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相关概念视频

Machines01:19

Machines

573
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
573
Machines: Problem Solving II01:30

Machines: Problem Solving II

661
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
661
Machines: Problem Solving I01:22

Machines: Problem Solving I

709
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
709
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

486
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
486
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

769
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
769

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相关实验视频

Updated: Jan 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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机器学习应用程序的基于知识的标签的自动杂草细分.

Thuan Ha1, Kathryn Aldridge2, Eric Johnson2

  • 1Department of Plant Sciences, University of Saskatchewan, Saskatoon, S7N5A8, Canada. thuan.ha@usask.ca.

Scientific reports
|January 26, 2026
PubMed
概括
此摘要是机器生成的。

一个自动化工作流程准确地标记了用于精密农业的无人机 (UAV) 图像中的景观特征. 这减少了为机器学习杂草检测创建数据集的手工工作.

关键词:
自动标签的自动化标签电感认知 (ECognition) 是一种图像细分 图像细分 图像细分精准农业 精准农业 精准农业无人机RGB图像 无人机RGB图像杂草检测器可以检测杂草.

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科学领域:

  • 农业科学 农业科学
  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉

背景情况:

  • 准确的景观特征分类对于精准农业至关重要,包括杂草控制和可变率应用.
  • 机器和深度学习模型为实时杂草检测提供了潜力,但需要广泛的标记数据集,这些数据集需要大量的劳动力来创建.

研究的目的:

  • 开发和评估用于无人机 (UAV) RGB 图像的自动化功能标签工作流.
  • 为了减少用于农业机器学习应用程序生成标记数据集所需的手工工作和时间.

主要方法:

  • 使用eCognition软件 (v9.5) 开发了一个自动化工作流程,集成了细分,线路检测,距离映射,卷积过,形态操作和值.
  • 使用高分辨率无人机RGB图像 (0.088厘米) 来研究一个研究领域的各种杂草物种 (kochia,野麦,野末,假剪刀) 和小麦.
  • 植被指数,包括植被颜色指数和过量绿色指数,用于区分作物,杂草和土壤.

主要成果:

  • 在没有人工培训标签的情况下,自动化工作流实现了87%的整体准确性,卡帕系数为0.81,用于对景观特征进行分类.
  • 空间算法和植被指数的组合有效地将作物和杂草从土壤背景中分离出来.
  • 随机分布的标签点和一个混矩阵被用于性能评估.

结论:

  • 开发的自动化工作流显示了加速创建用于机器学习和深度学习的标记数据集的巨大潜力.
  • 这种方法可以大大减少手工劳动,同时保持用于杂草检测和其他精准农业任务的高分类准确性.
  • 未来的研究将专注于提高工作流的跨领域的可转移性,获取日期和实验条件.