Fast fault section estimation in distribution control centers using adaptive genetic algorithm

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

Micro-arc oxidation as a tool to develop multifunctional calcium-rich surfaces for dental implant applications

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

Development and test of resistive superconducting fault current limiter; acting time and its recovery conditions

Resistive-type of superconducting fault current limiters (RSFCL) have been developed for medium voltage class aiming to operate at 1 MVA power capacity and short time recovery (< 2 s). A RSFCL in form of superconducting modular device was designed and constructed using 50 m-length of YBCO coated conductor tapes for operation under 1 kV / 1 kA and acting time of 0.1 s. In order to increase the acting time the RSFCL was combined with an air-core reactor in parallel to increase the fault limiting time up to 1 s. The tests determined the electrical and thermal characteristics of the combined resistive/ inductive protection unit. The combined fault current limiter reached a limiting current of 583 A, corresponding to a limiting factor of 3.3 times within an acting time of up to 1 s.

Metamorphic evolution and P-T path of gneisses from Goiás Magmatic Arc in southwestern Goiás State

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

An Empirical Study of Fault Localization for End-User Programmers

End users develop more software than any other group of programmers, using software authoring devices such as e-mail filtering editors, by-demonstration macro builders, and spreadsheet environments. Despite this, there has been little research on finding ways to help these programmers with the dependability of their software. We have been addressing this problem in several ways, one of which includes supporting end-user debugging activities through fault localization techniques. This paper presents the results of an empirical study conducted in an end-user programming environment to examine the impact of two separate factors in fault localization techniques that affect technique effectiveness. Our results shed new insights into fault localization techniques for end-user programmers and the factors that affect them, with significant implications for the evaluation of those techniques.

Incorporating Varying Test Costs and Fault Severities into Test Case Prioritization

Test case prioritization techniques schedule test cases for regression testing in an order that increases their ability to meet some performance goal. One performance goal, rate offault detection, measures how quickly faults are detected within the testing process. In previous work we provided a metric, APFD, for measuring rate of fault detection, and techniques for prioritizing test cases to improve APFD, and reported the results of experiments using those techniques. This metric and these techniques, however, applied only in cases in which test costs and fault severity are uniform. In this paper, we present a new metric for assessing the rate of fault detection of prioritized test cases, that incorporates varying test case and fault costs. We present the results of a case study illustrating the application of the metric. This study raises several practical questions that might arise in applying test case prioritization; we discuss how practitioners could go about answering these questions.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

Associations among genotype, clinical phenotype, and intracellular localization of trafficking proteins in ARC syndrome

Arthrogryposisrenal dysfunctioncholestasis (ARC) syndrome is a rare autosomal recessive multisystem disorder caused by mutations in vacuolar protein sorting 33 homologue B (VPS33B) and VPS33B interacting protein, apicalbasolateral polarity regulator (VIPAR). Cardinal features of ARC include congenital joint contractures, renal tubular dysfunction, cholestasis, severe failure to thrive, ichthyosis, and a defect in platelet alpha-granule biogenesis. Most patients with ARC do not survive past the first year of life. We report two patients presenting with a mild ARC phenotype, now 5.5 and 3.5 years old. Both patients were compound heterozygotes with the novel VPS33B donor splice-site mutation c.1225+5G>C in common. Immunoblotting and complementary DNA analysis suggest expression of a shorter VPS33B transcript, and cell-based assays show that c.1225+5G>C VPS33B mutant retains some ability to interact with VIPAR (and thus partial wild-type function). This study provides the first evidence of genotypephenotype correlation in ARC and suggests that VPS33B c.1225+5G>C mutation predicts a mild ARC phenotype. We have established an interactive online database for ARC (https://grenada.lumc.nl/LOVD2/ARC) comprising all known variants in VPS33B and VIPAR. Also included in the database are 15 novel pathogenic variants in VPS33B and five in VIPAR. Hum Mutat 33:16561664, 2012. (c) 2012 Wiley Periodicals, Inc.

Tailored SERS substrates obtained with cathodic arc plasma ion implantation of gold nanoparticles into a polymer matrix

This manuscript reports on the fabrication of plasmonic substrates using cathodic arc plasma ion implantation, in addition to their performance as SERS substrates. The technique allows for the incorporation of a wide layer of metallic nanoparticles into a polymer matrix, such as PMMA. The ability to pattern different structures using the PMMA matrix is one of the main advantages of the fabrication method. This opens up new possibilities for obtaining tailored substrates with enhanced performance for SERS and other surface-enhanced spectroscopies, as well as for exploring the basic physics of patterned metal nanostructures. The architecture of the SERS-active substrate was varied using three adsorption strategies for incorporating a laser dye (rhodamine): alongside the nanoparticles into the polymer matrix, during the polymer cure and within nanoholes lithographed on the polymer. As a proof-of-concept, we obtained the SERS spectra of rhodamine for the three types of substrates. The hypothesis of incorporation of rhodamine molecules into the polymer matrix during the cathodic arc plasma ion implantation was supported by FDTD (Finite-Difference Time-Domain) simulations. In the case of arrays of nanoholes, rhodamine molecules could be adsorbed directly on the gold surface, then yielding a well-resolved SERS spectrum for a small amount of analyte owing to the short-range interactions and the large longitudinal field component inside the nanoholes. The results shown here demonstrate that the approach based on ion implantation can be adapted to produce reproducible tailored substrates for SERS and other surface-enhanced spectroscopies.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

An integrated marine data repository based on the arc marine model: a pilot Project in the Canary Islands

Trabajo realizado por Antonio Machado Carrillo, Juan Antonio Bermejo e Ignacio Lorenzo

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.