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相关概念视频

Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Cognitive Learning01:21

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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.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Machines01:19

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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.
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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Updated: May 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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一个基于自适应和机器学习的安全和高效的加密系统,用于在雾计算中保护数据.

Priyanka Rajan Kumar1, Sonia Goel2

  • 1Department of Computer Science, Punjabi University, Patiala, 147001, India. srpriyankass@gmail.com.

Scientific reports
|April 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于雾计算的自适应加密框架,使用机器学习来动态调整安全级别,以加强数据保护和在边缘环境中高效处理.

关键词:
在AES算法中,AES算法适应性加密是适应性的加密.在 ECC 算法中, ECC 算法雾计算的计算方法机器学习是机器学习.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 边缘计算 边缘计算

背景情况:

  • 雾计算是边缘计算的组成部分,由于其分布式和异质性,它面临着独特的安全挑战.
  • 现有的安全措施可能无法在动态雾环境中充分平衡性能和保护.

研究的目的:

  • 为雾计算环境开发一种新的自适应加密框架.
  • 通过动态调整基于数据敏感性和上下文的加密策略来提高数据安全性和处理效率.

主要方法:

  • 实现了一个基于机器学习的系统,使用K-Nearest Neighbors (KNN) 来进行数据分类 (敏感与正常).
  • 采用混合加密方法 (ECC + AES) 处理敏感数据和标准AES处理正常数据.
  • 进行了全面的实验,评估了加密/解密时间,吞吐量,直方图分析,NPCR,UACI以及不同数据集大小的可扩展性.

主要成果:

  • 适应性框架展示了高NPCR (99.349%) 和UACI (33.079%) 的有效安全增强.
  • 评估证实了不同数据类型 (文本,图像) 和大小 (1kB至1000kB) 的一致性能和适应性.
  • 该系统在强大的安全性和高效的数据处理之间取得了平衡.

结论:

  • 拟议的自适应加密框架显著提高了雾计算中的安全性.
  • 该系统的动态调整能力确保在大型边缘环境中高效安全的数据传输.