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関連する概念動画

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

502
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...
502
Interference and Diffraction02:18

Interference and Diffraction

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Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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ディフラクティブ・ディープ・ニューラル・ネットワークを用いた全光学機械学習

Xing Lin1,2,3, Yair Rivenson1,2,3, Nezih T Yardimci1,3

  • 1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA.

Science (New York, N.Y.)
|July 28, 2018
PubMed
まとめ
この要約は機械生成です。

研究者は全光学機械学習のための物理的微分深層ニューラルネットワーク (D2NN) を開発した. この3Dプリントされた光学装置は 光速で複雑な計算を行い 新しい光学アプリケーションを可能にします

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Deep Neural Networks for Image-Based Dietary Assessment
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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科学分野:

  • 光学とフォトニクス
  • 人工知能
  • 機械学習

背景:

  • ディープラーニングモデルは 複雑な推論のタスクに優れています
  • 伝統的なディープラーニングは 電子計算に依存しています
  • 光学コンピューティングは高速処理の可能性を秘めています

研究 の 目的:

  • 全光屈折深層ニューラルネットワーク (D2NN) を使用した機械学習の物理的メカニズムを導入する.
  • ディープラーニング設計に基づく様々な機能を実行するD2NNの能力を実証する.
  • 光学画像分析とコンポーネント設計の応用を探求する.

主な方法:

  • D2NNアーキテクチャをデザインした.
  • 実験的な検証のために3DプリントされたD2NN.
  • 画像分類とテラヘルツスペクトルの画像レンズの機能のためにテストされたD2NN.

主要な成果:

  • 手書きの数字とファッション製品の画像分類を成功裏に実施しました.
  • テラヘルツスペクトルのイメージングレンズとしてD2NNを実証しました.
  • 光速で複雑な機能の全光学的な実行を達成しました.

結論:

  • 完全に光学的なD2NNフレームワークは高速な機械学習のための新しいパラダイムを提供します.
  • 潜在的なアプリケーションには,全光学画像分析,特徴検出,オブジェクト分類が含まれます.
  • 独特の機能を持つ新しいカメラ設計と光学部品を可能にします.