Constrained linear regression models for symbolic interval-valued variables

This paper introduces an approach to fitting a constrained linear regression model to interval-valued data. Each example of the learning set is described by a feature vector for which each feature value is an interval. The new approach fits a constrained linear regression model on the midpoints and range of the interval values assumed by the variables in the learning set. The prediction of the lower and upper boundaries of the interval value of the dependent variable is accomplished from its midpoint and range, which are estimated from the fitted linear regression models applied to the midpoint and range of each interval value of the independent variables. This new method shows the importance of range information in prediction performance as well as the use of inequality constraints to ensure mathematical coherence between the predicted values of the lower () and upper () boundaries of the interval. The authors also propose an expression for the goodness-of-fit measure denominated determination coefficient. The assessment of the proposed prediction method is based on the estimation of the average behavior of the root-mean-square error and square of the correlation coefficient in the framework of a Monte Carlo experiment with different data set configurations. Among other aspects, the synthetic data sets take into account the dependence, or lack thereof, between the midpoint and range of the intervals. The bias produced by the use of inequality constraints over the vector of parameters is also examined in terms of the mean-square error of the parameter estimates. Finally, the approaches proposed in this paper are applied to a real data set and performances are compared.     

Vehicle inductive signatures recognition using a Madaline neural network

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.     

Pattern classification with mixtures of weighted least-squares support vector machine experts

Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the kernel artifice of nonlinearly mapping input data into high-dimensional feature spaces toward the automatic construction of better discriminating linear decision boundaries. Among several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered.     

Making random permutations from physically unclonable constants

The recent availability of reliable schemes for physically unclonable constants (PUC) opens interesting possibilities in the field of security. In this paper, we explore the possibility of using PUCs to embed in a chip random permutations to be used, for example, as building blocks in cryptographic constructions such as sponge functions, substitution–permutation networks, and so on. We show that the most difficult part is the generation of random integers using as the only randomness source the bit-string produced by the PUC. In order to solve the integer generation problem, we propose a partial rejection method that allows the designer to trade-off between entropy and efficiency. The results show that the proposed schemes can be implemented with reasonable complexity.     

Characteristics of WWTP sludge after drying in greenhouse for agricultural purposes

The sludge generated by sewage treatment which meets regulatory standards can be used in agriculture. With this understanding, the focus of this study is the evaluation of the agricultural characteristics and inorganic substances in excess activated sludge, which was subjected to drying in a greenhouse. The variables (factor) evaluated during the drying process were: type of sludge (digested or not digested), addition of lime to the sludge, and the physical layout and rotation of sludge in the greenhouse. The parameters monitored for this assessment were moisture, volatile solids and pH. The greenhouse cover and sides were made of translucent plastic to allow the penetration of solar radiation and prevent water from entering. A impermeable floor was used. The sludge was generated in sewage treatment plants located in the metropolitan region of Grande Vitória, Espírito Santo, Brazil. The solar drying of wastewater sludge in a greenhouse presented satisfactory results.     

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

Electroluminescence of a device based on europium β-diketonate with phosphine oxide complex

Rare earth (RE) ions have spectroscopic characteristics to emit light in narrow lines, which makes RE complexes with organic ligands candidates for full color OLED (Organic Light Emitting Diode) applications. In particular, β-diketone rare earth (RE³⁺) complexes show high fluorescence emission efficiency due to the high absorption coefficient of the β-diketone and energy transfer to the central ion. In this work, the fabrication and the electroluminescent properties of devices containing a double and triple-layer OLED using a new β-diketone complex, [Eu(bmdm)₃(tppo)₂], as transporting and emitting layers are compared and discussed. The double and triple-layer devices based on this complex present the following configurations respectively: device 1: ITO/TPD (40 nm)/[Eu(bmdm)₃(tppo)₂] (40 nm)/Al (150 nm); device 2: ITO/TPD (40 nm)/[Eu(bmdm)₃(tppo)₂] (40 nm)/Alq₃ (20 nm)/Al (150 nm) and device 3: ITO/TPD (40 nm)/bmdm-ligand (40 nm)/Al (150 nm), were TPD is (N,N′-diphenyl-N,N′-bis(3-methylphenyl)-1,1-biphenil-4,4-diamine) and bmdm is butyl methoxy-dibenzoyl-methane. All the films were deposited by thermal evaporation carried out in a high vacuum system. These devices exhibit high intensity photo- (PL) and electro-luminescent (EL) emission. Electroluminescence spectra show emission from Eu³⁺ ions attributed to the ⁵D₀ to ⁷F_J (J = 0, 1, 2, 3 and 4) transitions with the hypersensitive ⁵D₀ → ⁷F₂ transition (around 612 nm) as the most prominent one. Moreover, a transition from ⁵D₁ to ⁷F₁ is also observed around 538 nm. The OLED light emission was almost linear with the current density. The EL CIE chromaticity coordinates (X = 0.66 and Y = 0.33) show the dominant wavelength, λ_d = 609 nm, and the color gamut achieved by this device is 0.99 in the CIE color space.     

Establishing quality performance of distribution companies based on yardstick regulation

This paper proposes a procedure and methodology for performance target setting related to continuity metrics in electricity distribution networks. This proposal deals with one of the key issues in the monopoly regulation, which is either the reduction of the information asymmetry among economic agents or the emulation of a competitive environment in industry segments usually considered as natural monopolies. This paper develops an approach toward standard definitions for Customer Average Interruption Duration Index (DEC) and Customer Average Interruption Frequency Index (FEC) based on inter-companies comparative analysis. This approach introduces productive efficiency concepts to the definition of network performance. In order to do so, the data envelopment analysis (DEA) technique, used for defining the efficiency frontier, was combined with the dynamic cluster technique, oriented toward models of identification of similar networks. A technique for comparative analysis was used for the definition of the network expected performance. Examples with the Brazilian electric systems are provided to clarify the methodology suggested by this paper.     

Spin Polarization by Tilted Magnetic Field in Wide Ga1?xAlxAs Parabolic Quantum Wells

Wide parabolic wells can be created by properly controlling the Al content during the growth of successive Ga_1–xAl_xAs thin layers. Under a tilted magnetic field these systems present interesting transport properties, which are associated to their composition dependent g-factor. We present an exact solution for the eigenstates of an electron gas inside such a quantum well. We calculate the renormalized cyclotron frequencies as functions of the angle of tilt, as well as the density of states, and the Fermi level. We discuss the conditions for the existence of spin-polarized charge.