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Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis.

Computational intelligence and neuroscience
With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and ...

Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm.

Computational intelligence and neuroscience
To enhance the visualization effect of substation high-voltage electrical equipment vulnerability, this study proposes an ISSA-LSTM coupled video overlay algorithm-based substation high-voltage electrical equipment vulnerability visualization and mon...

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis.

Computational intelligence and neuroscience
In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analy...

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm.

Computational intelligence and neuroscience
At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, ...

Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor.

Sensors (Basel, Switzerland)
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to iden...

Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks.

Sensors (Basel, Switzerland)
Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the ava...

Automated irreversible electroporated region prediction using deep neural network, a preliminary study for treatment planning.

Electromagnetic biology and medicine
The primary purpose of cancer treatment with irreversible electroporation (IRE) is to maximize tumor damage and minimize surrounding healthy tissue damage. Finite element analysis is one of the popular ways to calculate electric field and cell kill p...

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning.

The Journal of chemical physics
This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A MOB pairwise decomposition ...

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system.

Scientific reports
The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy s...

Liquid Metal-Elastomer Composites with Dual-Energy Transmission Mode for Multifunctional Miniature Untethered Magnetic Robots.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Miniature untethered robots attract growing interest as they have become more functional and applicable to disruptive biomedical applications recently. Particularly, the soft ones among them exhibit unique merits of compliance, versatility, and agili...