Multilevel high-voltage pulse generation based on a new modular solid-state switch

This paper describes a modular solid-state switching cell derived from the Marx generator concept to be used in topologies for generating multilevel unipolar and bipolar high-voltage (HV) pulses into resistive loads. The switching modular cell comprises two ON/OFF semiconductors, a diode, and a capacitor. This cell can be stacked, being the capacitors charged in series and their voltages balanced in parallel. To balance each capacitor voltage without needing any parameter measurement, a vector decision diode algorithm is used in each cell to drive the two switches. Simulation and experimental results, for generating multilevel unipolar and bipolar HV pulses into resistive loads are presented.

Solid-State bipolar Marx modulator modeling

A mathematical model that simulates the operation of a solid-state bipolar Marx modulator topology, including the influence of parasitic capacitances is presented and discussed as a tool to analyze the circuit behavior and to assist the design engineer to select the semiconductor components and to enhance the operating performance. Simulations show good agreement with experimental results, considering a four stage circuit assembled with 1200 V isolated gate bipolar transistors and diodes, operating at 1000 V dc input voltage and 1-kHz frequency, giving 4 kV and 10-mu s output pulses into several resistive loads. Results show that parasitic capacitances between Marx cells to ground can significantly load the solid-state switches, adding new operating circuit conditions.

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.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Solid-state sensory properties of Calix-Poly(Phenylene Ethynylene)s toward nitroaromatic explosives

This study is primarily focused in establishing the solid-state sensory abilities of several luminescent polymeric calix[4]arene-based materials toward selected nitroaromatic compounds (NACs), creating the foundations for their future application as high performance materials for detection of high explosives. The phenylene ethynylene-type polymers possessing bis-calix[4]arene scaffolds in their core were designed to take advantage of the known recognition abilities of calixarene compounds toward neutral guests, particularly in solid-state, therefore providing enhanced sensitivity and selectivity in the sensing of a given analyte. It was found that all the calix[4]arene-poly(para-phenylene ethynylene)s here reported displayed high sensitivities toward the detection of nitrobenzene, 2,4-dinitrotoluene and 2,4,6-trinitrotoluene (TNT). Particularly effective and significant was the response of the films (25-60 nm of thickness) upon exposure to TNT vapor (10 ppb): over 50% of fluorescence quenching was achieved in only 10 s. In contrast, a model polymer lacking the calixarene units showed only reduced quenching activity for the same set of analytes, clearly highlighting the relevance of the macrocyclics in promoting the signaling of the transduction event. The films exhibited high photostability (less than 0.5% loss of fluorescence intensity up to 15 min of continuous irradiation) and the fluorescence quenching sensitivity could be fully recovered after exposure of the quenched films to saturated vapors of hydrazine (the initial fluorescence intensities were usually recovered within 2-5 min of exposure to hydrazine).

Marx-Type solid-state bipolar modulator topologies: Performance comparison

The operation of generalized Marx-type solid-state bipolar modulators is discussed and compared with simplified Marx-derived circuits, to evaluate their capability to deal with various load conditions. A comparative analysis on the number of switches per cell, fiber optic trigger count, losses, and switch hold-off voltages has been made. A circuit topology is obtained as a compromise in terms of operating performance, trigger simplicity, and switching losses. A five-stage laboratory prototype of this circuit has been assembled using 1200 V insulated gate bipolar transistors (IGBTs) and diodes, operating with 1000 V dc input voltage and 1 kHz frequency, giving 5 kV bipolar pulses, with 2.5 mu s pulse width and 5 mu s relaxation time into resistive, capacitive, and inductive loads.

Modeling of a solid-state bipolar blumlein generator for N stages

This paper models an n-stage stacked Blumlein generator for bipolar pulses for various load conditions. Calculation of the voltage amplitudes in time domain at the load and between stages is described for an n-stage generator. For this, the reflection and transmission coefficients are mathematically modeled where impedance discontinuity occurs (i.e., at the junctions between two transmission lines). The mathematical model developed is assessed by comparing simulation results to experimental data from a two-stage Blumlein solid-state prototype.

Estudo técnico-económico para otimização do projeto e implementação de uma bomba de calor "heat machine"

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Fresh-state properties of self-compacting mortar and concrete with combined use of limestone filler and fly ash

This paper presents the results of a study on the behaviour of self-compacting concrete (SCC) in the fresh and hardened states, produced with binary and ternary mixes of fly ash (FA) and limestone filler (LF), using the method proposed by Nepomuceno. His method determines the SCC composition parameters in the mortar phase (self-compacting mortar - SCM) easily and efficiently, whilst guaranteeing the SCC properties in both the fresh and hardened states. For this, 11 SCMs were studied: one with cement (C) only; three with FA at 30%, 60% and 70% C substitution; three with LF at 30%, 60% and 70% C substitution; four with FA + LF in combinations of 10-20%, 20-10%, 20-40% and 40-20% C substitution. Once the composition of these mortars was defined, 18 SCC mixes were produced: 14 binary SCC mixes were produced with the seven binary mortar mixes, and four ternary SCC mixes were produced with the four ternary mortar mixes. In addition to the methodology proposed by Nepomuceno, the combined use of FA and LF in ternary mixtures was tested. The results confirmed that the method could yield SCC with adequate properties in both the fresh and hardened states. It was also possible to determine the SCC composition parameters in the mortar phase (self-compacting mortar - SCM) that will guarantee the SCC properties in both the fresh and hardened states, as confirmed through the optimized behaviour of the SCC in the fresh state and the promising results in the hardened state (compressive strength). The potential demonstrated by the joint use of LF and FA through the synergetic interaction of both additions is emphasized.

25 kV bipolar solid-state Marx generator for industrial food applications

The preliminary results from a bipolar industrial solidstate based Marx generator, developed for the food industry, capable of delivering 25 kV/250 A positive and negative pulses with 12 kW average power, are presented and discussed. This modular topology uses only four controlled switches per cell, 27 cells in total that can be charged up to 1000V each, the two extra cells are used for droop compensation. The triggering signals for all the switches are generated by a FPGA. Considering that biomaterials are similar to resistive type loads, experimental results from this new bipolar 25 kV modulator into resistive loads are presented and discussed.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.