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相关概念视频

Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

268
Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Aggregates Classification01:29

Aggregates Classification

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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...
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相关实验视频

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基于深度度度度学习的分类,用于路面困境图像.

Yuhui Li1,2, Jiaqi Wang3, Bo Lü3

  • 1School of Physics, Northeast Normal University, Changchun 130024, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

本研究引入了用于路面危险分类的深度度度度学习方法,提高了准确性,并使道路检查的增量学习成为可能. 该方法通过有效地识别各种各样的路面问题,以有限的数据来增强道路维护.

关键词:
软 三倍损失 三倍损失深度度度学学习 (deep metric learning) 是一种深度度度学.图像的分类图像的分类.路面应急检测 路面应急检测类似度指标是相似度指标.

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 传统的路面危险分类方法与大量数据集要求和学习新类别的能力有限而扎.
  • 在人行道危险图像中,高类内差异和低类间差异对准确的自动化分析构成重大挑战.

研究的目的:

  • 开发一种基于深度度度量学学习的新方法,用于路面危险分类.
  • 克服现有方法的局限性,包括数据依赖和增量学习.
  • 通过改进应急识别,提高道路检查的准确性和效率.

主要方法:

  • 设计了一个卷积神经网络 (CNN) 头部,使用SoftTriple损失训练的多集群中心体.
  • 通过结合样本相似性和类先验来解决数据不平衡,采用了自适应权重策略.
  • 使用软标签技术,通过评估与支持集样本的相似性来减少标签噪声.

主要成果:

  • 与传统的监督学习相比,拟议的方法在UAV-PDD2023数据集上取得了更高的性能.
  • 显示了比监督学习方法高3.2%的宏观回忆率.
  • 与iCaRL增量学习相比,宏观F1和加权F1分数分别提高了6.7%和8.5%.

结论:

  • 开发的深度度度度学习方法有效地分类了人行道困境,即使有很高的类内差异和很低的类间区别.
  • 该方法能够逐步学习新类别并处理数据不平衡的能力使其适合于现实世界的道路检查.
  • 这项研究为不断变化的路面危险类型和局限注释的场景提供了强大的解决方案,推进了自动化道路维护.