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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Machines01:19

Machines

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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.
A free-body diagram of the...
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Microorganisms in Agriculture and Food industry01:27

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Microorganisms play a crucial role in agriculture and the food industry, contributing to soil fertility, crop protection, and food production. Their functions range from nitrogen fixation and biopesticide production to fermentation and food preservation, making them indispensable to sustainable farming and food safety.Role in AgricultureNitrogen-fixing bacteria, such as Rhizobium (symbiotic) and Azotobacter (free-living), convert atmospheric nitrogen into ammonia through biological nitrogen...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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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Machines: Problem Solving I01:22

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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.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Updated: Jan 29, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Modelo optimizado de aprendizaje automático de conjuntos para la clasificación de ciberataques en IoT industrial

Batool Alabdullah1, Suresh Sankaranarayanan1

  • 1Department of Computer Science, King Faisal University, Al-Ahsa, Saudi Arabia.

Frontiers in artificial intelligence
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta modelos optimizados de conjuntos apilados para detectar amenazas cibernéticas en entornos de sistemas de control industrial (ICS) y de Internet de las cosas (IoT), logrando alta precisión y eficiencia.

Palabras clave:
ciberataqueaprendizaje de conjuntossistemas de control industrialinternet industrial de las cosasinternet de las cosasaprendizaje automáticocomportamiento maliciosopetróleo y gas

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Área de la Ciencia:

  • Ciberseguridad
  • Aprendizaje Automático
  • Sistemas de Control Industrial (ICS)
  • Internet de las Cosas (IoT)

Sus antecedentes:

  • Los sistemas de control industrial (ICS) y los dispositivos IoT se enfrentan a crecientes ciberamenazas, especialmente en sectores críticos como el petróleo y el gas.
  • Los métodos existentes de aprendizaje automático (ML) para la detección de ciberataques a menudo carecen de eficiencia computacional y se basan en la clasificación binaria.

Objetivo del estudio:

  • Proponer y evaluar modelos optimizados de conjuntos apilados para mejorar la detección de ciberataques en entornos ICS y IoT.
  • Reducir la sobrecarga computacional mientras se mejora la precisión de la detección de ciberamenazas sofisticadas.

Principales métodos:

  • Se desarrollaron dos modelos optimizados de conjuntos apilados que integran diversos aprendices base (Regresión Logística, Clasificador de Árboles Extra, XGBoost, LGBM, RFC).
  • Se seleccionaron modelos para abordar los desafíos en conjuntos de datos de seguridad, como el desequilibrio de clases, el ruido y los patrones de ataque complejos.
  • Se evaluó la capacidad de los modelos para aprovechar diferentes límites de decisión y mecanismos de aprendizaje para mejorar la detección.

Principales resultados:

  • El modelo Stacked Ensemble_2 logró una precisión del 97% con un tiempo de cómputo de 54 minutos.
  • Stacked Ensemble_2 superó a Stacked Ensemble_1 y alcanzó una precisión del 100% con un AUROC del 99% en el conjunto de datos CICIDS 2017.

Conclusiones:

  • El modelo propuesto Stacked Ensemble_2 ofrece una solución escalable y en tiempo real para proteger entornos ICS y IoT.
  • Se demostraron avances significativos sobre los métodos tradicionales en precisión y eficiencia para detectar ciberamenazas en infraestructuras críticas.