AIMC Topic: Electricity

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State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy.

Sensors (Basel, Switzerland)
Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles ...

Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks.

Sensors (Basel, Switzerland)
As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows mo...

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines.

PloS one
The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxi...

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review.

Sensors (Basel, Switzerland)
This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider's physical and physiological information, monitoring of their health state, and adjusting the "medical" assistance accord...

Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers.

Sensors (Basel, Switzerland)
This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimiz...

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data.

Sensors (Basel, Switzerland)
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-p...

Electric Shovel Teeth Missing Detection Method Based on Deep Learning.

Computational intelligence and neuroscience
Electric shovels are widely used in the mining industry to dig ore, and the teeth in shovels' bucket can be lost due to the tremendous pressure exerted by ore materials during operation. When the teeth fall off and enter the crusher with other ore ma...

The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China.

Computational intelligence and neuroscience
Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hou...

Large-Area Pixelized Optoelectronic Neuromorphic Devices with Multispectral Light-Modulated Bidirectional Synaptic Circuits.

Advanced materials (Deerfield Beach, Fla.)
The complete hardware implementation of an optoelectronic neuromorphic computing system is considered as one of the most promising solutions to realize energy-efficient artificial intelligence. Here, a fully light-driven and scalable optoelectronic n...

Ultrafast, High-Contractile Electrothermal-Driven Liquid Crystal Elastomer Fibers towards Artificial Muscles.

Small (Weinheim an der Bergstrasse, Germany)
Liquid crystal elastomer (LCE) fibers are capable of large and reversible deformations, making them an ideal artificial muscle. However, limited to stimulating source and structural design, current LCE fibers have not yet achieved both large contract...