Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

216
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
216
Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
181
Sample Preparation for Analysis: Overview01:21

Sample Preparation for Analysis: Overview

217
Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...
217

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Axial acoustic radiation force on a sphere embedded in a gel phantom within a focused ultrasound field: simulation and measurement.

Ultrasonics·2026
Same author

Visualization of Cu-Cluster-Driven CO<sub>2</sub> Electroreduction by Spatiotemporally Coupled In-Situ Electrochemical Mass Spectrometry.

Journal of the American Chemical Society·2026
Same author

Advances in Nanobody-Based Platforms for Precision Cancer Diagnosis and Therapy.

Polymer science & technology (Washington, D.C.)·2026
Same author

Direct visualization of inner-sphere electrocatalytic reactions as they occur at detachable electrochemical interfaces.

Chemical science·2026
Same author

A follow-up study on the effect of exercise intervention on the executive functions of typical primary students and language learning difficulties.

Frontiers in psychology·2026
Same author

Ferroptosis in Neuropsychiatric and Neurodegenerative Disorders: Shared Mechanisms and Disease-Specific Signatures.

Pharmaceuticals (Basel, Switzerland)·2026

相关实验视频

Updated: Jun 27, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.6K

SLKIR:用于从空中交通管制指令中提取关键信息的框架 使用小样本学习.

Peiyuan Jiang1, Chen Zeng2, Weijun Pan3

  • 1Air Traffic Management College, Civil Aviation Flight University of China, Guanghan, 618307, China.

Scientific reports
|April 29, 2024
PubMed
概括

本研究介绍了SLKIR,这是一种用于空中交通管制 (ATC) 指令中的关键信息识别 (KIR) 的深度学习框架. SLKIR显著提高了准确性,在空中交通管制数据集上表现优于现有的模型.

更多相关视频

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

248
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K

相关实验视频

Last Updated: Jun 27, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.6K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

248
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K

科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 空中交通管理是指空中交通管理.

背景情况:

  • 关键信息识别 (KIR) 对于空中交通管制 (ATC) 自动化至关重要.
  • 在ATC KIR中存在有限的研究,这造成了与行业进步的差距.

研究的目的:

  • 引入一个创新的端到端深度学习框架,SLKIR,以增强空中管制指令中的KIR.
  • 在ATC KIR中解决研究的稀缺性,弥合学术发现和行业实践之间的差距.

主要方法:

  • 开发了SLKIR,这是一个端到端的深度学习框架,用于关键信息识别 (KIR).
  • 纳入了一个新的多头本地词汇协会注意力 (MHLA) 机制,用于边界词的识别.
  • 实施了以提示为重点的任务,以提高语义理解,并针对类别不平衡进行量身定制的损失函数优化.

主要成果:

  • SLKIR在两个不同的ATC指令数据集上表现出卓越的性能.
  • 与W2NER相比,在商业飞行数据集上F1得分增加了3.65%,在训练飞行数据集上增加了12.8%.
  • 这项研究是首次将小样本学习应用于ATC中的KIR.

结论:

  • SLKIR框架有效地提高了空中交通管制指令中的关键信息识别.
  • 新的MHLA机制和量身定制的优化策略显著提高了识别准确性.
  • 在应用深度学习用于自动化空中管制数据处理方面,SLKIR代表了重大进步.