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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

508
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
508
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

964
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
964
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

740
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
740
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.3K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.3K
Observational Learning01:12

Observational Learning

1.1K
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...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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相关实验视频

Updated: Feb 25, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.9K

演变的马尔科夫链:在线模式发现和识别数据流的数据流.

Kutalmls Coskun, Borahan Tumer, Bjarne C Hiller

    IEEE transactions on pattern analysis and machine intelligence
    |February 23, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了进化马尔科夫链 (EMC),这是一种高效的在线方法,可以模拟随时间变化的真实世界过程. 电磁共振器在没有事先知识的情况下,可以自适应地发现模式和跟踪过渡,从而更好地理解动态系统.

    相关实验视频

    Last Updated: Feb 25, 2026

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.9K

    科学领域:

    • 机器学习 机器学习
    • 时间序列分析时间序列分析
    • 随机过程 随机过程

    背景情况:

    • 传统的马尔科夫链假定静态数据具有固定的过渡概率.
    • 现实世界的过程经常表现出动态的行为变化,在未知的模式之间切换.
    • 现有的方法很难适应这些不可预测的,时间变化的过渡.

    研究的目的:

    • 提出一种在线和高效的方法来构建进化的马尔科夫链 (EMC).
    • 为了使过渡概率的自适应跟踪和动态过程中模式的自动发现.
    • 实时检测实时的模式切换器,用于实时,现实世界的应用程序.

    主要方法:

    • 为EMC开发了一个适应性更新方案,有效地跟踪过渡概率.
    • 实现了一种自动发现未知模式和在线检测模式开关的方法.
    • 电磁中心的设计是任意排序的,不依赖于固定的跟踪窗口.

    主要成果:

    • 拟议的EMC方法证明了预期估计的几何趋同.
    • 对合成数据的评估证实了该方法的效率和适应性.
    • 在人类活动识别,运动状况监测和基于EEG的眼睛状态识别方面展示了成功的现实应用.

    结论:

    • 演变的马尔科夫链 (EMC) 提供了一种多功能和高效的方法来建模动态的现实世界过程.
    • 电磁中心的在线性质允许实时跟踪,建模和理解行为变化.
    • 电磁中心在需要适应性时间过程分析的各种应用中具有重大潜力.