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関連する概念動画

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

467
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...
467
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

723
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...
723
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

261
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
261
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

666
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
666
Machines: Problem Solving II01:30

Machines: Problem Solving II

622
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.
622
Machines: Problem Solving I01:22

Machines: Problem Solving I

659
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...
659

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関連する実験動画

Updated: Jan 8, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

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大規模言語モデルに基づく再構成可能生産ラインの段取り替えタスク計画フレームワーク

Jinliang Hou1, Li Yan2, Hua Qiang3

  • 1Xi'an Technological University, Xi'an, China. houjinliang@st.xatu.edu.cn.

Scientific reports
|December 17, 2025
PubMed
まとめ

本研究では、大規模言語モデル（LLM）を用いた再構成可能生産ラインの自動段取り替えタスク計画フレームワークを紹介します。LLMベースのシステムは、生産ラインの段取り替えにおける効率を高め、エラーを削減します。

キーワード:
大規模言語モデル生産ライン段取り替えタスク計画ロボット工学自動化製造システム

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科学分野:

  • ロボット工学と自動化
  • 人工知能
  • 製造システム工学

背景:

  • 再構成可能生産ラインにおける手動での段取り替えは、非効率性と高いエラー率に悩まされています。
  • これらの操作の自動化は、製造の柔軟性と生産性を向上させるために不可欠です。

研究 の 目的:

  • 再構成可能生産ラインの自動段取り替えタスク計画フレームワークを提案すること。
  • 大規模言語モデル（LLM）を活用して、ユーザーの指示を解釈し、ロボットを制御すること。

主な方法:

  • 知識グラフ、高レベルタスクプランナー、低レベルタスクプランナーを統合したフレームワーク。
  • 意味論的な指示のLLMベースの解釈により、タスクを分解し、スキルシーケンスを生成します。
  • 4層制御アーキテクチャによるスキルベースのモジュラープログラミング。

主要な成果:

  • 単一ロボットおよび実際の生産ライン実験を通じて、フレームワークの正当性を実証しました。
  • 製造された段取り替えロボットを制御し、自動段取り替えタスクを実行させることに成功しました。
  • ユーザーの指示を解釈し、自動段取り替えを実行するフレームワークの能力を検証しました。

結論:

  • 提案されたLLMベースのフレームワークは、手動での段取り替え操作における課題を効果的に解決します。
  • 再構成可能生産ラインにおける自動段取り替えタスクを可能にし、効率と精度を向上させます。
  • よりインテリジェントで自律的な製造システムへの道を開きます。