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Related Concept Videos

Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Pole and System Stability01:24

Pole and System Stability

The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's response.
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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.
Control Systems01:10

Control Systems

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...
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:

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Related Experiment Videos

Stability-Controlled Continual Federated Learning for Energy-Harvesting AIoT Systems.

Junsoo Park1, Ikjune Yoon2, Dong Kun Noh3

  • 1School of Biomedical Systems, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for energy-harvesting AIoT systems. It ensures stable continual federated learning by managing energy and preventing data forgetting, even with fluctuating power.

Keywords:
Lyapunov stabilitycontinual learningenergy-aware controlenergy-harvesting AIoTfederated learning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence of Things (AIoT)
  • Machine Learning
  • Energy Harvesting

Background:

  • Energy-harvesting (EH) AIoT systems face challenges with unstable energy availability, hindering stable learning.
  • Federated learning (FL) in these systems risks energy depletion (blackout), while continual learning struggles with data evolution and catastrophic forgetting.

Purpose of the Study:

  • To develop a stability-controlled continual federated learning framework for EH-AIoT systems.
  • To address the trade-off between energy stability and catastrophic forgetting in autonomous learning systems.

Main Methods:

  • A novel framework jointly regulates local training intensity and rehearsal usage based on residual energy.
  • The method employs a Lyapunov drift-plus-penalty formulation and a lightweight mode-based control policy.

Main Results:

  • The proposed method significantly reduces blackouts in simulations using real solar energy traces.
  • It improves learning accuracy and mitigates catastrophic forgetting compared to existing methods.

Conclusions:

  • Energy-aware joint control is effective for stable continual federated learning in EH-AIoT systems.
  • The framework enables robust and autonomous long-term operation of AIoT devices.