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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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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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Fruit Development, Structure, and Function01:58

Fruit Development, Structure, and Function

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Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
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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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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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果果边缘检测模型使用粗集和卷积神经网络.

Junqing Li1, Ruiyi Han2, Fangyi Li3

  • 1College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用深度学习和粗略集的新果果边缘检测模型 (RED). 红色模型提高了精度和稳定性,用于智能水果采摘和产量预测,即使在具有挑战性的条件下.

关键词:
速度更快的RCNNN果果果的果实果的果实果的果实边缘检测 边缘检测 边缘检测这是一个粗略的设置.目标检测 目标检测 目标检测

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Deep Neural Networks for Image-Based Dietary Assessment
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相关实验视频

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 农业技术 农业技术

背景情况:

  • 精确的果检测对于智能采摘和产量预测至关重要.
  • 现有的边缘检测方法与复杂的农业环境作斗争.

研究的目的:

  • 为智能农业开发一种有效的果边检测算法.
  • 为了提高果检测的准确性和稳定性.

主要方法:

  • 提出了一个融合边缘检测模型 (RED),将卷积神经网络 (CNN) 和粗略集结合起来.
  • 使用Faster R-CNN进行初始图像细分和K-means集群以减少噪音.
  • 应用了粗略的集合理论,以改进不同照明和遮蔽条件下的边缘检测.

主要成果:

  • 红色模型在检测果果边缘方面表现出高精度和稳定性.
  • 与传统运营商相比,显著提高了检测准确性和稳定性.
  • 在诸如复杂的背景和不同的照明等具有挑战性的条件下,性能尤其显著.

结论:

  • 红色模型为智能水果采摘和产量预测提供了一个有前途的解决方案.
  • 融合方法有效地解决了水果检测中的噪音和环境挑战.
  • 这项研究为推进自动化农业实践提供了基础.