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Related Concept Videos

Transformers01:26

Transformers

1.1K
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...
1.1K
Types Of Transformers01:16

Types Of Transformers

977
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...
977
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

157
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...
157
Transformers in Distribution System01:27

Transformers in Distribution System

103
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...
103
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

74
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
74
Energy Losses in Transformers01:21

Energy Losses in Transformers

876
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...
876

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Automatic Mapping of Terminology Items with Transformers.

Alberto Purpura1, Joao Bettencourt-Silva1, Natasha Mulligan1

  • 1IBM Research, Dublin, Ireland.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to map biomedical ontology classes to terminology terms, significantly improving knowledge graph creation for clinical data analysis. The system achieves human-level performance, aiding researchers in organizing complex medical information.

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Area of Science:

  • Bioinformatics
  • Medical Informatics
  • Natural Language Processing

Background:

  • Biomedical ontologies are crucial for analyzing clinical text data, organizing information via class hierarchies.
  • Mapping concepts to expert-developed terminologies is essential for building knowledge graphs but is labor-intensive.
  • Current methods for mapping ontology classes to terminology terms require extensive manual review.

Purpose of the Study:

  • To present an automated approach and repeatable framework for learning mappings between ontology classes and terminology terms.
  • To leverage vocabularies from the Unified Medical Language System (UMLS) metathesaurus for this mapping process.
  • To improve the efficiency and accuracy of knowledge graph construction in biomedical informatics.

Main Methods:

  • Developed an automated system to learn mappings between biomedical ontology classes and UMLS terminology terms.
  • Created a repeatable framework to ensure consistency and scalability of the mapping process.
  • Utilized Natural Language Processing (NLP) techniques to facilitate the extraction and organization of information.

Main Results:

  • The proposed automated system achieved performance comparable to human experts in mapping ontology classes to terminology terms.
  • Demonstrated substantial performance improvements over existing systems developed by the National Library of Medicine.
  • Successfully created a framework for efficient and accurate knowledge graph construction.

Conclusions:

  • The automated approach significantly reduces the manual effort required for mapping biomedical ontologies to terminologies.
  • The developed framework offers a scalable and effective solution for researchers in biomedical informatics.
  • This advancement facilitates more robust knowledge graph generation for clinical data analysis and inference.