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Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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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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Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Related Experiment Videos

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

Juan Pardo1, Francisco Zamora-Martínez2, Paloma Botella-Rocamora3

  • 1ESAI-Embedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/San Bartolomé, 46115 Valencia, Spain. juan.pardo@uch.ceu.es.

Sensors (Basel, Switzerland)
|April 24, 2015
PubMed
Summary
This summary is machine-generated.

This study implements an Artificial Neural Network (ANN) on a low-cost microcontroller for real-time indoor temperature forecasting. The system enables efficient Heating, Ventilating, and Air Conditioning (HVAC) use in smart homes without historical data.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Indoor temperature forecasting is crucial for optimizing Heating, Ventilating, and Air Conditioning (HVAC) systems and enhancing energy efficiency in smart homes.
  • Existing methods often require extensive historical data and significant computational resources, posing challenges for low-cost embedded systems.

Purpose of the Study:

  • To implement an Artificial Neural Network (ANN) algorithm on a low-cost microcontroller (8051MCU) for autonomous wireless sensor network-based indoor temperature forecasting.
  • To develop an on-line learning approach using the Back-Propagation (BP) algorithm for real-time time series learning without relying on large historical databases.
  • To address the challenge of deploying computationally demanding algorithms on resource-constrained hardware.

Main Methods:

  • Development of a Wireless Sensor Network (WSN) using 8051MCU technology for indoor temperature monitoring.
  • Implementation of an on-line learning Back-Propagation (BP) algorithm for Artificial Neural Networks (ANNs) enabling real-time model training.
  • Validation through a simulation study comparing the developed model against a Bayesian baseline model using real-world data.

Main Results:

  • Successful implementation of a real-time indoor temperature forecasting system on a low-cost, resource-constrained microcontroller.
  • Demonstration of an on-line learning approach that trains the ANN model with incoming data, eliminating the need for extensive historical data storage.
  • Validation of the algorithm's performance and accuracy through comparative simulation studies.

Conclusions:

  • The developed ANN-based WSN provides an effective solution for real-time indoor temperature forecasting in smart homes.
  • The on-line learning approach is suitable for resource-limited environments, enabling efficient energy management through optimized HVAC utilization.
  • This research highlights the feasibility of deploying advanced algorithms on low-cost microcontrollers for intelligent building applications.