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関連する概念動画

Flame Photometry: Overview01:02

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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...
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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.
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Updated: Sep 10, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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ハイブリッド・ゴールデン・シャカールとモット・フラーム・オプティマイゼーション・アルゴリズムに基づく,異質なUAVネットワークにおけるカバーパスの計画

K Karthik1, C Balasubramanian2, R Praveen3

  • 1Department of Electronics and Communication Engineering, P.S.R.R. College of Engineering, Sivakasi, Tamil Nadu, 626140, India. karthikkesavan@psrr.edu.in.

Scientific reports
|August 23, 2025
PubMed
まとめ

この研究は,無人航空機 (UAV) のカバーパスの計画のためのハイブリッド最適化アルゴリズムを導入します. この新しいアプローチは効率を高め,タスクの完了と実行時間を短縮し,地域を包括的にカバーします.

キーワード:
覆面経路の計画ゴールデン・シャカルの最適化アルゴリズムモット・フラーム・最適化アルゴリズム興味のある地域無人航空機 (UAV)

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科学分野:

  • ロボット
  • 人工知能
  • 運用研究

背景:

  • カバレッジ・パス・プランニング (CPP) は,無人航空機 (UAV) がターゲット地域を効率的に調査するために不可欠です.
  • 異なる能力を持つ異質なUAVの飛行経路を最適化することは大きな課題です.
  • エネルギー消費とタスクの完了時間を最小限に抑えることは,UAVベースのカバーミッションの重要な目標です.

研究 の 目的:

  • 異質なUAVを使用した効果的なCoverage Path Planning (CPP) のための新しいハイブリッド最適化アルゴリズムを提案する.
  • 指定されたターゲット地域を包括的にカバーするUAVの効率と有効性を高める.
  • エネルギー消費を削減し,飛行性能を向上させるための飛行経路の最適化.

主な方法:

  • CPPのためのハイブリッドのゴールデン・ジャカールとモット・フラーム・オプティマイゼーション・アルゴリズム (HGJMFOA) の開発
  • 各UAVの最適な点から点への飛行経路を特定するための線形プログラミングモデルの統合.
  • HGJMFOAを使用して可能な経路を探索し,飛行時間を最小限に抑える.

主要な成果:

  • HGJMFOAのアプローチは,パフォーマンスメトリックの有意な改善を示しました.
  • 作業の完了時間を 21.34% 短縮しました
  • 24.56%の偏差率と34.19%の実行時間を削減しました.

結論:

  • 提案されたHGJMFOA戦略は,異質なUAVのカバーパスの計画を効果的に最適化します.
  • 多様なUAV間の協力により,最適なカバー,センサー範囲の利用,優れた飛行性能を達成しました.
  • シミュレーションの結果は,時間を最小限に抑え,全体のミッション効率を改善するアルゴリズムの有効性を検証します.