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関連する概念動画

State Space Representation01:27

State Space Representation

593
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...
593
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

221
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
221
Control Volume and System Representations01:16

Control Volume and System Representations

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
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Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

553
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
553
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

994
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
994
Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
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ニューラルシーンの表現とレンダリング

S M Ali Eslami1, Danilo Jimenez Rezende2, Frederic Besse2

  • 1DeepMind, 5 New Street Square, London EC4A 3TW, UK. aeslami@google.com.

Science (New York, N.Y.)
|June 16, 2018
PubMed
まとめ
この要約は機械生成です。

機械はGenerative Query Network (GQN) を使って 人間のラベルなしで シーンの表現を学習できます このAIフレームワークは 機械が自体のセンサーデータから 学習することで 自律的に環境を理解できるようにします

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Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans
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関連する実験動画

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科学分野:

  • 人工知能
  • コンピュータ・ビジョン
  • 機械学習

背景:

  • シーンの表現はインテリジェントなシステムにとって極めて重要です.
  • ニューラルネットワークは有効ですが,通常,ラベル付きの大きなデータセットが必要です.
  • 人工知能における重要な課題は 人工知能によるラベリングへの依存を減らすことです

研究 の 目的:

  • 無監督のシーンの表現学習のための新しい枠組みを導入します.
  • 機械が自分のセンサーデータだけで シーンの表現を学習できるようにする.
  • 人工知能が人間のラベルや事前の領域知識なしに環境を理解するための方法を開発する.

主な方法:

  • 生成クエリネットワーク (GQN) を開発した.
  • GQNは複数の視点から画像を処理し,内部シーンの表現を構築します.
  • フレームワークは新しい視点から シーンの外観を予測します

主要な成果:

  • 人間のラベルなしで 成功した表現学習を証明した.
  • 観察されていない視点から 現場の外観を予測する能力を示した.
  • GQNは自律的にシーンの表現を学習します.

結論:

  • GQNは,人間の監督や領域の専門知識なしに,表現学習を容易にする.
  • このアプローチは,自律的に周囲の環境を認識し理解することを学ぶことができる AI システムの開発を進めます.
  • より有能で適応力のある 機械に道を開くのです