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相关概念视频

Reinforcements in Concrete01:25

Reinforcements in Concrete

122
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
122
Mixing Concrete01:30

Mixing Concrete

139
Concrete mixing ensures a homogenous blend where aggregates are well-coated with cement paste. Concrete mixing is typically done using two main types of mixers: batch and continuous. Batch mixers handle one batch at a time, thoroughly combining materials before discharging and receiving the next batch. In contrast, continuous mixers receive a steady flow of ingredients, mixing them consistently and discharging without interruption. Within batch mixers, tilting drum mixers mix with internal...
139
Design Example: Distributing Reinforcements in Concrete Sections01:22

Design Example: Distributing Reinforcements in Concrete Sections

120
The topic explores the practical aspects of adjusting steel reinforcements within a concrete beam section to meet specific design requirements. When designing a reinforced concrete beam, it is essential to distribute the steel reinforcements properly to ensure structural integrity and efficiency. The example provided details a scenario where a beam requires a total steel cross-section of 4 square inches. The engineer identifies that the available steel bars have a nominal diameter of 1.693...
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Reinforced Brick Masonry01:15

Reinforced Brick Masonry

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Reinforced brick masonry is an advanced construction technique that enhances the structural integrity of brick walls by incorporating steel reinforcements. These reinforcements are either placed within the hollow cores of bricks or sandwiched between two layers of masonry, known as wythes, and are then secured in place with grout. Grout is a fluid mixture composed of Portland cement, aggregate, and water, providing the necessary bonding agent for the steel and brick.
To fortify brick walls...
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Mixing Time01:19

Mixing Time

198
The concept of mixing time is significant in producing a uniform concrete mix with the required strength. The mixing period starts once all components are in the mixer. Initially, the mixer is charged with 10% of the water, followed by the consistent addition of solids and then 80% of the water. The remaining water is added later, within the first quarter of the mixing period. The minimum mixing time varies according to the mixer's capacity; for example, mixers with up to 1 cubic yard...
198
Associative Learning01:27

Associative Learning

461
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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相关实验视频

Updated: Jul 26, 2025

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering
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Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering

Published on: June 23, 2023

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强化混合学习的学习混合.

Yuan Le1, Fan Zhou2, Yang Bai2

  • 1Institute of Statistics and Apllied Mathematics, Anhui University of Finance and Economics, Bengbu, China.

Neural networks : the official journal of the International Neural Network Society
|June 12, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了混合学习的新增强化学习方法,将其视为马尔科夫决策过程 (MDP). 这种方法绕过了分布假设,在复杂,非凸的数据上表现优于预期最大化 (EM) 等传统算法.

关键词:
预期最大化最大化混合学习学习是一种混合学习.政策梯度的政策梯度是什么强化学习是一种强化学习.频谱嵌入是指光谱嵌入.

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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 混合学习对于数据分析至关重要,但传统方法通常依赖于强大的分布假设.
  • 预期最大化 (EM) 算法是一种常见的方法,但与非凸或错误指定的数据分布作斗争.

研究的目的:

  • 制定混合学习作为马尔科夫决策过程 (MDP).
  • 为混合学习开发一种强化学习算法,不需要先前的分布假设.
  • 证明算法的有效性,特别是在非凸和错误指定的数据上.

主要方法:

  • 混合学习作为马尔科夫决策过程 (MDP) 的制定.
  • 使用光谱图理论和线性差异分析 (LDA) 开发一种无模型奖励机制.
  • 强化学习原则的应用,以优化混合作业.

主要成果:

  • 理论上,MDP的客观值与数据的日志概率有关.
  • 强化算法在高斯假设成立时显示了与EM可比的性能.
  • 当模型假设被违反时,拟议的方法显著优于EM和其他聚类方法.

结论:

  • 强化混合学习方法为传统方法提供了灵活而强大的替代方案.
  • 这种基于MDP的框架有效地处理复杂的数据结构,而不需要明确的分布拟合.
  • 该方法在模型错误规范的场景中表现出卓越的性能.