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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

624
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
624
Types Of Transformers01:16

Types Of Transformers

1.8K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.8K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.5K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.5K
Transformers01:26

Transformers

2.3K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
2.3K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

680
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
680
Three-Winding Transformers01:19

Three-Winding Transformers

926
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
926

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Related Experiment Videos

BigBWA: approaching the Burrows-Wheeler aligner to Big Data technologies.

José M Abuín1, Juan C Pichel1, Tomás F Pena1

  • 1CITIUS, Universidade de Santiago de Compostela, Spain and.

Bioinformatics (Oxford, England)
|September 2, 2015
PubMed
Summary

BigBWA enhances the Burrows-Wheeler Aligner (BWA) using Big Data technology for faster performance. This fault-tolerant tool improves execution times without altering the original BWA source code.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • The Burrows-Wheeler Aligner (BWA) is a widely used tool for aligning sequencing reads.
  • Large-scale genomic datasets necessitate efficient and scalable alignment tools.
  • Existing BWA implementations may face performance limitations with big data.

Purpose of the Study:

  • To introduce BigBWA, a novel tool designed to accelerate BWA performance.
  • To leverage Big Data technologies for enhanced sequence alignment.
  • To provide a fault-tolerant solution without modifying the original BWA source code.

Main Methods:

  • Integration of the Hadoop framework with the Burrows-Wheeler Aligner.
  • Development of BigBWA to process large datasets efficiently.
  • Benchmarking BigBWA against the standard BWA for performance evaluation.

Main Results:

  • Significant reductions in execution times were observed using BigBWA.
  • BigBWA demonstrated robust fault tolerance during alignment tasks.
  • No modifications to the original BWA source code were required for implementation.

Conclusions:

  • BigBWA offers a substantial performance improvement for BWA.
  • The tool provides a scalable and fault-tolerant solution for big data alignment.
  • BigBWA is a valuable addition to the bioinformatics toolkit for large-scale genomics.