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相关概念视频

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.9K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.9K
Precipitation Processes01:12

Precipitation Processes

490
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...
490
Precipitation Gravimetry01:03

Precipitation Gravimetry

6.7K
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...
6.7K
Types of Coprecipitation01:10

Types of Coprecipitation

666
Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
666
Precipitation of Ions03:11

Precipitation of Ions

28.0K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
28.0K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

1.9K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
1.9K

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相关实验视频

Updated: Jul 23, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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LSTMAtU-Net:一个基于ECSA模块的降水现播模型.

Huantong Geng1,2, Xiaoyan Ge1, Boyang Xie1

  • 1School of Computer, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究引入了LSTMAtU-Net用于降水现在预测,改进了基于雷达的降雨预测. 新模型通过更好地保存图像细节,提高了中高强度降水的准确性.

关键词:
卷积式LSTM (卷积式LSTM) 是一种有效的通道和空间注意力 (ECSA)这是一个U-Net架构.降水 现在 播放 降水

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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科学领域:

  • 气象学 天气学
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 降水现在预测使用气象数据预测未来0-2小时的降雨情况.
  • 目前的深度学习方法缺乏物理约束,导致预测降雨图像中的细节丢失.

研究的目的:

  • 提出一个新的深度学习框架,LSTMAtU-Net,以提高降水现在的预测准确性.
  • 为了解决当前物理不受约束的深度学习模型中图像细节的丢失.

主要方法:

  • 在基于U-Net架构的基础上开发了LSTMATU-Net.
  • 包含一个卷积式LSTM (ConvLSTM) 单元,具有垂直流量和深度可分离的卷积.
  • 引入了一个高效通道和空间注意力 (ECSA) 模块来增强特征学习.

主要成果:

  • 与测试数据集上的现有模型相比,LSTMAtU-Net模型表现出更高的性能.
  • 该模型显著提高了中高强度降水现预测的准确性.
  • 在ECSA模块加强了对降水图像细节的关注.

结论:

  • LSTMAtU-Net有效地解决了降水现观测中的细节损失问题.
  • 拟议的模型为短期降雨预测提供了一个物理知情和准确的方法.