Retrieval of microphysical and optical properties in aerosol plumes with hyperspectral imagery: L-APOM method

This paper presents the retrieval method L-APOM which aims at characterizing the microphysical and optical properties of aerosol plumes from hyperspectral images with high spatial resolution. The inversion process is divided into three steps: estimation of the ground reflectance below the plume, characterization of the standard atmosphere (gases and background aerosols) and estimation of the plume aerosols properties. As using spectral information only is not sufficient to insure uniqueness of solutions, original constraints are added by assuming slow spatial variations of particles properties within the plume. The whole inversion process is validated on a large set of simulated images and reveals to remain accurate even in the worst cases of noise: relative estimation errors of aerosol properties remain between 10% and 20% in most cases. L-APOM is applied on a real AVIRIS hyperspectral image of a biomass burning plume for which in situ measurements are available. Retrieved properties appear globally consistent with measurements.     

Aerodynamic and aeroelastic analyses on the CAARC standard tall building model using numerical simulation

The simulation of the wind action over the CAARC (Commonwealth Advisory Aeronautical Council) standard tall building model is performed in the present work. Aerodynamic and aeroelastic analyses are reproduced numerically in order to demonstrate the applicability of CFD techniques in the field of wind engineering. A major topic in this paper is referred to one of the first attempts to simulate the aeroelastic behavior of a tall building employing complex CFD techniques. Numerical results obtained in this work are compared with numerical and wind tunnel measurements and some important concluding remarks about the present simulation are also reported.     

Computing the Duquenne–Guigues basis: an algorithm for choosing the order

This paper presents an algorithm for choosing the order in which pseudo-intents are enumerated when computing the Duquenne–Guigues basis of a formal context. Sets are constructed through the use of a spanning tree to ensure they are all found once. The time and space complexities of the algorithm are empirically evaluated using, respectively, the number of logical closures and the number of sets in memory as measures. It is found that only the space complexity depends on the enumeration order.     

Computing maximal weak and other bisimulations

We present and compare several algorithms for computing the maximal strong bisimulation, the maximal divergence-respecting delay bisimulation, and the maximal divergence-respecting weak bisimulation of a generalised labelled transition system. These bisimulation relations preserve CSP semantics, as well as the operational semantics of programs in other languages with operational semantics described by such GLTSs and relying only on observational equivalence. They can therefore be used to combat the space explosion problem faced in explicit model checking for such languages. We concentrate on algorithms which work efficiently when implemented rather than on ones which have low asymptotic growth.     

Simultaneous automated design of structured QFT controller and prefilter using nonlinear programming

This paper describes a nonlinear programming‐based robust design methodology for controllers and prefilters of a predefined structure for the linear time‐invariant systems involved in the quantitative feedback theory. This controller and prefilter synthesis problem is formulated as a single optimization problem with a given performance optimization objective and constraints enforcing stability and various specifications usually enforced in the quantitative feedback theory. The focus is set on providing constraints expression that can be used in standard nonlinear programming solvers. The nonlinear solver then computes in a single‐step controller and prefilter design parameters that satisfy the prescribed constraints and maximizes the performance optimization objective. The effectiveness of the proposed approach is demonstrated through a variety of difficult design cases like resonant plants, open‐loop unstable plants, and plants with variation in the time delay. Copyright © 2016 John Wiley & Sons, Ltd.     

Active learning paradigms for CBIR systems based on optimum-path forest classification

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query at each iteration. In planned methods, the most informative images are returned during a few iterations and the most relevant ones are only presented afterward. In the past, we proposed a greedy approach based on optimum-path forest classification (OPF) and demonstrated its gain in effectiveness with respect to a planned method based on support-vector machines and another greedy approach based on multi-point query. In this work, we introduce a planned approach based on the OPF classifier and demonstrate its gain in effectiveness over all methods above using more image databases. In our tests, the most informative images are better obtained from images that are classified as relevant, which differs from the original definition. The results also indicate that both OPF-based methods require less user involvement (efficiency) to satisfy the user's expectation (effectiveness), and provide interactive response times.     

An integration experience of a software architecture and a monitoring infrastructure to deploy applications with non‐functional requirements in computing grids

Resource management is an important aspect to consider regarding applications that might have different non‐functional or operational requirements, when running in distributed and heterogeneous environments. In this context, it is necessary to provide the means to specify the required resource constraints and an infrastructure that can adapt the applications in light of the changes in resource availability. We adopted a contract‐based approach to describe and maintain parallel applications that have non‐functional requirements in a Computing Grid context, called ZeliGrid. To form the supporting infrastructure we have designed a software architecture that integrates some of the Globus services, the LDAP and the NWS monitoring services. Some modules that map the contract approach into software artifacts were also integrated to this architecture. This paper addresses the architecture and integration issues of our approach, as well as how we put the pieces together highlighting deployment and implementation details, which have to consider diverse aspects such as monitoring, security and dynamic reconfiguration. Copyright © 2010 John Wiley & Sons, Ltd.     

3D porosity prediction from seismic inversion and neural networks

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L₁-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.     

A synergistic model-driven approach for persistence modeling with UML

The Model Driven Development (MDD) approach proposes that models (and model-to-model transformations) play the main role on system development. However, there is not a consensual notation to model persistence based upon object-relational mapping frameworks: while UML lacks specific resources for persistence modeling, the entity-relationship model does not make reference to the dynamic concepts existing in UML.This paper proposes MD-JPA, a UML profile for persistence modeling based on the well-known Java Persistence API 2 (JPA) standard for object-relational mapping, pursuing the modeling of transient and persistent elements in a more coherent and synergistic way. This paper describes the main characteristics of MD-JPA as well as the way that models that adopt such profile can them be used to generate a Java implementation by the application of the proposed model transformations on a MDD approach. Finally, an open source tool was developed to make the results of this work available to the community.