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相关概念视频

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

71
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
71
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Aggregates Classification01:29

Aggregates Classification

298
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
298
Sampling Plans01:23

Sampling Plans

163
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...
163
Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

93
Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
93
Types of Aggregate Grading01:15

Types of Aggregate Grading

381
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
381

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相关实验视频

Updated: May 24, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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数据流集群:为增量集群融合过程引入递归可扩展聚合函数.

A Urio-Larrea, H Camargo, G Lucca

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    |March 3, 2025
    PubMed
    概括

    本研究引入了增量模糊数据流集群的新方法,使模型能够适应连续数据. 这种新的方法改善了集群比较和融合,优于现有的算法.

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    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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    科学领域:

    • 机器学习 机器学习
    • 数据挖掘 数据挖掘
    • 人工智能的人工智能

    背景情况:

    • 数据流 (DS) 学习需要因大量的连续数据而增加模型更新.
    • 模糊的DS集群包括将数据吸收到现有的集群中或创建新的集群.
    • 重叠的集群需要渐进的合并策略.

    研究的目的:

    • 为了正式化和操作化增量模糊集群比较用于数据流学习.
    • 为增量融合过程开发强大的集群比较措施 (CMs).
    • 为了提高模糊数据流集群算法的性能.

    主要方法:

    • 引入了用于增量融合的递归可扩展 (RE) 聚合函数.
    • 提出了两个集群比较方法:基于RE函数的相似性和重叠性.
    • 将增量CM集成到d-FuzzStream算法中进行分析.

    主要成果:

    • 证明了模糊星团的有效增量比较和融合.
    • 拟议的RE聚合功能可以实现高效的在线处理.
    • 增强的d-FuzzStream算法显示性能有所改善.

    结论:

    • 新的增量集群比较方法为模糊的DS集群提供了正式的基础.
    • 新方法提高了数据流集群的适应性和准确性.
    • 这项工作在现有的最先进的DS集群算法上取得了重大进展.