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関連する概念動画

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

397
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
397
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
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...
1.2K
Precipitation Processes01:12

Precipitation Processes

5.6K
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
5.6K
Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.3K
Observational Learning01:12

Observational Learning

848
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
848
Precipitation Gravimetry01:03

Precipitation Gravimetry

14.7K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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関連する実験動画

Updated: Jan 19, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

ENSOの多年予測のためのディープラーニング

Yoo-Geun Ham1, Jeong-Hwan Kim2, Jing-Jia Luo3,4

  • 1Department of Oceanography, Chonnam National University, Gwangju, South Korea. ygham@jnu.ac.kr.

Nature
|September 20, 2019
PubMed
まとめ
この要約は機械生成です。

新しいディープラーニングモデルにより,エルニーニョ/南方振動 (ENSO) の予測が 1.5 年前まで大幅に改善されています. この高度なコンボリューションニューラルネットワーク (CNN) モデルは,ENSOイベントと海面温度を予測する現在のシステムを上回ります.

関連する実験動画

Last Updated: Jan 19, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

科学分野:

  • 気候科学
  • 人工知能
  • 海洋学

背景:

  • エルニーニョ/南方振動 (ENSO) の変動は,地球規模の極端な気候と生態系に影響を及ぼします.
  • ENSOの正確な長期予測は,効果的な政策と資源管理に不可欠です.
  • 現在の予測モデルは1年を超えるリードタイムで苦戦しています.

研究 の 目的:

  • ENSOの予測モデルを高度に熟練し,実行期間を延長する.
  • 予測の精度とメカニズム分析の改善のためにディープラーニングを活用する.
  • 既存の動的予測システムの限界を克服する.

主な方法:

  • モデルトレーニングの移転学習によるコンボリューションニューラルネットワーク (CNN) を利用した.
  • 歴史的な気候シミュレーションと再分析データに関するCNNの訓練 (1871年−1973年).
  • ニノ3.4指数と海面温度データ (1984年−2017年) を用いてモデルの性能を検証した.

主要な成果:

  • CNNモデルでは ENSOの予測を1年半前に達成しました
  • 最先端のダイナミックモデルと比較して,Nino3.4の相関能力が著しく高いことが示されています.
  • ENSOの重要な特徴である海面温度の地域分布を正確に予測した.
  • ヒートマップの分析は,ENSOの予測のために物理的に妥当な前駆体を使用することを確認しました.

結論:

  • ディープラーニング,特にCNNは ENSOの予測に強力な新しいアプローチを提供します.
  • 開発されたCNNモデルは 予測の精度とリードタイムにおいて既存のシステムを上回ります
  • このモデルは,ENSOメカニズムを理解し,気候予測を改善するための貴重なツールを提供します.