Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

271
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
271
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

530
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
530
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

422
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
422
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

429
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
429
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

393
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
393
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

448
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
448

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Numerical Simulation and Theoretical Analysis of Flexural Strengthening of Undamaged RC Beams with Steel Strand Mesh-Reinforced ECC.

Materials (Basel, Switzerland)·2026
Same author

Invited review: Manufacturing Whey Protein Colloidal Particles via Liquid Antisolvent Precipitation Method: Particle Formation Mechanism and Ingredient Functionality Aspects.

Journal of dairy science·2026
Same author

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

For Intermediate-Size Sessile Serrated Lesions, Durable Clearance Should Remain the Decisive Endpoint.

United European gastroenterology journal·2026
Same author

Therapeutic potential of wogonoside in hypertension-induced cardiac injury: Targeting apoptosis and MAPK signaling pathway.

The Journal of nutritional biochemistry·2026
Same author

Interleukins in community-acquired pneumonia: from biomarkers to precision medicine.

Frontiers in immunology·2026

関連する実験動画

Updated: Sep 9, 2025

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.6K

自律運転のための弱くて自己監視のクラス無意識の運動予測

Ruibo Li, Hanyu Shi, Zhe Wang

    IEEE transactions on pattern analysis and machine intelligence
    |August 28, 2025
    PubMed
    まとめ

    この研究では,LiDARデータを用いてクラス無関係な運動予測のための新しい弱点で自己監視された方法が導入されています. これらのアプローチは,自動運転の競争力のある性能を達成しながら,注釈の必要性を大幅に削減します.

    科学分野:

    • コンピュータ・ビジョン
    • ロボット
    • 機械学習

    背景:

    • 自動運転は 動的な環境で正確な動きを予測する必要があります
    • LiDARの点雲からのクラス別運動予測は重要な研究分野です.
    • 現在の方法は,しばしば広範囲な運動アノテーションに依存しています.

    研究 の 目的:

    • LiDARを用いた弱体で自己監視の クラス無意識の運動予測を調査する.
    • シーンの構造を活用して詳細なアノテーションへの依存を減らす.
    • アノテーションの努力と予測のパフォーマンスのバランスをとる強力な方法を開発する.

    主な方法:

    • 動き予測のための前景/背景マスクを使用する弱点的に監督されたパラダイムを提案した.
    • アノテーションが少ない代替手段として,非グラウンド/グラウンドマスクを使用した.
    • アノテーションを必要としない自己監督方法を開発しました.
    • 偏差値抑制のために,強固な一貫性認識のチャンファー距離損失を導入した.

    主要な成果:

    • 弱くて自己管理されたモデルは既存の自己管理された方法を上回った.
    • 監督が弱かったモデルは,監督されたいくつかの方法と比較できるパフォーマンスを達成しました.

    さらに関連する動画

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.1K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    関連する実験動画

    Last Updated: Sep 9, 2025

    Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
    09:24

    Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

    Published on: May 17, 2024

    1.6K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.1K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K
  • アノテーションの努力と予測のパフォーマンスとの間に有効なバランスを示した.
  • 結論:

    • シーンの解析シグナル (フログラウンド/バックグラウンド,非グラウンド/グラウンド) を活用することで,効率的な弱点および自己監視の動作予測が可能になります.
    • 運動予測モデルの実用性を大幅に改善します.
    • 提案された方法は,効率的な自動運転の知覚のための有望な方向性を提供します.