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相关概念视频

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

63
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...
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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.7K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

194
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
194
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.7K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
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使用基于序列的生成对抗网络生成和归算数据.

Abdulrahman Al-Fakih1, A Koeshidayatullah2, Tapan Mukerji3

  • 1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum Minerals, 31261, Dhahran, Saudi Arabia. alja2014ser@gmail.com.

Scientific reports
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PubMed
概括
此摘要是机器生成的。

本研究引入了使用生成对抗网络 (GAN) 进行井日志数据归算和合成数据生成的新框架. 该方法通过准确填补数据空白并创建现实的日志数据来提高碳化合物勘探数据的可靠性.

关键词:
生成性的对抗性网络模型.序列GAN模型中的序列GAN模型.合成井日志数据生成数据生成时间序列模型时间序列模型好吧,日志数据归算是可以做到的.

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科学领域:

  • 地质科学 地质科学
  • 石油工程是石油工程中的一个.
  • 数据科学数据科学数据科学

背景情况:

  • 井日志数据对于碳化合物勘探至关重要,但往往含有空白和不准确.
  • 这些数据缺陷给水库评估带来了不确定性.
  • 有效的合成数据生成和缺失数据归算方法对于可靠的分析至关重要.

研究的目的:

  • 开发和评估一个新的框架,用于井日志数据的生成和归算.
  • 为应对不完整和不准确的井口日志数据所带来的挑战.
  • 提高地质科学中井日志数据的完整性和实用性.

主要方法:

  • 使用基于序列的生成对抗网络 (GAN).
  • 综合时间序列GAN (TSGAN) 用于合成数据生成和序列GAN (SeqGAN) 用于数据归算.
  • 在北海,荷兰的数据集上测试了框架,并进行了标准化的日志测量.

主要成果:

  • 与其他深度学习模型相比,归算方法在填补数据缺口方面表现出更高的准确性.
  • 实现了高的R平方值 (高达0.92) 和低的平均绝对误差 (MAE) 的归算.
  • 合成数据生成也产生了有希望的结果,R平方为0.92.

结论:

  • 拟议的基于GAN的框架有效地生成合成井日志数据,并赋值缺失的值.
  • 这种方法显著提高了储水库评估数据的完整性和可靠性.
  • 该研究为地质科学应用中井日志数据完整性设定了新的基准.