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関連する概念動画

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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Phase Transitions02:31

Phase Transitions

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Machines01:19

Machines

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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...
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What is Energy?04:10

What is Energy?

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The universe is composed of matter in different forms, and all forms of matter contain energy.  The different forms of energy on Earth originate from the Sun — the ultimate energy source. Plants capture light energy from the Sun, and, via the process of photosynthesis, convert it into chemical energy. This stored energy from plants can be harnessed in many ways. For example, eating plant products as food provides energy for our body to function, and burning wood or coal (fossilized...
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Free Energy01:21

Free Energy

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Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
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Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

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Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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関連する実験動画

Updated: Jan 29, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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EMO-PEGASIS: WSNにおけるエネルギー遅延最適化のためのデュアルフェーズ機械学習プロトコル

Abdulla Juwaied1

  • 1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
まとめ

強化マルチオブジェクティブPEGASIS (EMO-PEGASIS) は、機械学習を使用してエネルギー効率を高め、遅延を削減することにより、ワイヤレスセンサーネットワークを改善します。このプロトコルは、ネットワーク寿命と安定性を大幅に向上させます。

キーワード:
K近傍法 (K-NN)K平均法PEGASISエネルギー効率機械学習マルチオブジェクティブ最適化伝送遅延ワイヤレスセンサーネットワーク (WSN)

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科学分野:

  • コンピュータサイエンス; 電気工学; ネットワーク工学

背景:

  • ワイヤレスセンサーネットワーク (WSN) は、エネルギー節約とデータ伝送遅延の間の重要なトレードオフに直面しています。PEGASISのような既存のプロトコルはエネルギー効率を提供しますが、高いレイテンシと不均衡な負荷分散に悩まされています。従来のWSNプロトコルにおける最適ではないクラスター形成は、全体的なネットワークパフォーマンスを制限します。

主な方法:

  • プロトコル設計と実装のために、デュアルフェーズ機械学習戦略が採用されました。K平均法クラスタリングは、ネットワークの堅牢な空間分割に使用されました。K近傍法 (K-NN) 分類は、適応的でインテリジェントなルーティングに使用されました。

結論:

  • EMO-PEGASISプロトコルは、WSNにおけるマルチオブジェクティブ最適化問題に効果的に対処します。機械学習技術の統合は、WSNパフォーマンスを大幅に向上させます。EMO-PEGASISは、エネルギーおよび遅延制約のあるWSN環境に対して信頼性の高いマルチオブジェクティブ最適化を提供します。