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

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

743
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
743
Purposive Learning01:22

Purposive Learning

208
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
208
Observational Learning01:12

Observational Learning

317
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
317
Cognitive Learning01:21

Cognitive Learning

539
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
539
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.6K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.6K
Synthetic Biology02:55

Synthetic Biology

5.0K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.0K

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

An Explainable AI-Based Transfer Learning Method for Breast Cancer Prediction.

Journal of visualized experiments : JoVE·2026
Same author

BEML-sonar: a bio-inspired echolocation and machine learning-enhanced SONAR for underwater object detection and navigation.

Scientific reports·2026
Same author

An Edge-Enabled Low-Latency Cross-Lingual Speech-to-Text Framework for Efficient Human-Robot Interaction.

Big data·2026
Same author

Building novel LLM-enabled explainable ensemble transformer models combining endoscopic and CT images for discriminating the different grades of gastrointestinal cancers.

Frontiers in medicine·2026
Same author

An owl-inspired temporal transformer for enhanced shrimp detection in aquatic environments.

Scientific reports·2026
Same author

Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.

Scientific reports·2026

相关实验视频

Updated: Sep 15, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.7K

扩散驱动的代理学习策略与安全的同行互动,用于网络物理系统中的生成智能.

K M Karthick Raghunath1, T R Mahesh2, Surbhi Bhatia Khan3

  • 1Department of Computer Science and Engineering, JAIN (Deemed-to-be University).

Journal of visualized experiments : JoVE
|July 14, 2025
PubMed
概括

生成代理学习框架 (GPLF) 通过实现安全数据分析和合成数据生成,增强了网络物理系统 (CPS) 中的生成AI. 这种方法可以改善异常检测和预测建模,同时保护敏感信息.

更多相关视频

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

相关实验视频

Last Updated: Sep 15, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.7K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

科学领域:

  • 网络物理系统 (CPS)
  • 生成型的人工智能 (AI)
  • 机器学习 机器学习
  • 数据安全与隐私数据安全与隐私

背景情况:

  • 网络物理系统 (CPS) 将计算智能与物理过程进行监控和自动化.
  • 在CPS中部署生成AI是具有挑战性的,因为分布式环境,敏感数据和隐私问题.
  • 现有的联合学习 (FL) 方法在CPS中与模型多样性和隐私风险作斗争.

研究的目的:

  • 在CPS中引入生成代理学习框架 (GPLF) 以确保生成AI应用程序的安全性.
  • 通过使用先进的AI技术,解决分布式CPS环境中的隐私和安全挑战.
  • 增强CPS内部用于异常检测和预测建模的生成AI能力.

主要方法:

  • 生成式代理学习框架 (GPLF) 使用基于代理的联合学习 (ProxyFL),适用于CPS中的生成AI.
  • 每个参与者都维护了本地数据的私有模型和安全协作的共享代理模型.
  • 先进的扩散模型产生高保真合成传感器数据,保留关键功能,具有差异性隐私和加密,用于安全更新和通信.

主要成果:

  • GPLF显示隐私泄露减少了25%,数据交换能力提高了25%.
  • 在基准CPS数据集中,生成任务的准确性提高了18%.
  • 该框架可实现安全的生成过程,包括异常检测,合成数据创建和预测建模.

结论:

  • 生成式代理学习框架 (GPLF) 为网络物理系统的安全和智能操作提供了一个变革性的解决方案.
  • GPLF有效地平衡了数据分析和模型培训的需求,并提供了强大的隐私和安全保证.
  • 该框架能够生成现实的合成数据,这提高了生成AI在关键CPS应用中的实用性.