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相关概念视频

Control Systems01:10

Control Systems

1.4K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Open and closed-loop control systems01:17

Open and closed-loop control systems

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1000
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Classification of Systems-I01:26

Classification of Systems-I

312
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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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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在工业控制系统中通过灰狼优化器和自动编码器集成的优化异常检测框架.

Muhammad Muzamil Aslam1, Liyanage Chandratilak De Silva2, Rosyzie Anna Awg Haji Mohd Apong1

  • 1School of Digital Science, Universiti Brunei Darussalam, Gadong A, Bandar Seri Begawan, BE1410, Brunei Darussalam.

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|July 30, 2025
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概括

本研究引入了一种新的方法,通过将灰狼优化器 (GWO) 和自动编码器 (AE) 结合起来,来检测工业控制系统 (ICS) 中的异常. 优化的框架显著提高了检测准确性,并减少了错误.

关键词:
异常检测检测异常检测合作方式 (GWO+AE)工业控制系统 工业控制系统这里是SWAT.安全的安全的安全的安全的安全.在WADI数据集中,WADI数据集包含了许多数据.

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 工业控制系统 工业控制系统

背景情况:

  • 可靠的互联网连接对于工业控制系统 (ICS) 的实时监控和异常检测至关重要.
  • 目前ICS中的异常检测方法面临着诸如高计算复杂性,数据集限制和高假阳性率等挑战.

研究的目的:

  • 开发一种新的协作数据处理框架,用于加强ICS中的异常检测.
  • 将灰狼优化器 (GWO) 与自动编码器 (AE) 集成和优化,以提高性能.

主要方法:

  • 拟议的方法通过增强猎物选择,包围和初始种群生成来优化GWO.
  • 自动编码器 (AE) 丢失功能得到了改进,以实现更好的模型通用化.
  • 该框架采用两阶段的过程:GWO用于特征选择,AE用于异常检测.

主要成果:

  • 在SWaT和WADI数据集上的实验验证显示,与现有方法相比,性能优越.
  • 在准确性,精度,回忆和F1得分方面观察到显著的改善.
  • 该模型有效地识别了相关特征,并减少了特征错误.

结论:

  • 拟议的GWO-AE框架在解决当前ICS异常检测系统的局限性方面具有重大潜力.
  • 该方法为ICS环境中的实时监控和异常检测提供了更准确,更可靠的解决方案.