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Related Concept Videos

Machines01:19

Machines

345
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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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
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Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Cross-Layer Analysis of Machine Learning Models for Secure and Energy-Efficient IoT Networks.

Rashid Mustafa1, Nurul I Sarkar1, Mahsa Mohaghegh1

  • 1Department of Computer and Information Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cross-layer Internet of Things (IoT) architecture using machine learning (ML) and lightweight cryptography. The system enhances security by up to 95% and reduces power consumption by 30% for IoT devices.

Keywords:
cross-layer analysisenergy-efficient networkmachine learning modelsecure IoTsimulation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • Widespread Internet of Things (IoT) adoption presents significant security and energy efficiency challenges, especially for resource-constrained devices.
  • Existing IoT security solutions often struggle to balance robust protection with minimal power consumption.

Purpose of the Study:

  • To propose a novel cross-layer IoT architecture that integrates machine learning (ML) models and lightweight cryptography.
  • To enhance security, improve authentication, and optimize energy efficiency in large-scale IoT deployments.

Main Methods:

  • Implementation of a cross-layer IoT architecture featuring role-based access control (RBAC) with energy-aware policies.
  • Integration of layer-specific ML models (LSTM for anomaly detection, decision trees for validation) and adaptive Speck encryption.
  • Leveraging convolutional neural networks (CNNs) for enhanced IoT security and energy efficiency.

Main Results:

  • Reduced false positives by up to 32%.
  • Prevented unauthorized access attempts with up to 95% effectiveness.
  • Achieved a 30% reduction in power consumption using lightweight Speck encryption compared to AES.

Conclusions:

  • The proposed cross-layer IoT architecture effectively harmonizes ML-driven security with energy-efficient operations.
  • The system demonstrates significant improvements in security and power consumption for practical IoT applications.
  • This approach offers a viable solution for securing smart cities, homes, and schools.