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関連する概念動画

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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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Flame Photometry: Overview01:02

Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
801
Flame Photometry: Lab01:16

Flame Photometry: Lab

360
In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
360
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Reducing Line Loss01:18

Reducing Line Loss

193
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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関連する実験動画

Updated: Sep 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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YOLO11-RLN:森林火災検出のための空中UAVアルゴリズム

Li Gao1, Gaohua Chen1

  • 1School of Electronic Information Engineering, Taiyuan University of Science and Technology, Shanxi Taiyuan, China.

Annals of the New York Academy of Sciences
|August 27, 2025
PubMed
まとめ

この研究は,ドローンの森林火災検出アルゴリズムであるYOLO11-RLNを導入し,複雑な環境での誤報を大幅に高めています.

科学分野:

  • コンピュータ・ビジョン
  • 人工知能
  • 環境監視

背景:

  • 現存する森林火災検知モデルは ドローンの適応性や正確性や 誤った検知率に問題があります
  • 無人航空機 (UAV) による検出には,複雑な森林地形のための強力なアルゴリズムが必要です.

研究 の 目的:

  • 現在の限界を克服するUAV指向の森林火災検出アルゴリズムを開発する.
  • 検出の精度を高め 偽陽性を減らし 森林火災のモニタリングにドローンの適応性を向上させる

主な方法:

  • 特徴抽出のためのRepVGGバックボーンを統合したYOLO11-RLNアルゴリズム.
  • 消防機能の感知を改善するために,新しい長火線テクスチャー融合 (LTF) モジュールが導入されました.
  • WIoU損失関数とYOLOv8-ナノパラメータ化が実装され,小型火災検出とモデルの最適化が強化されています.

主要な成果:

  • YOLO11- RLNはYOLO11と比較して有意な改善を示した.
  • 精度7.338%,リコール5.392%,mAP50で7.862%,mAP50〜75で7.019%の改善を達成しました.
  • 統計分析により,性能改善の強度と重要性が確認されました.
キーワード:
LTF についてRepVGG についてYOLO11 について森林火災の検出損失関数ナノ

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Last Updated: Sep 10, 2025

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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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結論:

  • 提案されたYOLO11-RLNアルゴリズムは,UAVベースの森林火災検出に優れた性能を提供します.
  • RepVGG,LTFモジュール,WIoU損失,ナノ最適化の統合は,挑戦的な環境での検出能力を向上させます.