Improved high-speed de-excitation system for brushless synchronous machines tested on a 20 MVA hydro-generator

Synchronous machines with brushless excitation have the disadvantage that the field winding is not accessible for the de-excitation of the machine. This means that, despite the proper operation of the protection system, the slow de-excitation time constant may produce severe damage in the event of an internal short circuit. A high-speed de-excitation system for these machines was developed, and this study presents the continuation of a previously published study. This study presents the design by computer simulation and the results of the first commissioning of this de-excitation system in a commercial 20 MVA hydro-generator. The de-excitation is achieved by inserting resistance in the field circuit, obtaining a dynamic response similar to that achieved in machines with static excitation. In this case, a non-linear discharge resistor was used, making the dynamic response even better.

Field-winding fault detection in synchronous machines with static excitation through frequency response analysis.

Frequency Response Analysis is a well-known technique for the diagnosis of power transformers. Currently, this technique is under research for its application in rotary electrical machines. This paper presents significant results on the application of Frequency Response Analysis to fault detection in field winding of synchronous machines with static excitation. First, the influence of the rotor position on the frequency response is evaluated. Secondly, some relevant test results are shown regarding ground fault and inter-turn fault detection in field windings at standstill condition. The influence of the fault resistance value is also taken into account. This paper also studies the applicability of Frequency Response Analysis in fault detection in field windings while rotating. This represents an important feature because some defects only appear with the machine rated speed. Several laboratory test results show the applicability of this fault detection technique in field windings at full speed with no excitation current.

New Fault-Resistance Estimation Algorithm for Rotor-Winding Ground-Fault Online Location in Synchronous Machines With Static Excitation

New Fault-Resistance Estimation Algorithm for Rotor-Winding Ground-Fault Online Location in Synchronous Machines With Static Excitation

Reciprocal secretion of proteins by the bacterial type III machines of plant and animal pathogens suggests universal recognition of mRNA targeting signals

Bacterial pathogens of both animals and plants use type III secretion machines to inject virulence proteins into host cells. Although many components of the secretion machinery are conserved among different bacterial species, the substrates for their type III pathways are not. The Yersinia type III machinery recognizes some secretion substrates via a signal that is encoded within the first 15 codons of yop mRNA. These signals can be altered by frameshift mutations without affecting secretion of the encoded polypeptides, suggesting a mechanism whereby translation of yop mRNA is coupled to the translocation of newly synthesized polypeptide. We report that the type III machinery of Erwinia chrysanthemi cloned in Escherichia coli recognizes the secretion signals of yopE and yopQ. Pseudomonas syringae AvrB and AvrPto, two proteins exported by the recombinant Erwinia machine, can also be secreted by the Yersinia type III pathway. Mapping AvrPto sequences sufficient for the secretion of reporter fusions in Yersinia revealed the presence of an mRNA secretion signal. We propose that 11 conserved components of type III secretion machines may recognize signals that couple mRNA translation to polypeptide secretion.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Homologous recombination promoted by reverse transcriptase during copying of two distinct RNA templates.

Retroviruses are known to mutate at high rates. An important source of genetic variability is recombination taking place during reverse transcription of internal regions of the two genomic RNAs. We have designed an in vitro model system, involving genetic markers carried on two RNA templates, to allow a search for individual recombination events and to score their frequency of occurrence. We show that Moloney murine leukemia virus reverse transcriptase alone promotes homologous recombination efficiently. While RNA concentration has little effect on recombination frequency, there is a clear correlation between the amount of reverse transcriptase used in the assay and the extent of recombination observed. Under conditions mimicking the in vivo situation, a rate compatible with ex vivo estimates has been obtained.

Reverse Vending Machines: A Potential Solution for the Florida Plastic Waste Management Challenge

The waste of plastic beverage bottles creates environmental problems and takes up a large volume of landfill space. The high rate of consumption of plastics in the State of Florida is challenging the disposal capacity of waste authorities. The lack of the reverse vending machines in the State of Florida, including applicable scientific or technical literature represented an opportunity for this research to discuss the applicability of this equipment as a potential solution for the management of the plastic waste in Florida. With this research document, I will propose a recycling system for plastic bottles made with PET based on the implementation of reverse vending machines, stressing the importance of the creation of policies that promote recycling and public participation.

Detailing for a research centre in Antarctica: An experiment to force students to be creative instead of copying standard solutions

In order to equip architecture students with ambitious detail designing ability, related courses of the architecture programs should deal with subjects which are rather rare and unusual for real life practices in order to prevent students copying standard details. In 2015 an innovative project brief has been given to architecture students of Istanbul Technical University. The scenario given in the brief is to design a research station for the first group of Turkish scientist, to be built in one of the coldest and most arid regions on earth; the Antarctica. The performance requirements given in the brief were determined to prevent the students from copying details from any kind of resources as the total number of details generated in real life for those conditions are very limited and specific. The method used has demonstrated a great success and creative detail solutions were generated by the students. In the paper, the innovative coursework brief for bettering the detail design ability of architecture students is explained and the output of the studio is presented.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.