AIMC Topic: Normal Distribution

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2D Transformations of Energy Signals for Energy Disaggregation.

Sensors (Basel, Switzerland) Sep 22, 2022
The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. ...
Neural Networks, Computer Noise Normal Distribution
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A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors.

Sensors (Basel, Switzerland) Sep 20, 2022
For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current ...
Algorithms Support Vector Machine Bayes Theorem Magnets Normal Distribution
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Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection.

Computational intelligence and neuroscience Sep 19, 2022
Nuclear methods, such as the study of the main components of nuclear and the support of vector machines, have gradually evolved into a type of pillar methods for pattern recognition and economic statistics. Therefore, how to choose the inner product ...
Artificial Intelligence Algorithms Normal Distribution
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Comparison and Analysis of Several Clustering Algorithms for Pavement Crack Segmentation Guided by Computational Intelligence.

Computational intelligence and neuroscience Sep 3, 2022
Cracks are one of the most common types of imperfections that can be found in concrete pavement, and they have a significant influence on the structural strength. The purpose of this study is to investigate the performance differences of various spat...
Artificial Intelligence Algorithms Cluster Analysis Noise Normal Distribution
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Leveraging Theory for Enhanced Machine Learning.

ACS macro letters Aug 26, 2022
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is...
Machine Learning Polymers Solvents Normal Distribution
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Hierarchical and Self-Attended Sequence Autoencoder.

IEEE transactions on pattern analysis and machine intelligence Aug 4, 2022
It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded i...
Neural Networks, Computer Algorithms Learning Semantics Normal Distribution
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Return of the normal distribution: Flexible deep continual learning with variational auto-encoders.

Neural networks : the official journal of the International Neural Network Society Jul 21, 2022
Learning continually from sequentially arriving data has been a long standing challenge in machine learning. An emergent body of deep learning literature suggests various solutions, through introduction of significant simplifications to the problem s...
Machine Learning Bayes Theorem Normal Distribution Adaptation, Physiological
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Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space.

Journal of chemical theory and computation Jul 20, 2022
We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an ent...
Machine Learning Algorithms Cluster Analysis Normal Distribution
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Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.

IEEE transactions on neural networks and learning systems Jul 6, 2022
Causal discovery from observational data is a fundamental problem in science. Though the linear non-Gaussian acyclic model (LiNGAM) has shown promising results in various applications, it still faces the following challenges in the data with multiple...
Neural Networks, Computer Algorithms Linear Models Normal Distribution
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Metamodeling for Policy Simulations with Multivariate Outcomes.

Medical decision making : an international journal of the Society for Medical Decision Making Jun 23, 2022
PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We devel...
Humans Neural Networks, Computer Algorithms Policy Linear Models Normal Distribution
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