POSITIVE FILTERED P_N METHOD FOR LINEAR TRANSPORT EQUATIONS AND THE ASSOCIATED OPTIMIZATION ALGORITHM

We propose a positive, accurate moment closure for linear kinetic transport equations based on a filtered spherical harmonic (FP_N) expansion in the angular variable. The FP_N moment equations are accurate approximations to linear kinetic equations, but they are known to suffer from the occurrence of unphysical, negative particle concentrations. The new positive filtered P_N (FP_N+) closure is developed to address this issue. The FP_N+ closure approximates the kinetic distribution by a spherical harmonic expansion that is non-negative on a finite, predetermined set of quadrature points. With an appropriate numerical PDE solver, the FP_N+ closure generates particle concentrations that are guaranteed to be non-negative. Under an additional, mild regularity assumption, we prove that as the moment order tends to infinity, the FP_N+ approximation converges, in the L2 sense, at the same rate as the FP_N approximation; numerical tests suggest that this assumption may not be necessary. By numerical experiments on the challenging line source benchmark problem, we confirm that the FP_N+ method indeed produces accurate and non-negative solutions. To apply the FP_N+ closure on problems at large temporal-spatial scales, we develop a positive asymptotic preserving (AP) numerical PDE solver. We prove that the propose AP scheme maintains stability and accuracy with standard mesh sizes at large temporal-spatial scales, while, for generic numerical schemes, excessive refinements on temporal-spatial meshes are required. We also show that the proposed scheme preserves positivity of the particle concentration, under some time step restriction. Numerical results confirm that the proposed AP scheme is capable for solving linear transport equations at large temporal-spatial scales, for which a generic scheme could fail. Constrained optimization problems are involved in the formulation of the FP_N+ closure to enforce non-negativity of the FP_N+ approximation on the set of quadrature points. These optimization problems can be written as strictly convex quadratic programs (CQPs) with a large number of inequality constraints. To efficiently solve the CQPs, we propose a constraint-reduced variant of a Mehrotra-predictor-corrector algorithm, with a novel constraint selection rule. We prove that, under appropriate assumptions, the proposed optimization algorithm converges globally to the solution at a locally q-quadratic rate. We test the algorithm on randomly generated problems, and the numerical results indicate that the combination of the proposed algorithm and the constraint selection rule outperforms other compared constraint-reduced algorithms, especially for problems with many more inequality constraints than variables.

IN-SITU ELECTRICAL, MECHANICAL AND ELECTROCHEMICAL CHARACTERIZATIONS OF ONE-DIMENSIONAL NANOSTRUCTURES

One-dimensional nanostructures initiated new aspects to the materials applications due to their superior properties compared to the bulk materials. Properties of nanostructures have been characterized by many techniques and used for various device applications. However, simultaneous correlation between the physical and structural properties of these nanomaterials has not been widely investigated. Therefore, it is necessary to perform in-situ study on the physical and structural properties of nanomaterials to understand their relation. In this work, we will use a unique instrument to perform real time atomic force microscopy (AFM) and scanning tunneling microscopy (STM) of nanomaterials inside a transmission electron microscopy (TEM) system. This AFM/STM-TEM system is used to investigate the mechanical, electrical, and electrochemical properties of boron nitride nanotubes (BNNTs) and Silicon nanorods (SiNRs). BNNTs are one of the subjects of this PhD research due to their comparable, and in some cases superior, properties compared to carbon nanotubes. Therefore, to further develop their applications, it is required to investigate these characteristics in atomic level. In this research, the mechanical properties of multi-walled BNNTs were first studied. Several tests were designed to study and characterize their real-time deformation behavior to the applied force. Observations revealed that BNNTs possess highly flexible structures under applied force. Detailed studies were then conducted to understand the bending mechanism of the BNNTs. Formations of reversible ripples were observed and described in terms of thermodynamic energy of the system. Fracture failure of BNNTs were initiated at the outermost walls and characterized to be brittle. Second, the electrical properties of individual BNNTs were studied. Results showed that the bandgap and electronic properties of BNNTs can be engineered by means of applied strain. It was found that the conductivity, electron concentration and carrier mobility of BNNTs can be tuned as a function of applied stress. Although, BNNTs are considered to be candidate for field emission applications, observations revealed that their properties degrade upon cycles of emissions. Results showed that due to the high emission current density, the temperature of the sample was increased and reached to the decomposition temperature at which the B-N bonds start to break. In addition to BNNTs, we have also performed in-situ study on the electrochemical properties of silicon nanorods (SiNRs). Specifically, lithiation and delithiation of SiNRs were studied by our STM-TEM system. Our observations showed the direct formation of Li22Si5 phases as a result of lithium intercalation. Radial expansion of the anode materials were observed and characterized in terms of size-scale. Later, the formation and growth of the lithium fibers on the surface of the anode materials were observed and studied. Results revealed the formation of lithium islands inside the ionic liquid electrolyte which then grew as Li dendrite toward the cathode material.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

E-contracting with price configuration for Web services and QoS

The large amount of information in electronic contracts hampers their establishment due to high complexity. An approach inspired in Software Product Line (PL) and based on feature modelling was proposed to make this process more systematic through information reuse and structuring. By assessing the feature-based approach in relation to a proposed set of requirements, it was showed that the approach does not allow the price of services and of Quality of Services (QoS) attributes to be considered in the negotiation and included in the electronic contract. Thus, this paper also presents an extension of such approach in which prices and price types associated to Web services and QoS levels are applied. An extended toolkit prototype is also presented as well as an experiment example of the proposed approach.

SAB(IO): A BIOLOGICALLY PLAUSIBLE CONNECTIONIST APPROACH TO AUTOMATIC TEXT SUMMARIZATION

An implementation of a computational tool to generate new summaries from new source texts is presented, by means of the connectionist approach (artificial neural networks). Among other contributions that this work intends to bring to natural language processing research, the use of a more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expectation that it may bring an increase in computational efficiency when compared to the sa-called biologically implausible algorithms.

Developing and implementing an open and non-proprietary device description for FOUNDATION fieldbus based on software standards

Support for interoperability and interchangeability of software components which are part of a fieldbus automation system relies on the definition of open architectures, most of them involving proprietary technologies. Concurrently, standard, open and non-proprietary technologies, such as XML, SOAP, Web Services and the like, have greatly evolved and been diffused in the computing area. This article presents a FOUNDATION fieldbus (TM) device description technology named Open-EDD, based on XML and other related technologies (XLST, DOM using Xerces implementation, OO, XMIL Schema), proposing an open and nonproprietary alternative to the EDD (Electronic Device Description). This initial proposal includes defining Open-EDDML as the programming language of the technology in the FOUNDATION fieldbus (TM) protocol, implementing a compiler and a parser, and finally, integrating and testing the new technology using field devices and a commercial fieldbus configurator. This study attests that this new technology is feasible and can be applied to other configurators or HMI applications used in fieldbus automation systems. (c) 2008 Elsevier B.V. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

A small area 8 bits 50 MHz CMOS DAC for Bluetooth transmitter

This paper presents a small-area CMOS current-steering segmented digital-to-analog converter (DAC) design intended for RF transmitters in 2.45 GHz Bluetooth applications. The current-source design strategy is based on an iterative scheme whose variables are adjusted in a simple way, minimizing the area and the power consumption, and meeting the design specifications. A theoretical analysis of static-dynamic requirements and a new layout strategy to attain a small-area current-steering DAC are included. The DAC was designed and implemented in 0.35 mu m CMOS technology, requiring an active area of just 200 mu m x 200 mu m. Experimental results, with a full-scale output current of 700 mu A and a 3.3 V power supply, showed a spurious-free dynamic range of 58 dB for a 1 MHz output sine wave and sampling frequency of 50 MHz, with differential and integral nonlinearity of 0.3 and 0.37 LSB, respectively.

Modeling and analysis of laminate composite plates with embedded active-passive piezoelectric networks

The objective of this work is to present the finite element modeling of laminate composite plates with embedded piezoelectric patches or layers that are then connected to active-passive resonant shunt circuits, composed of resistance, inductance and voltage source. Applications to passive vibration control and active control authority enhancement are also presented and discussed. The finite element model is based on an equivalent single layer theory combined with a third-order shear deformation theory. A stress-voltage electromechanical model is considered for the piezoelectric materials fully coupled to the electrical circuits. To this end, the electrical circuit equations are also included in the variational formulation. Hence, conservation of charge and full electromechanical coupling are guaranteed. The formulation results in a coupled finite element model with mechanical (displacements) and electrical (charges at electrodes) degrees of freedom. For a Graphite-Epoxy (Carbon-Fibre Reinforced) laminate composite plate, a parametric analysis is performed to evaluate optimal locations along the plate plane (xy) and thickness (z) that maximize the effective modal electromechanical coupling coefficient. Then, the passive vibration control performance is evaluated for a network of optimally located shunted piezoelectric patches embedded in the plate, through the design of resistance and inductance values of each circuit, to reduce the vibration amplitude of the first four vibration modes. A vibration amplitude reduction of at least 10 dB for all vibration modes was observed. Then, an analysis of the control authority enhancement due to the resonant shunt circuit, when the piezoelectric patches are used as actuators, is performed. It is shown that the control authority can indeed be improved near a selected resonance even with multiple pairs of piezoelectric patches and active-passive circuits acting simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

Refined sandwich model for the vibration of beams with embedded shear piezoelectric actuators and sensors

This work extends a previously presented refined sandwich beam finite element (FE) model to vibration analysis, including dynamic piezoelectric actuation and sensing. The mechanical model is a refinement of the classical sandwich theory (CST), for which the core is modelled with a third-order shear deformation theory (TSDT). The FE model is developed considering, through the beam length, electrically: constant voltage for piezoelectric layers and quadratic third-order variable of the electric potential in the core, while meclianically: linear axial displacement, quadratic bending rotation of the core and cubic transverse displacement of the sandwich beam. Despite the refinement of mechanical and electric behaviours of the piezoelectric core, the model leads to the same number of degrees of freedom as the previous CST one due to a two-step static condensation of the internal dof (bending rotation and core electric potential third-order variable). The results obtained with the proposed FE model are compared to available numerical, analytical and experimental ones. Results confirm that the TSDT and the induced cubic electric potential yield an extra stiffness to the sandwich beam. (C) 2007 Elsevier Ltd. All rights reserved.

Coloured Petri nets and graphical simulation for the validation of a robotic cell in aircraft industry

This paper proposes a mixed validation approach based on coloured Petri nets and 3D graphic simulation for the design of supervisory systems in manufacturing cells with multiple robots. The coloured Petri net is used to model the cell behaviour at a high level of abstraction. It models the activities of each cell component and its coordination by a supervisory system. The graphical simulation is used to analyse and validate the cell behaviour in a 3D environment, allowing the detection of collisions and the calculation of process times. The motivation for this work comes from the aeronautic industry. The automation of a fuselage assembly process requires the integration of robots with other cell components such as metrological or vision systems. In this cell, the robot trajectories are defined by the supervisory system and results from the coordination of the cell components. The paper presents the application of the approach for an aircraft assembly cell under integration in Brazil. This case study shows the feasibility of the approach and supports the discussion of its main advantages and limits. (C) 2011 Elsevier Ltd. All rights reserved.

Denoising swallowing sound to improve the evaluator`s qualitative analysis

Swallowing dynamics involves the coordination and interaction of several muscles and nerves which allow correct food transport from mouth to stomach without laryngotracheal penetration or aspiration. Clinical swallowing assessment depends on the evaluator`s knowledge of anatomic structures and of neurophysiological processes involved in swallowing. Any alteration in those steps is denominated oropharyngeal dysphagia, which may have many causes, such as neurological or mechanical disorders. Videofluoroscopy of swallowing is presently considered to be the best exam to objectively assess the dynamics of swallowing, but the exam needs to be conducted under certain restrictions, due to patient`s exposure to radiation, which limits periodical repetition for monitoring swallowing therapy. Another method, called cervical auscultation, is a promising new diagnostic tool for the assessment of swallowing disorders. The potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. Even so, the captured sound has an amount of noise, which can hamper the evaluator`s decision. In that way, the present paper proposes the use of a filter to improve the quality of audible sound and facilitate the perception of examination. The wavelet denoising approach is used to decompose the noisy signal. The signal to noise ratio was evaluated to demonstrate the quantitative results of the proposed methodology. (C) 2007 Elsevier Ltd. All rights reserved.

A survey on key management mechanisms for distributed Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have a vast field of applications, including deployment in hostile environments. Thus, the adoption of security mechanisms is fundamental. However, the extremely constrained nature of sensors and the potentially dynamic behavior of WSNs hinder the use of key management mechanisms commonly applied in modern networks. For this reason, many lightweight key management solutions have been proposed to overcome these constraints. In this paper, we review the state of the art of these solutions and evaluate them based on metrics adequate for WSNs. We focus on pre-distribution schemes well-adapted for homogeneous networks (since this is a more general network organization), thus identifying generic features that can improve some of these metrics. We also discuss some challenges in the area and future research directions. (C) 2010 Elsevier B.V. All rights reserved.