Validation of artificial neural networks to model the acoustic behaviour
of induction motors
- URL: http://arxiv.org/abs/2401.15377v1
- Date: Sat, 27 Jan 2024 10:49:33 GMT
- Title: Validation of artificial neural networks to model the acoustic behaviour
of induction motors
- Authors: F.J. Jimenez-Romero, D. Guijo-Rubio, F.R. Lara-Raya, A. Ruiz-Gonzalez,
C. Hervas-Martinez
- Abstract summary: This work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors.
Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In the last decade, the sound quality of electric induction motors is a hot
topic in the research field. Specially, due to its high number of applications,
the population is exposed to physical and psychological discomfort caused by
the noise emission. Therefore, it is necessary to minimise its psychological
impact on the population. In this way, the main goal of this work is to
evaluate the use of multitask artificial neural networks as a modelling
technique for simultaneously predicting psychoacoustic parameters of induction
motors. Several inputs are used, such as, the electrical magnitudes of the
motor power signal and the number of poles, instead of separating the noise of
the electric motor from the environmental noise. Two different kind of
artificial neural networks are proposed to evaluate the acoustic quality of
induction motors, by using the equivalent sound pressure, the loudness, the
roughness and the sharpness as outputs. Concretely, two different topologies
have been considered: simple models and more complex models. The former are
more interpretable, while the later lead to higher accuracy at the cost of
hiding the cause-effect relationship. Focusing on the simple interpretable
models, product unit neural networks achieved the best results: for MSE and for
SEP. The main benefit of this product unit model is its simplicity, since only
10 inputs variables are used, outlining the effective transfer mechanism of
multitask artificial neural networks to extract common features of multiple
tasks. Finally, a deep analysis of the acoustic quality of induction motors in
done using the best product unit neural networks.
Related papers
- Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies [0.0]
We consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network.
We adapt several noise reduction techniques to the essential setting of classification tasks.
arXiv Detail & Related papers (2024-11-07T01:21:12Z) - sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection
with Spiking Neural Networks [51.516451451719654]
Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient.
This paper introduces a novel SNN-based Voice Activity Detection model, referred to as sVAD.
It provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms.
arXiv Detail & Related papers (2024-03-09T02:55:44Z) - Single Neuromorphic Memristor closely Emulates Multiple Synaptic
Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions.
These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
arXiv Detail & Related papers (2024-02-26T15:01:54Z) - On the Trade-off Between Efficiency and Precision of Neural Abstraction [62.046646433536104]
Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models.
We employ formal inductive synthesis procedures to generate neural abstractions that result in dynamical models with these semantics.
arXiv Detail & Related papers (2023-07-28T13:22:32Z) - Spiking neural network for nonlinear regression [68.8204255655161]
Spiking neural networks carry the potential for a massive reduction in memory and energy consumption.
They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware.
A framework for regression using spiking neural networks is proposed.
arXiv Detail & Related papers (2022-10-06T13:04:45Z) - Mental arithmetic task classification with convolutional neural network
based on spectral-temporal features from EEG [0.47248250311484113]
Deep neural networks (DNN) show significant advantages in computer vision applications.
We present here a shallow neural network that uses mainly two convolutional neural network layers, with relatively few parameters and fast to learn spectral-temporal features from EEG.
Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%.
arXiv Detail & Related papers (2022-09-26T02:15:22Z) - POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with
Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations.
The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
arXiv Detail & Related papers (2022-01-19T09:26:34Z) - Information contraction in noisy binary neural networks and its
implications [11.742803725197506]
We consider noisy binary neural networks, where each neuron has a non-zero probability of producing an incorrect output.
Our key finding is a lower bound for the required number of neurons in noisy neural networks, which is first of its kind.
This paper offers new understanding of noisy information processing systems through the lens of information theory.
arXiv Detail & Related papers (2021-01-28T00:01:45Z) - Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of
legged robots [58.720142291102135]
Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus.
The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
arXiv Detail & Related papers (2021-01-24T12:44:38Z) - A superconducting nanowire spiking element for neural networks [0.0]
Key to the success of largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics.
We present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of 10 aJ.
We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold.
arXiv Detail & Related papers (2020-07-29T20:48:36Z) - A convolutional neural-network model of human cochlear mechanics and
filter tuning for real-time applications [11.086440815804226]
We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics.
The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity.
These unique CoNNear features will enable the next generation of human-like machine-hearing applications.
arXiv Detail & Related papers (2020-04-30T14:43:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.