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Instantaneous Power

Instantaneous Power

Instantaneous power is important in electrical circuits, mainly when dealing with sinusoidal input. Instantaneous power, denoted as p(t), results from the multiplication of the instantaneous voltage (v(t)) across an element and the instantaneous current (i(t)) flowing through it. This relationship adheres to the passive sign convention and represents a fundamental principle in electrical engineering.

Maximum Power Transfer

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...

Power

Power

The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...

Average Power

Average Power

In practical electrical applications, the concept of time-varying instantaneous power is not frequently utilized. Instead, focus shifts to the more practical quantity known as average power. Average power is determined by integrating the instantaneous power over a specified time period and subsequently dividing it by that duration.

Power in an AC Circuit

Power in an AC Circuit

In a DC circuit, the power consumed is simply the product of the DC voltage times the DC current, given in watts. However, the power consumed for AC circuits with reactive components is calculated differently. Since electrical power is the "rate" at which energy is used in a circuit, all electrical and electronic components and devices have a safe operating range for electrical power.
In a DC circuit, there is no sinusoidal waveform associated with the supply; the voltages and currents are...

Power Factor

Power Factor

The power factor is defined as the ratio of average (or active) power to apparent power, as illustrated by the relation

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Related Experiment Video

Updated: May 13, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

First experiments with POWERPLAY.

Rupesh Kumar Srivastava¹, Bas R Steunebrink, Jürgen Schmidhuber

¹The Swiss AI Lab IDSIA, Galleria 2, 6928 Manno-Lugano, University of Lugano & SUPSI, Switzerland.

Neural Networks : the Official Journal of the International Neural Network Society

|March 8, 2013

Summary

This summary is machine-generated.

POWERPLAY, a novel AI system, learns new skills and invents new problems. This artificial intelligence continually improves its problem-solving abilities and creates increasingly complex, self-generated tasks.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Current AI systems often focus on solving predefined tasks.
The development of AI that can autonomously generate and solve novel problems is a key research area.
Understanding emergent behaviors in complex AI systems is crucial for advancing artificial general intelligence.

Purpose of the Study:

To describe initial experiments with the POWERPLAY system.
To investigate the capabilities of a POWERPLAY-driven SLIM RNN in self-directed learning and problem invention.
To identify developmental stages and self-organization in an open-ended AI system.

Main Methods:

Utilized POWERPLAY, an AI system designed for skill acquisition and problem generation.
Employed a self-delimiting recurrent neural network (SLIM RNN) as the core computational architecture.
Analyzed the system's ability to encode programs, interact with its environment, and abstract event sequences.

Main Results:

The POWERPLAY-driven SLIM RNN demonstrated increasing generality in solving self-invented problems.
The system exhibited a growing repertoire of problem-solving procedures.
Emerging developmental stages and automatic self-modularization were observed, with frequent code reuse for novel tasks.

Conclusions:

POWERPLAY systems can autonomously learn, invent problems, and develop sophisticated problem-solving skills.
The SLIM RNN architecture supports open-ended learning and the emergence of complex AI behaviors.
This research contributes to understanding AI development and self-organization in artificial systems.