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 ...

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 ...

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 ...

This paper proposes a human pose estimation network to enhance the detection accuracy of human body keypoints. The network initially divides the input image into four sub-regions. Subsequently, these ...

We develop a suite of Deep Learning (DL) models, including One-Dimension (1D) Convolutional Neural Network (CNN) and fully-connected dense layers, for the detection of harmful events in noisy ...

Our previous study (Müller et al., 2020) compared results from a variety of DL methods including multilayer perceptron (MLP), RNN, long short term memory (LSTM), and 1D-convolutional neural network ...

The goal of this study is to detect online gaming addiction in young individuals using two types of neural networks: a novel recurrent neural network and a convolutional neural network. The study will ...