Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
79
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

53
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,...
53
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

136
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
136
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

141
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
141

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Terahertz Rectangular Waveguides by UV-LIGA with Megasonic Agitation.

Micromachines·2022
Same author

Bayesian Factor-adjusted Sparse Regression.

Journal of econometrics·2022
Same author

Adaptive Huber Regression on Markov-dependent Data.

Stochastic processes and their applications·2022
Same author

Hoeffding's inequality for general Markov chains with its applications to statistical learning.

Journal of machine learning research : JMLR·2021
Same author

Hard thresholding regression.

Scandinavian journal of statistics, theory and applications·2020
Same author

Simultaneous dimension reduction and adjustment for confounding variation.

Proceedings of the National Academy of Sciences of the United States of America·2016
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: May 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

基于CNC机床的深度学习进行本地角落光滑.

Bai Jiang1, Rong Sun2, Ze-Long Li3

  • 1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150006, China.

Scientific reports
|January 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习算法,通过优化曲率来平滑加工工具路径. 这提高了加工质量,并允许更高的料率,提高效率.

关键词:
深度学习是一种深度学习.料率规划 料率规划 料率规划智能优化算法 智能优化算法当地角落平滑的地方

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

881
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

577

相关实验视频

Last Updated: May 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

881
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

577

科学领域:

  • 制造业 工程 制造工程
  • 计算机辅助设计 (CAD)
  • 人工智能 (AI) 是一种人工智能.

背景情况:

  • 加工工具路径通常使用线性段,限制速度和质量.
  • 优化工具路径曲率对于更顺的加工至关重要.
  • 现有的智能优化算法面临着计算资源的挑战.

研究的目的:

  • 提出一个新的策略,以优化曲水平的加工工具路径.
  • 为工具路径平滑开发一个高效的深度学习算法.
  • 通过解决当地的角落光滑性来提高加工进料率和质量.

主要方法:

  • 引入了用于曲率级工具路径优化的三个基本组件.
  • 开发了双响网局部平滑 (DRLS) 算法,结合了第一双局部平滑 (FDLS) 和第二双局部平滑 (SDLS).
  • 集成的几何,驱动状态和轮误差限制用于料率规划.

主要成果:

  • 与传统的智能算法相比,DRLS算法显著提高了优化效率.
  • FDLS和SDLS有效地优化了NURBS控制点和重量,使工具路径更顺.
  • 模拟验证了该方法在实现更高的料率和保持加工质量的有效性.

结论:

  • 拟议的DRLS算法为平滑加工工具路径提供了有效的解决方案.
  • 在局部角落优化工具路径曲率可以提高加工性能.
  • 该方法成功地平衡了更高的料率与基本的加工质量限制.