On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Monetary policy as a source of uncertainty

This paper proposes a model in which control variations induce an increase in the uncertainty of the system. The aim of our paper is to provide a stochastic theoretical model that can be used to explain under which uncertainty conditions monetary policy rules should be less or more aggressive, or, simply, applied or not.

Controllable spin-dependent transport in armchair graphene nanoribbon structures

Using the non-equilibrium Green's functions formalism in a tight binding model, the spin-dependent transport in armchair graphene nanoribbon (GNR) structures controlled by a ferromagnetic gate is investigated. Beyond the oscillatory behavior of conductance and spin polarization with respect to the barrier height, which can be tuned by the gate voltage, we especially analyze the effect of width-dependent band gap and the nature of contacts. The oscillation of spin polarization in the GNRs with a large band gap is strong in comparison with 2D-graphene structures. Very high spin polarization (close to 100%) is observed in normal-conductor/graphene/normal-conductor junctions. Moreover, we find that the difference of electronic structure between normal conductor and graphene generates confined states in the device which have a strong influence on the transport quantities. It suggests that the device should be carefully designed to obtain high controllability of spin current.Comment: 8 pages, 7 figure

DEVS-based intelligent control of space adapted fluid mixing

The development is described of event-based intelligent control system for a space-adapted mixing process by employing the DEVS (Discrete Event System Specification) formalism. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The DEVS-based intelligent control paradigm was applied in a space-adapted mixing system capable of supporting the laboratory automation aboard a Space Station

Multivariate texture discrimination based on geodesics to class centroids on a generalized Gaussian Manifold

A texture discrimination scheme is proposed wherein probability distributions are deployed on a probabilistic manifold for modeling the wavelet statistics of images. We consider the Rao geodesic distance (GD) to the class centroid for texture discrimination in various classification experiments. We compare the performance of GD to class centroid with the Euclidean distance in a similar context, both in terms of accuracy and computational complexity. Also, we compare our proposed classification scheme with the k-nearest neighbor algorithm. Univariate and multivariate Gaussian and Laplace distributions, as well as generalized Gaussian distributions with variable shape parameter are each evaluated as a statistical model for the wavelet coefficients. The GD to the centroid outperforms the Euclidean distance and yields superior discrimination compared to the k-nearest neighbor approach

A basis of cranking operators for the pairing-plus-quadrupole model

We investigate the RPA normal-mode coordinates in the pairing-plus-quadrupole model, with an eye on simplifying the application of large amplitude collective motion techniques. At the Hartree-Bogoliubov minimum, the RPA modes are exactly the cranking operators of the collective coordinate approach. We examine the possibility of representing the self-consistent cranking operator by linear combinations of a limited number of one-body operators. We study the Sm nuclei as an example, and find that such representations exist in terms of operators that are state-dependent in a characteristic manner.Comment: 6 pages, 1 figure, using IOP journal style files, to be published in J. Phys.