Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

Connecting Scientific Data to Scientific Experiments with Provenance

As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises: since specifying details of execution is now automatic, a workflow's results are harder to interpret, as they are partly due to specifics of execution. By automating steps between the experiment design and its results, we lose the connection between them, hindering interpretation of results. To reconnect the scientific data with the original experiment, we argue that scientists should have access to the full provenance of their data, including not only parameters, inputs and intermediary data, but also the abstract experiment, refined into a concrete execution by the "workflow compiler". In this paper, we describe preliminary work on adapting Pegasus to capture the process of workflow refinement in the PASOA provenance system.

Extending Agent Languages for Autonomy

BDI agent languages provide a useful abstraction for complex systems comprised of interactive autonomous entities, but they have been used mostly in the context of single agents with a static plan library of behaviours invoked reactively. These languages provide a theoretically sound basis for agent design but are very limited in providing direct support for autonomy and societal cooperation needed for large scale systems. Some techniques for autonomy and cooperation have been explored in the past in ad hoc implementations, but not incorporated in any agent language. In order to address these shortcomings we extend the well known AgentSpeak(L) BDI agent language to include behaviour generation through planning, declarative goals and motivated goal adoption. We also develop a language-specific multiagent cooperation scheme and, to address potential problems arising from autonomy in a multiagent system, we extend our agents with a mechanism for norm processing leveraging existing theoretical work. These extensions allow for greater autonomy in the resulting systems, enabling them to synthesise new behaviours at runtime and to cooperate in non-scripted patterns.

Artificial neural networks applied in study of atmospheric parameters to high voltage substations concerning lightning

This gaper demonstrates that artificial neural networks can be used effectively for estimation of parameters related to study of atmospheric conditions to high voltage substations design. Specifically, the neural networks are used to compute the variation of electrical field intensity and critical disruptive voltage in substations taking into account several atmospheric factors, such as pressure, temperature, humidity, so on. Examples of simulation of tests are presented to validate the proposed approach. The results that were obtained by experimental evidences and numerical simulations allowed the verification of the influence of the atmospheric conditions on design of substations concerning lightning.

A modified Hopfield model for solving the N-Queens problem

A neural network model for solving the N-Queens problem is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. Simulation results are presented to validate the proposed approach.

Using neural networks for estimation of aquifer dynamical behavior

The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.

Application of neural networks to identify features of dynamical grounding systems

The accurate identification of features of dynamical grounding systems are extremely important to define the operational safety and proper functioning of electric power systems. Several experimental tests and theoretical investigations have been carried out to obtain characteristics and parameters associated with the technique of grounding. The grounding system involves a lot of non-linear parameters. This paper describes a novel approach for mapping characteristics of dynamical grounding systems using artificial neural networks. The network acts as identifier of structural features of the grounding processes. So that output parameters can be estimated and generalized from an input parameter set. The results obtained by the network are compared with other approaches also used to model grounding systems.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

Micro Welding of Ni-based Alloy Monel 400 Thin Foil by Pulsed Nd:YAG laser

In this study a pulsed Nd:YAG laser was used to join Monel 400 thin foil with 100 mu m thickness. Pulse energy was varied from 1.0 to 2.25J at small increments of 0.25J. The macro and microstructures were analyzed by optical microscopy, tensile shear test and microhardness. Sound laser welds without discontinuities were obtained with 1.5 J pulse energy. Results indicate that using a precise control of the pulse energy, and so a control of the bottom foil dilution rate, it is possible to weld Monel 400 thin foil. The process appeared to be very sensitive to the gap between couples.

Toward the Application of Three-Dimensional Approach to Few-body Atomic Bound States

The first step toward the application of an effective non partial wave (PW) numerical approach to few-body atomic bound states has been taken. The two-body transition amplitude which appears in the kernel of three-dimensional Faddeev-Yakubovsky integral equations is calculated as function of two-body Jacobi momentum vectors, i.e. as a function of the magnitude of initial and final momentum vectors and the angle between them. For numerical calculation the realistic interatomic interactions HFDHE2, HFD-B, LM2M2 and TTY are used. The angular and momentum dependence of the fully off-shell transition amplitude is studied at negative energies. It has been numerically shown that, similar to the nuclear case, the transition amplitude exhibits a characteristic angular behavior in the vicinity of He-4 dimer pole.

Universality and Halo Nuclei

Universal aspects of few-body systems will be reviewed motivated by recent interest in atomic and nuclear physics. The critical conditions for the existence of excited states in three-body systems with two-identical particles will be explored. In particular, we consider halo nuclei that can be modeled as three-body nuclear systems, with two halo neutrons and a core. In this context, we also discuss the low-energy neutron-C-19 elastic scattering, near the conditions for the appearance of an Efimov state.

Study of the (D)over-barN Interaction in a QCD Coulomb Gauge Quark Model

We study the (D) over barN interaction at low energies with a quark model inspired in the QCD Hamiltonian in Coulomb gauge. The model Hamiltonian incorporates a confining Coulomb potential extracted from a self-consistent quasiparticle method for the gluon degrees of freedom, and transverse-gluon hyperfine interaction consistent with a finite gluon propagator in the infrared. Initially a constituent-quark mass function is obtained by solving a gap equation and baryon and meson bound-states are obtained in Fock space using a variational calculation. Next, having obtained the constituent-quark masses and the hadron waves functions, an effective meson-nucleon interaction is derived from a quark-interchange mechanism. This leads to a short range meson-baryon interaction and to describe long-distance physics vector- and scalar-meson exchanges described by effective Lagrangians are incorporated. The derived effective (D) over barN potential is used in a Lippmann-Schwinger equation to obtain phase shifts. The results are compared with a recent similar calculation using the nonrelativistic quark model.