrix for each spacecraft and each task. The velocity vector for each task is then projected into the null-space for higher prioritized tasks to remove conflicting velocity components. Simulation results are presented, showing that each spacecraft moves into the predefined formation without breaking any rules for the higher priority tasks, and all collisions are avoided.     

A semantics for Behavior Trees using CSP with specification commands

In this paper we give a formal definition of the requirements translation language Behavior Trees. This language has been used with success in industry to systematically translate large, complex, and often erroneous requirements documents into a structured model of the system. It contains a mixture of state-based manipulations, synchronisation, message passing, and parallel, conditional, and iterative control structures. The formal semantics of a Behavior Tree is given via a translation to a version of Hoare’s process algebra CSP, extended with state-based constructs such as guards and updates, and a message passing facility similar to that used in publish/subscribe protocols. We first provide the extension of CSP and its operational semantics, which preserves the meaning of the original CSP operators, and then the Behavior Tree notation and its translation into the extended version of CSP.     

Designing a Controller for a Multi-Train Multi-Track System

The use of formal methods for analyzing and synthesizing a controller for a multi-train multi-track railway system is discussed. The research was motivated by a case study involving the Bay Area Rapid Transit (BART) system. The overall goal is to design a train acceleration control function that enables trains to be safely placed but also increases system throughput. The use of a modeling language for specifying safety properties and a control function is illustrated. The program transformation methodology supported in the HATS system is employed to generate an efficient implementation from a high-level specification of a controller. This implementation can then be used to simulate the controller behavior, thus further enhancing confidence in the design. Properties of optimization transformations can be verified using an rewrite-rule based induction theorem prover Rewrite Rule Laboratory (RRL).     

Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. Among a number of k-anonymisation schemes, local recoding methods are promising for minimising the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymisation in attribute hierarchical taxonomies. Firstly, we define a proper distance metric to achieve local recoding generalisation with small distortion. Secondly, we propose a means to control the inconsistency of attribute domains in a generalised view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality k-anonymity tables in three quality measures than a global recoding anonymisation method, Incognito, and a multidimensional recoding anonymisation method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalised view.     

Towards the next generation of artificial neural networks for civil engineering

The purpose of this paper is to stimulate interest within the civil engineering research community for developing the next generation of applied artificial neural networks. In particular, it identifies what the next generation of these devices needs to achieve, and provides direction in terms of how their development may proceed. An analysis of the current situation indicates that progress in the development of artificial neural network applications has largely stagnated. Suggestions are made for advancing the field to the next level of sophistication and application, using genetic algorithms and related techniques. It is shown that this approach will require the design of some very sophisticated genetic coding mechanisms in order to develop the required higher-order network structures, and will utilize development mechanisms observed in nature such as growth, self-organization, and multi-stage objective functions. The capabilities of such an approach and the way in which they can be achieved are explored with reference to the problems of: (a) determining truck attributes from the strain envelopes they induce in structural members when crossing a bridge, and; (b) developing a decision support system for dynamic control of industrialized manufacturing of houses.     

Aero-structural optimization using adjoint coupled post-optimality sensitivities

A new subspace optimization method for performing aero-structural design is introduced. The method relies on a semi-analytic adjoint approach to the sensitivity analysis that includes post-optimality sensitivity information from the structural optimization subproblem. The resulting coupled post-optimality sensitivity approach is used to guide a gradient-based optimization algorithm. The new approach simplifies the system-level problem, thereby reducing the number of calls to a potentially costly aerodynamics solver. The aero-structural optimization of an aircraft wing is performed using linear aerodynamic and structural analyses, and a performance comparison is made between the new approach and the conventional multidisciplinary feasible method. The new asymmetric suboptimization method is found to be the more efficient approach when it adequately reduces the number of system evaluations or when there is a large enough discrepancy between disciplinary solution times.     

Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal

Recently, a powerful two-phase method for restoring images corrupted with high level impulse noise has been developed. The main drawback of the method is the computational efficiency of the second phase which requires the minimization of a non-smooth objective functional. However, it was pointed out in (Chan et al. in Proc. ICIP 2005, pp. 125–128) that the non-smooth data-fitting term in the functional can be deleted since the restoration in the second phase is applied to noisy pixels only. In this paper, we study the analytic properties of the resulting new functional ℱ. We show that ℱ, which is defined in terms of edge-preserving potential functions φ _α , inherits many nice properties from φ _α , including the first and second order Lipschitz continuity, strong convexity, and positive definiteness of its Hessian. Moreover, we use these results to establish the convergence of optimization methods applied to ℱ. In particular, we prove the global convergence of some conjugate gradient-type methods and of a recently proposed low complexity quasi-Newton algorithm. Numerical experiments are given to illustrate the convergence and efficiency of the two methods.