Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Modeling for control design of a multi-reach water canal

This article addresses the problem of obtaining reduced complexity models of multi-reach water delivery canals that are suitable for robust and linear parameter varying (LPV) control design. In the first stage, by applying a method known from the literature, a finite dimensional rational transfer function of a priori defined order is obtained for each canal reach by linearizing the Saint-Venant equations. Then, by using block diagrams algebra, these different models are combined with linearized gate models in order to obtain the overall canal model. In what concerns the control design objectives, this approach has the advantages of providing a model with prescribed order and to quantify the high frequency uncertainty due to model approximation. A case study with a 3-reach canal is presented, and the resulting model is compared with experimental data. © 2014 IEEE.

Modeling for control design of a multi-reach water canal

This article addresses the problem of obtaining reduced complexity models of multi-reach water delivery canals that are suitable for robust and linear parameter varying (LPV) control design. In the first stage, by applying a method known from the literature, a finite dimensional rational transfer function of a priori defined order is obtained for each canal reach by linearizing the Saint-Venant equations. Then, by using block diagrams algebra, these different models are combined with linearized gate models in order to obtain the overall canal model. In what concerns the control design objectives, this approach has the advantages of providing a model with prescribed order and to quantify the high frequency uncertainty due to model approximation. A case study with a 3-reach canal is presented, and the resulting model is compared with experimental data. © 2014 IEEE.

Design and construction of tunnels in zones subjected to high convergences

The economic development of a region depends on the speed that people and goods can travel. The reduction of people and goods travel time can be achieved by planning smooth road layouts, which are obtained by crossing natural obstacles such as hills, by tunneling at great depths, and allowing the reduction of the road alignment length. The stress state in rock masses at such depths, either because of the overburden or due to the tectonic conditions of the rock mass induces high convergences of the tunnel walls. These high convergence values are incompatible with the supports structural performance installed in the excavation stabilization. In this article it is intended to evaluate and analyze some of the solutions already implemented in several similar geological and geotechnical situations, in order to establish a methodological principle for the design of the tunnels included in a highway section under construction in the region influenced by the Himalayas, in the state of Himachal Pradesh (India) and referenced by "four laning of Kiratpur to Ner Chowk section".

Magnetic flux density distribution in the air gap of a ferromagnetic core with superconducting blocks: three-dimensional analysis and experimental NMR results

The design of magnetic cores can be carried out by taking into account the optimization of different parameters in accordance with the application requirements. Considering the specifications of the fast field cycling nuclear magnetic resonance (FFC-NMR) technique, the magnetic flux density distribution, at the sample insertion volume, is one of the core parameters that needs to be evaluated. Recently, it has been shown that the FFC-NMR magnets can be built on the basis of solenoid coils with ferromagnetic cores. Since this type of apparatus requires magnets with high magnetic flux density uniformity, a new type of magnet using a ferromagnetic core, copper coils, and superconducting blocks was designed with improved magnetic flux density distribution. In this paper, the designing aspects of the magnet are described and discussed with emphasis on the improvement of the magnetic flux density homogeneity (Delta B/B-0) in the air gap. The magnetic flux density distribution is analyzed based on 3-D simulations and NMR experimental results.

High-level Petri nets modules for embedded controllers design

Modular design is crucial to manage large-scale systems and to support the divide-and-conquer development approach. It allows hierarchical representations and, therefore, one can have a system overview, as well as observe component details. Petri nets are suitable to model concurrent systems, but lack on structuring mechanisms to support abstractions and the composition of sub-models, in particular when considering applications to embedded controllers design. In this paper we present a module construct, and an underlying high-level Petri net type, to model embedded controllers. Multiple interfaces can be declared in a module, thus, different instances of the same module can be used in different situations. The interface is a subset of the module nodes, through which the communication with the environment is made. Module places can be annotated with a generic type, overridden with a concrete type at instance level, and constants declared in a module may have a new value in each instance.

MDAI: Model based Design in Automobile Industry

It is proposed a new approach based on a methodology, assisted by a tool, to create new products in the automobile industry based on previous defined processes and experiences inspired on a set of best practices or principles: it is based on high-level models or specifications; it is component-based architecture centric; it is based on generative programming techniques. This approach follows in essence the MDA (Model Driven Architecture) philosophy with some specific characteristics. We propose a repository that keeps related information, such as models, applications, design information, generated artifacts and even information concerning the development process itself (e.g., generation steps, tests and integration milestones). Generically, this methodology receives the users' requirements to a new product (e.g., functional, non-functional, product specification) as its main inputs and produces a set of artifacts (e.g., design parts, process validation output) as its main output, that will be integrated in the engineer design tool (e.g. CAD system) facilitating the work.

New Steel Alloys for the Design of Heat Recovery Steam Generator Components of Combined Cycle Gas Plants

Demand for power is growing every day, mainly due to emerging economies in countries such as China, Russia, India, and Brazil. During the last 50 years steam pressure and temperature in power plants have been continuously raised to improve thermal efficiency. Recent efforts to improve efficiency leads to the development of a new generation of heat recovery steam generator, where the Benson once-through technology is applied to improve the thermal efficiency. The main purpose of this paper is to analyze the mechanical behavior of a high pressure superheater manifold by applying finite element modeling and a finite element analysis with the objective of analyzing stress propagation, leading to the study of damage mechanism, e.g., uniaxial fatigue, uniaxial creep for life prediction. The objective of this paper is also to analyze the mechanical properties of the new high temperature resistant materials in the market such as 2Cr Bainitic steels (T/P23 and T/P24) and also the 9-12Cr Martensitic steels (T/P91, T/P92, E911, and P/T122). For this study the design rules for construction of power boilers to define the geometry of the HPSH manifold were applied.

Transport properties in microcrystalline silicon solar cells under AM1.5 illumination analysed by two-dimensional numerical simulation

Microcrystalline silicon is a two-phase material. Its composition can be interpreted as a series of grains of crystalline silicon imbedded in an amorphous silicon tissue, with a high concentration of dangling bonds in the transition regions. In this paper, results for the transport properties of a mu c-Si:H p-i-n junction obtained by means of two-dimensional numerical simulation are reported. The role played by the boundary regions between the crystalline grains and the amorphous matrix is taken into account and these regions are treated similar to a heterojunction interface. The device is analysed under AM1.5 illumination and the paper outlines the influence of the local electric field at the grain boundary transition regions on the internal electric configuration of the device and on the transport mechanism within the mu c-Si:H intrinsic layer.

Projecto de fundações e estrutura de um edifício destinado a pavilhão gimnodesportivo

Este trabalho tem como objectivo a elaboração do projecto de estruturas de um edifício destinado a pavilhão gimnodesportivo, caracterizando as suas diferentes fases de execução, desde a etapa inicial de concepção até à fase final de dimensionamento. Trata-se de um projecto complexo de uma estrutura com elementos estruturais em betão armado e pré-esforçado, e com muros de contenção. Na concepção do edifício foram utilizados os critérios gerais de dimensionamento presentes na regulamentação Europeia (Eurocódigos), uma vez que estes elementos representam o futuro da regulamentação de estruturas em termos Europeus, vindo substituir a nível nacional o “Regulamento de Segurança e Acções para Estruturas de Betão Armado (RSA)” e o “Regulamento para Estruturas de Betão Armado e Pré- Esforçado (REBAP)”. A adopção das normas europeias representam assim um elevado desafio devido ao aumento da complexidade na concepção e dimensionamento de estruturas que estes regulamentos traduzem, principalmente o Eurocódigo 8, que define de um modo mais detalhado e complexo a análise sísmica, relativamente à regulamentação actual em vigor. Devido à elevada complexidade que os projectos de estruturas apresentam, utilizam-se actualmente ferramentas de cálculo automático. No dimensionamento deste edifício foi utilizado um programa tridimensional de elementos finitos para a modelação da estrutura. Pretende-se com a escolha deste projecto e dos métodos de dimensionamento presentes nos Eurocódigos, o desenvolvimento de um trabalho detalhado e correcto, permitindo assim adquirir conhecimentos importantes relativamente às futuras normas, e pôr em prática as competências e os conhecimentos obtidos ao longo curso.

Scalable Unified Transform Architecture for Advanced Video Coding Embedded Systems

A novel high throughput and scalable unified architecture for the computation of the transform operations in video codecs for advanced standards is presented in this paper. This structure can be used as a hardware accelerator in modern embedded systems to efficiently compute all the two-dimensional 4 x 4 and 2 x 2 transforms of the H.264/AVC standard. Moreover, its highly flexible design and hardware efficiency allows it to be easily scaled in terms of performance and hardware cost to meet the specific requirements of any given video coding application. Experimental results obtained using a Xilinx Virtex-5 FPGA demonstrated the superior performance and hardware efficiency levels provided by the proposed structure, which presents a throughput per unit of area relatively higher than other similar recently published designs targeting the H.264/AVC standard. Such results also showed that, when integrated in a multi-core embedded system, this architecture provides speedup factors of about 120x concerning pure software implementations of the transform algorithms, therefore allowing the computation, in real-time, of all the above mentioned transforms for Ultra High Definition Video (UHDV) sequences (4,320 x 7,680 @ 30 fps).

Design of a Multiband Full-Rate Ultra-Wideband Receiver in FPGA

MultiBand OFDM (MB-OFDM) UWB [1] is a short-range promising wireless technology for high data rate communications up to 480 Mbps. In this paper, we have designed and implemented in an Virtex-6 FPGA an MB-OFDM UWB receiver for the highest data rate of 480 Mbps. To test the system, we have also implemented an MB-OFDM transmitter and an AWGN generator in VHDL and determined the bit error rates at the receiver running in an FPGA.