Advancing Workplace Safety: A Proactive Approach with Convolutional Neural Network for Hand Pose Estimation in Press Machine Operations DOI Creative Commons
Şuayip Aykut Atmaca, Hüseyin Hamad, Burcu Çağlar Gençosman

et al.

The European Journal of Research and Development, Journal Year: 2023, Volume and Issue: 3(4), P. 66 - 75

Published: Dec. 31, 2023

Press machine operations are integral to goods production across industries, yet worker safety faces significant risks. Machine misuse and non-compliance with standards contribute substantially these incidents. This study addresses the mounting concerns regarding workplace incidents through a proactive solution—a Convolutional Neural Network (CNN) model crafted prevent press by monitoring workers' hand placement during operation. The that we suggest ensures adherence standards. CNN does not replace role of human operators but acts as supportive layer, providing instant feedback intervention when deviations from detected. In conclusion, this research endeavors pave way for safer more secure industrial environment leveraging capabilities advanced technology. proposed current sets precedent future advancements in ensuring diverse industries.

Language: Английский

Electromyographic hand gesture recognition using convolutional neural network with multi-attention DOI
Zhen Zhang,

Quming Shen,

Yanyu Wang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 91, P. 105935 - 105935

Published: Jan. 10, 2024

Language: Английский

Citations

13
A temporal convolution and gated recurrent unit network with attention for state of charge estimation of lithium-ion batteries DOI
Kuo Yang, Yanyu Wang, Yugui Tang

et al.

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 72, P. 108774 - 108774

Published: Aug. 28, 2023

Language: Английский

Citations

19
A deep learning approach incorporating attention mechanism and transfer learning for lithium-ion battery lifespan prediction DOI

Wanjie Zhao,

Wei Ding, Shujing Zhang

et al.

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 75, P. 109647 - 109647

Published: Nov. 17, 2023

Language: Английский

Citations

15
Online cross session electromyographic hand gesture recognition using deep learning and transfer learning DOI
Zhen Zhang, Shilong Liu, Yanyu Wang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107251 - 107251

Published: Oct. 9, 2023

Language: Английский

Citations

13
A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation DOI
Zhen Zhang,

Yuewei Ming,

Yanyu Wang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108952 - 108952

Published: July 13, 2024

Language: Английский

Citations

5
A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends DOI

Sike Ni,

Mohammed A. A. Al‐qaness, Ammar Hawbani

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 166, P. 112235 - 112235

Published: Sept. 11, 2024

Language: Английский

Citations

4
Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle DOI Creative Commons
Shing-Hong Liu, Alok Kumar Sharma,

Bo-Yan Wu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 12, 2025

Language: Английский

Citations

0
Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training DOI
Yugui Tang, Kuo Yang, Shujing Zhang

et al.

Applied Energy, Journal Year: 2023, Volume and Issue: 355, P. 122266 - 122266

Published: Nov. 18, 2023

Language: Английский

Citations

10
Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer DOI Creative Commons
Reham Elshamy, Osama Abu-Elnasr, Mohamed Elhoseny

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 22, 2024

Abstract Optimizers are the bottleneck of training process any Convolutionolution neural networks (CNN) model. One critical steps when work on CNN model is choosing optimal optimizer to solve a specific problem. Recent challenge in nowadays researches building new versions traditional optimizers that can more efficient than optimizers. Therefore, this proposes novel enhanced version Adagrad called SAdagrad avoids drawbacks dealing with tuning learning rate value for each step process. In order evaluate SAdagrad, paper builds combines fine- technique and weight decay together. It trains proposed Kather colorectal cancer histology dataset which one most challenging datasets recent Diagnose Colorectal Cancer (CRC). fact, recently, there have been plenty deep models achieving successful results regard CRC classification experiments. However, enhancement these remains challenging. To train our model, transfer process, adopted from pre-complicated defined applied combined it regularization helps avoiding overfitting. The experimental show reaches remarkable accuracy (98%), compared Adaptive momentum (Adam) optimizer. experiments also reveal has stable testing processes, reduce overfitting problem multiple epochs achieve higher previous Diagnosis using same dataset.

Language: Английский

Citations

3
A federated transfer learning approach for lithium-ion battery lifespan early prediction considering privacy preservation DOI
Zhen Zhang, Yanyu Wang,

X. D. Ruan

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 102, P. 114153 - 114153

Published: Oct. 21, 2024

Language: Английский

Citations

3