Stress analysis in oral obturator prostheses over parallel and tilted implants: photoelastic imaging

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Metabolic profiling by ultra-performance liquid chromatography-mass spectrometry and parallel factor analysis for the determination of disease biomarkers in Eucalyptus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Harmony Search applied for Support Vector Machines Training Optimization

Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.

Improvements in the score matrix calculation method using parallel score estimating algorithm

The increasing amount of sequences stored in genomic databases has become unfeasible to the sequential analysis. Then, the parallel computing brought its power to the Bioinformatics through parallel algorithms to align and analyze the sequences, providing improvements mainly in the running time of these algorithms. In many situations, the parallel strategy contributes to reducing the computational complexity of the big problems. This work shows some results obtained by an implementation of a parallel score estimating technique for the score matrix calculation stage, which is the first stage of a progressive multiple sequence alignment. The performance and quality of the parallel score estimating are compared with the results of a dynamic programming approach also implemented in parallel. This comparison shows a significant reduction of running time. Moreover, the quality of the final alignment, using the new strategy, is analyzed and compared with the quality of the approach with dynamic programming.

Bit-parallel coprocessor for standard ECC-GF(2m) on FPGA

This paper presents the design of a high-speed coprocessor for Elliptic Curve Cryptography over binary Galois Field (ECC- GF(2m)). The purpose of our coprocessor is to accelerate the scalar multiplication performed over elliptic curve points represented by affine coordinates in polynomial basis. Our method consists of using elliptic curve parameters over GF(2163) in accordance with international security requirements to implement a bit-parallel coprocessor on field-programmable gate-array (FPGA). Our coprocessor performs modular inversion by using a process based on the Stein's algorithm. Results are presented and compared to results of other related works. We conclude that our coprocessor is suitable for comparing with any other ECC-hardware proposal, since its speed is comparable to projective coordinate designs.

Using Threads to Overcome Synchronization Delays in Parallel Multiple Progressive Alignment Algorithms

Sao Paulo State Research Foundation-FAPESP

Seasonal variation of biotic and abiotic parameters in parallel neotropical fishponds

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Recovery Tests of an Hybrid Current Limiter Composed of a SFCL in Parallel to an Air-Core Power Reactor

The resistive-type superconducting fault current limiters (RSFCL) prototypes using YBCO-coated conductors have shown current limitation for medium voltage class applications for acting time up to 80 ms. By connecting an air-core reactor in parallel with the RSFCL, thus making an hybrid current limiter, one can extend the acting time for up to 1 s. In this work, we report the performance of a hybrid current limiter subjected to an AC peak fault current of 2 kA during 1 s for which within the first 80 ms the SFCL limits the current concurrently with the air-core reactor, and for the remaining 920 ms, only the air-core reactor limits the current. In order to evaluate the actual conditions for subsequent reconnection of RSFCL to the power grid, the hybrid fault current limiter was tested varying the time interval for recovery from 900 ms and 1.2 s, followed again by the concurrent operation of the hybrid limiter during 1 s (SFCL during 80 ms). From this evaluation test, the recovery time can be measured and compared using the voltage peak generated in superconducting module from the first and second fault test. The recovery time was also determined through the pulsed current method (PCM) on short-length sample test. The results showed that the fault current was limited from 1.9 kA down to 514 A after 1 cycle of 60 Hz frequency, with recovery time lower than 1.2 s for two subsequent fault current tests.

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Instantaneous complex power applied to three-phase machines

The theory presented in this paper was primarily developed to give a physical interpretation for the instantaneous power flow on a three-phase induction machine, without a neutral conductor, on any operational state and may be extended to any three-phase load. It is a vectorial interpretation of the instantaneous reactive power theory presented by Akagi et al. Which, believe the authors, isn't enough developed and its physical meaning not yet completely understood. This vectorial interpretation is based on the instantaneous complex power concept defined by Torrens for single-phase, ac, steady-state circuits, and leads to a better understanding of the power phenomenon, particularly of the distortion power. This concept has been extended by the authors to three-phase systems, through the utilization of the instantaneous space vectors. The results of measurements of instantaneous complex power on a self-excited induction generator's terminals, during an over-load application transient, are presented for illustration. The compensation of reactive power proposed by Akagi is discussed and a new horizon for the theory application is opened.

Expert system to detect incipient fauts in rotating machines using fuzzy logic and virtual instrumentation

This paper refers to the design of an expert system that captures a waveform through the use of an accelerometer, processes the signal and converts it to the frequency domain using a Fast Fourier Transformer to then, using artificial intelligence techniques, specifically Fuzzy Reasoning, it determines if there is any failure present in the underlying mode of the equipment, such as imbalance, misalignment or bearing defects.

Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

A Randomized, Naturalistic, Parallel-Group Study for the Long-Term Treatment of Panic Disorder With Clonazepam or Paroxetine

This long-term extension of an 8-week randomized, naturalistic study in patients with panic disorder with or without agoraphobia compared the efficacy and safety of clonazepam (n = 47) and paroxetine (n = 37) over a 3-year total treatment duration. Target doses for all patients were 2 mg/d clonazepam and 40 mg/d paroxetine (both taken at bedtime). This study reports data from the long-term period (34 months), following the initial 8-week treatment phase. Thus, total treatment duration was 36 months. Patients with a good primary outcome during acute treatment continued monotherapy with clonazepam or paroxetine, but patients with partial primary treatment success were switched to the combination therapy. At initiation of the long-term study, the mean doses of clonazepam and paroxetine were 1.9 (SD, 0.30) and 38.4 (SD, 3.74) mg/d, respectively. These doses were maintained until month 36 (clonazepam 1.9 [ SD, 0.29] mg/d and paroxetine 38.2 [SD, 3.87] mg/d). Long-term treatment with clonazepam led to a small but significantly better Clinical Global Impression (CGI)-Improvement rating than treatment with paroxetine (mean difference: CGI-Severity scale -3.48 vs -3.24, respectively, P = 0.02; CGI-Improvement scale 1.06 vs 1.11, respectively, P = 0.04). Both treatments similarly reduced the number of panic attacks and severity of anxiety. Patients treated with clonazepam had significantly fewer adverse events than those treated with paroxetine (28.9% vs 70.6%, P < 0.001). The efficacy of clonazepam and paroxetine for the treatment of panic disorder was maintained over the long-term course. There was a significant advantage with clonazepam over paroxetine with respect to the frequency and nature of adverse events.