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相关概念视频

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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State Space Representation01:27

State Space Representation

232
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...
232
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

12.9K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.2K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.2K
Random Variables01:09

Random Variables

12.3K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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相关实验视频

Updated: Jul 16, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

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使用特征空间随机性的对抗性攻击和防御.

Jumpei Ukita1, Kenichi Ohki2

  • 1Department of Physiology, The University of Tokyo School of Medicine, 7-3-1, Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan.

Neural networks : the official journal of the International Neural Network Society
|September 18, 2023
PubMed
概括
此摘要是机器生成的。

在深层神经网络中注入随机噪音有助于防御某些攻击. 然而,新的特征空间对抗示例需要在隐藏层中注入噪音,以有效地防御不受限制的对抗攻击.

关键词:
敌对的攻击是敌对的攻击.敌对辩护是对抗性的辩护.功能的光滑使其光滑.

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

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Cross-Modal Multivariate Pattern Analysis
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Cross-Modal Multivariate Pattern Analysis

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相关实验视频

Last Updated: Jul 16, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

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Published on: March 1, 2022

2.6K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度神经网络 深度神经网络

背景情况:

  • 深度神经网络 (DNN) 容易受到对抗性干扰的影响.
  • 输入层噪声注入是对Lp-norm-bounded对抗例的已知的防御.
  • 这种防御在不受限制的对抗性示例上是不够的,这些示例在输入层中没有界限.

研究的目的:

  • 引入一类新的不受限制的对抗性示例:特征空间对抗性示例.
  • 针对这些新实例,研究DNN不同层噪声注入的有效性.

主要方法:

  • 生成特征空间对抗示例,其特点是输入空间距离,但隐藏层接近.
  • 对输入层与隐藏层的噪声注入的实证评估.

主要成果:

  • 输入层噪声注入无法抵御特征空间对立的例子.
  • 隐藏层噪声注入成功地抵御了特征空间对立的例子.
  • 高层网络层中的随机性提供了一个新的防御机制.

结论:

  • 特性空间对抗的例子构成了传统输入层防御无法缓解的威胁.
  • 在隐藏层中注入噪音是一种有希望的策略,用于保护DNN免受更广泛的对抗性攻击.
  • 这些发现强调了在更深层网络层探索随机性的重要性,以提高对抗性强度.