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関連する概念動画

Plastic Deformations01:19

Plastic Deformations

477
Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
477
Plastic Deformations01:14

Plastic Deformations

477
It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
477
Bacterial Transformation01:33

Bacterial Transformation

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In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
60.2K
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

415
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
415
Deformations in a Symmetric Member in Bending01:18

Deformations in a Symmetric Member in Bending

528
When analyzing the deformation of a symmetric prismatic member subjected to bending by equal and opposite couples, it becomes clear that as the member bends, the originally straight lines on its wider faces curve into circular arcs, with a constant radius centered at a point known as Point C. This phenomenon helps to understand the stress and strain distribution within the member more clearly.
When the member is segmented into tiny cubic elements, it is observed that the primary stress...
528
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

491
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
491

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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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半監視運転者の注意をそらす検出のための変形可能なピラミッド・スパース・トランスフォーマー

Qiang Zhao1, Zhichao Yu2, Jiahui Yu2

  • 1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
まとめ
この要約は機械生成です。

この研究では,ドライバーの注意散漫を検知するための適応的な半監督のフレームワークを導入し,正確なパフォーマンスを確保するために,限られたラベル付きデータとラベルのないサンプルを使用して,安全システムを大幅に改善します.

キーワード:
YOLOベースの検出形状が変形する特徴は,核融合です.ドライバーの注意をそらすこと 検知 ドライバーの注意をそらすことドライバーモニタリングシステムインテリジェント・トランスポートシステムマルチスケール機能アラインメントオブジェクト検出 オブジェクト検出半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,半監督学習 (semi-supervised learning) とは,教師・生徒のフレームワーク

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科学分野:

  • インテリジェント・輸送システム
  • コンピュータビジョン コンピュータビジョン
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.

背景:

  • 運転者の注意は,インテリジェントな交通安全にとって極めて重要です.
  • ドライバーの注意散漫を検知するモデルの手動のアノテーションは,費用がかかり,時間がかかります.
  • 既存のモデルは,ラベル付きのデータが限られているのに苦労しています.

研究 の 目的:

  • 適応性のある半監督運転手分散検知フレームワークを提案する.
  • 限定されたラベルデータでモデルのパフォーマンスを改善します.
  • 現実世界のドライバー監視システムを強化します.

主な方法:

  • 教師と生徒の学習と,変形ピラミッドの機能融合を利用しています.
  • カテゴリ意識の値と信頼の重み付けによる適応的な擬似ラベル最適化戦略を組み込む.
  • 変形可能なピラミッド・スパース・トランスフォーマー (DPST) モジュールをYOLOv11検出器に統合します.
  • 教師指導による特徴一貫性蒸留を採用しています.

主要な成果:

  • 制限されたラベル付きデータと豊富なラベルのないデータを用いて,堅牢でスケーラブルな分散検出を実現します.
  • DPSTモジュールは,微妙なドライバーの行動の微細な認識を強化します.
  • mAPメトリックにおけるRoboflow Distracted Drivingデータセットにおける完全に監督されたベースラインを上回る.
  • 精度とリコールとのバランスの取れたトレードオフを示しています.

結論:

  • 提案されたフレームワークは,限られた注釈条件下でのドライバーの注意をそらす検出のための効果的なソリューションを提供します.
  • 実践的でスケーラブルなドライバーモニタリングシステムを可能にします.
  • 特徴一致性蒸留による騒々しい擬似ラベルの影響を軽減します.