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相关概念视频

Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
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Rate-Determining Steps03:08

Rate-Determining Steps

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Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Method of Sections: Problem Solving II01:30

Method of Sections: Problem Solving II

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Consider an arbitrary truss structure composed of diagonal, vertical, and horizontal members fixed to the wall. To calculate the force acting on members CB, GB, and GH, method of sections can be used. The loads and lengths of the horizontal and vertical members are known parameters, as shown in the figure.
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Frames: Problem Solving II01:26

Frames: Problem Solving II

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Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.
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相关实验视频

Updated: Jun 29, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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关系增量场景图形生成由一个分割和征服管道与功能适配器.

Xuewei Li, Guangcong Zheng, Yunlong Yu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |April 8, 2024
    PubMed
    概括

    本研究介绍了关系增量场景图形生成 (RISGG) 以逐步学习图像中的对象关系. 拟议的DaCFA-Net通过解决增量学习中的旧类和背景转变,显著提高了绩效.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 场景图形生成 (SGG) 识别图像中对象之间的语义关系.
    • 增量学习带来了诸如旧阶级转移和SGG的背景转移等挑战.

    研究的目的:

    • 解决关系增量场景图形生成 (RISGG) 的挑战.
    • 为有效的RISGG提出一个新的网络,DaCFA-Net.

    主要方法:

    • 划分和征服 (DaC) 管道将关系类识别解,以减轻旧的类转移.
    • 功能适配器 (FA) 桥梁具有空间差距,并挖掘旧的关系信息.
    • 结合在一起的DAC和FA构成了DACFA-Net架构.

    主要成果:

    • 在基准数据集上,DaCFA-Net表现出了显著的绩效增长.
    • 拟议的方法在VG数据集上的现有SGG基线上实现了大约20%的改进.

    结论:

    • 在RISGG中,DaCFA-Net有效地处理了旧的阶级和背景转移.
    • 该方法提供了一个强大的解决方案,用于在场景图表生成中的增量学习.

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