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

Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

340
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
340
Reinforcement Schedules01:24

Reinforcement Schedules

115
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
115
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.5K
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...
3.5K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

466
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
466
Reinforcement01:23

Reinforcement

152
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
152
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

268
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
268

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

MAD-Net: Morphometric-Attentive Diffusion Network for Predicting Longitudinal Infant Brain Functional Connectivity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Mg-MOF-74 Derived Defective Framework for Hydrogen Storage at Above-Ambient Temperature Assisted by Pt Catalyst.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Weight loss and all-cause mortality: A propensity score matching cohort study.

Obesity research & clinical practice·2022
Same author

Trends in Nutritional Biomarkers by Demographic Characteristics Across 14 Years Among US Adults.

Frontiers in nutrition·2022
Same author

Microbial Inactivation Property of Pulsed Corona Discharge Plasma and Its Effect on Chilled Pork Preservation.

Foodborne pathogens and disease·2021
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

10.9K

通过强化学习加速大规模线性编程的预解.

Yufei Kuang, Xijun Li, Jie Wang

    IEEE transactions on pattern analysis and machine intelligence
    |April 21, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了Presolve (RL4Presolve) 的强化学习,这是一种新的框架,可以自动为线性编程 (LP) 解决者设计高效的预解程序. RL4Presolve通过学习最佳的预解决器选择和执行策略,显著提高LP解决效率.

    更多相关视频

    Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
    05:08

    Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

    Published on: January 12, 2024

    1.4K
    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
    09:12

    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

    Published on: March 17, 2019

    9.4K

    相关实验视频

    Last Updated: May 10, 2025

    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    10.9K
    Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
    05:08

    Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

    Published on: January 12, 2024

    1.4K
    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
    09:12

    Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

    Published on: March 17, 2019

    9.4K

    科学领域:

    • 运营研究 运营研究
    • 人工智能的人工智能
    • 计算机科学 计算机科学

    背景情况:

    • 预解决程序对于现代线性编程 (LP) 解决器的效率至关重要,它利用各种预解决器来消除冗余.
    • 设计有效的预解决程序是具有挑战性的,因为广的搜索空间和需要广泛的领域专业知识和手动调整.
    • 预解决者的选择,订单和停止标准显著影响LP解决性能.

    研究的目的:

    • 开发一个自动化框架,用于设计LP解决器中的高质量预解决程序.
    • 解决手动调整和优化预解决策略所需的领域专业知识所面临的挑战.
    • 利用机器学习,特别是强化学习,提高LP解决器的效率.

    主要方法:

    • 介绍了Presolve的强化学习 (RL4Presolve),这是一个基于学习的框架,用于自动化预解决程序设计.
    • 实施一种新的适应性动作序列,使复杂的预解决器组合能够有效地学习.
    • 与传统方法相比,进行了广泛的实验,以评估学习过的预解决程序的性能.

    主要成果:

    • RL4Presolve在LP解决效率方面取得了显著的改进,其收益高达大约90%.
    • 学习的预解决程序在复杂的现实应用中表现出有效性,例如华为的供应链.
    • 从已学习的策略中提取的程序提供了简单,高效的部署,而不需要GPU资源.

    结论:

    • RL4Presolve代表了第一个基于学习的方法,为LP解决者自动设计有效的预解决程序.
    • 该框架显著提高了LP解决效率,并减少了手动调整和领域专业知识的需求.
    • 学习的预解决程序是实用的,可以在工业环境中有效地部署,提供了实质性的经济价值.