The estimation of stochastic context-free grammars using the Inside-Outside algorithm

Using an entropy argument, it is shown that stochastic context-free grammars (SCFG's) can model sources with hidden branching processes more efficiently than stochastic regular grammars (or equivalently HMM's). However, the automatic estimation of SCFG's using the Inside-Outside algorithm is limited in practice by its O(n3) complexity. In this paper, a novel pre-training algorithm is described which can give significant computational savings. Also, the need for controlling the way that non-terminals are allocated to hidden processes is discussed and a solution is presented in the form of a grammar minimization procedure. © 1990.

Vortex Transport and Blade Interactions in High Pressure Turbines

This paper presents a study of the three-dimensional flow field within the blade rows of a high-pressure axial flow steam turbine stage. Half-delta wings were fixed to a rotating hub to simulate an upstream rotor passage vortex. The flow field is investigated in a Low-Speed Research Turbine using pneumatic and hot-wire probes downstream of the blade row. The paper examines the impact of the delta wing vortex transport on the performance of the downstream blade row. Steady and unsteady numerical simulations were performed using structured 3D Navier-Stokes solver to further understand the flow field. The loss measurements at the exit of the stator blade showed an increase in stagnation pressure loss due to the delta wing vortex transport. The increase in loss was 21% of the datum stator loss, demonstrating the importance of this vortex interaction. The transport of the stator viscous flow through the rotor blade row is also described. The rotor exit flow was affected by the interaction between the enhanced stator passage vortex and the rotor blade row. Flow underturning near the hub and overturning towards the mid-span was observed, contrary to the classical model of overturning near the hub and underturning towards the mid-span. The unsteady numerical simulation results were further analysed to identify the entropy producing regions in the unsteady flow field.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

Magnetocaloric and transport study of poly- and nanocrystalline composite manganites La 0.7Ca 0.3MnO 3/La 0.8Sr 0.2MnO 3

Magnetocaloric and transport properties are reported for novel poly- and nanocrystalline double composite manganites, La 0.8Sr 0.2MnO 3/La 0.7Ca 0.3MnO 3, prepared by the sol-gel method. Magnetic field dependence of magnetic entropy change is found to be stronger for the nano- than the polycrystalline composite. The remarkable broadening of the temperature interval, where the magnetocaloric effect occurs in poly- and nanocrystalline composites, causes the relative cooling power (RCP(S)) of the nanocrystalline composite to be reduced by only 10 compared to the Sr based polycrystalline phase. The RCP(S) of the polycrystalline composite becomes remarkably enhanced. The low temperature magnetoresistance is enhanced by 5 for the nanostructured composite. © 2012 American Institute of Physics.

Magnetocaloric effect in La0.75Sr0.25MnO3 manganite

The polycrystalline manganite La0.75Sr0.25MnO 3 prepared by an alternative carbonate precipitation route reveals the rhombohedral perovskite structure. Magnetization isotherms measured up to 2 T are used to determine Curie temperature of 332 K by means of Arrott plot. Maximum of magnetic entropy change is found at Curie temperature. The relative cooling power equal to 64 J/kg for 1.5 T magnetic field, is superior as compared to the manganite with the same chemical composition from the solgel method. © 2010 Elsevier B.V. All rights reserved.

Magnetocaloric effect and magnetic properties of Tb0.9 Sn0.1 MnO3

The magnetocaloric effect in magnetic materials is of great interest nowadays. In this article we present an investigation about the magnetic properties near the magnetic transition in a polycrystalline sample of a manganite Tb0.9 Sn0.1 MnO3. Particularly, we are interested in describing the nature of the magnetic interactions and the magnetocaloric effect in this compound. The temperature dependence of the magnetization was measured to determine the characteristics of the magnetic transition and the magnetic entropy change was calculated from magnetization curves at different temperatures. The magnetic solid is paramagnetic at high temperatures. We observe a dominant antiferromagnetic interaction below Tn =38 K for low applied magnetic fields; the presence of Sn doping in this compound decreases the Ńel temperature of the pure TbMnO3 system. A drastic increase in the magnetization as a function of temperature near the magnetic transition suggests a strong magnetocaloric effect. We found a large magnetic entropy change Δ SM (T) of about -4 J/kg K at H=3 T. We believe that the magnetic entropy change is associated with the magnetic transition and we interpret it as due to the coupling between the magnetic field and the spin ordering. This relatively large value and broad temperature interval (about 35 K) of the magnetocaloric effect make the present compound a promising candidate for magnetic refrigerators at low temperatures. © 2007 American Institute of Physics.

Magneto-transport study of Nb-doped Bi/Pb2223 superconductor

The magneto-transport properties of Bi1.5Pb0.4Nb0.1Sr2Ca2Cu 3O10-x polycrystalline, superconducting ceramic are reported. The material was found to be chemically homogeneous and partially textured. The mixed state properties were investigated by measuring the electrical resistivity, longitudinal and transverse (Nernst effect) thermoelectric power, and thermal conductivity. The magnetization and AC susceptibility measurements were also performed. The variation of these characteristics for magnetic fields up to 5 T are discussed and compared to those of the zero field case. The transport entropy and thermal Hall angle are extracted and quantitatively compared to previously reported data of closely related systems. © 2003 Elsevier Science B.V. All rights reserved.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Wetness loss prediction for a low pressure steam turbine using computational fluid dynamics

Two-phase computational fluid dynamics modelling is used to investigate the magnitude of different contributions to the wet steam losses in a three-stage model low pressure steam turbine. The thermodynamic losses (due to irreversible heat transfer across a finite temperature difference) and the kinematic relaxation losses (due to the frictional drag of the drops) are evaluated directly from the computational fluid dynamics simulation using a concept based on entropy production rates. The braking losses (due to the impact of large drops on the rotor) are investigated by a separate numerical prediction. The simulations show that in the present case, the dominant effect is the thermodynamic loss that accounts for over 90% of the wetness losses and that both the thermodynamic and the kinematic relaxation losses depend on the droplet diameter. The numerical results are brought into context with the well-known Baumann correlation, and a comparison with available measurement data in the literature is given. The ability of the numerical approach to predict the main wetness losses is confirmed, which permits the use of computational fluid dynamics for further studies on wetness loss correlations. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Guided self-organization in a dynamic embodied system based on attractor selection mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. © 2014 by the authors; licensee MDPI, Basel, Switzerland.