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State Space Representation01:27

State Space Representation

285
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
285
State Space to Transfer Function01:21

State Space to Transfer Function

302
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
302
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124
Transfer Function to State Space01:23

Transfer Function to State Space

403
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
403
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

184
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
184
Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

223
Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
223

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関連する実験動画

Updated: Sep 10, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

160

完成 マンバ:ポイントクラウドの完成のためのステートスペースモデルを制御する

Zhiheng Fu, Jiehua Zhang, Longguang Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 25, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    CompletionMambaは,ステート・スペース・モデル (SSM) を使用して,長距離依存性を捉えるため,部分的なスキャンから3Dの形状を効果的に再構築します. この新しいアプローチは,より高い精度のために形状情報を統合することによって,点雲の完成を改善します.

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    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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    Trajectory Data Analyses for Pedestrian Space-time Activity Study
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    Trajectory Data Analyses for Pedestrian Space-time Activity Study

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    関連する実験動画

    Last Updated: Sep 10, 2025

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    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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    科学分野:

    • コンピュータ・ビジョン
    • 3D 形状再構築
    • 深層学習

    背景:

    • 不完全なデータから3D形状を再構築するには,ポイントクラウドの完成が不可欠です.
    • トランスフォーマーには グローバル依存性がありますが 長いシーケンスには 計算コストがかかります
    • ステート・スペース・モデル (SSM) は,長いシーケンスに対するメモリ効率性を提供するが,因果関係要件により,無秩序な点雲との課題に直面する.

    研究 の 目的:

    • 効率的かつ正確なポイントクラウドの完成のための新しいディープラーニングネットワークを開発する.
    • 複雑な3D空間的関係と形状情報を捕捉する既存の方法の限界に対処する.

    主な方法:

    • ポイントクラウドの完成のためのステート・スペース・モデル (SSM) ベースのネットワークであるCompletionMambaを導入しました.
    • 座標を並べ替え,地域空間を定義することで,因果的に点雲を構成する方法を開発した.
    • マンバモデルに統合された形状コードで,包括的なモデリングのための形状情報の伝播を可能にします.

    主要な成果:

    • ポイントクラウド内のグローバルとローカルの両方の依存関係を効果的にキャプチャします.
    • 提案されている形状認識マンバは,完全な3D形状のモデリングを大幅に強化します.
    • ポイントクラウドの完了タスクのためのMVPとPCNのデータセットで最先端の性能を達成しました.

    結論:

    • 3Dポイントクラウドの完成には強力で効率的なソリューションを提供しています.
    • 形状認識メカニズムとSSMの統合は,この分野で重要な進歩を表しています.
    • この方法は,部分スキャンから完全な3D形を再構築する上で優れた性能を示しています.