Lithium-ion batteries (LIBs) play a significant role in various applications such as electric vehicles, portable electronic devices, and renewable energy storage systems. Accurately predicting the ...

Open-circuit faults in three-phase voltage source inverters can lead to unbalanced currents, high torque ripple, and excessive core losses for permanent magnet synchronous machines. To effectively ...

Using a 1-D convolutional neural network (1D-CNN) classifier for swimming pattern recognition at three kicking frequencies, the effectiveness of the method was experimentally validated by nine ...

The proposed approach utilizes the channel state information (CSI) measurements (complex values) from Wi-Fi and processes the different combinations of the real, imaginary, and absolute values using ...

Ensuring the quality of grapes is essential for producing premium wine. Grapes that are not sweet, excessively acidic, diseased, hybrid, or unripe can adversely affect the winemaking process. This ...

Accurate gas volume fraction (GVF) measurement in gas-liquid two-phase flow remains a key challenge in industrial process monitoring and control. In order to address this, a deep learning-based soft ...

Rolling bearing failure will affect the normal operation of the mechanical equipment. Effective early failure diagnosis can avoid unnecessary losses caused by bearing fault. A fault diagnostic method ...

A 1D Convolutional Neural Network was developed and validated using experimental data, achieving a classification accuracy of 97% in controlled scenarios. The architecture of the model balances ...

Brain Computer Interface (BCI) technologies use Electroencephalogram (EEG) impulses to provide direct interaction between the brain and external equipment, therefore presenting a revolutionary ...

This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural ...

A convolutional neural network (CNN) was trained on X-cut and Y-cut cross-sectional images of devices under different LER conditions to predict these performance metrics.

With the advancement of deep learning, and CNNs in particular, comes the ability to correctly classify plant diseases automatically with increased accuracy. This work provides a robust system by ...