A regression method for EEG-based cross-dataset fatigue detection

Yuan, Duanyang; Yue, Jingwei; Xiong, Xuefeng; Jiang, Yang; Zan, Peng; Li, Chunyong

doi:10.3389/fphys.2023.1196919

Cited by 4 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantage of a dataset analysis is that the statistics fully cover the studied economic processes, with the error of results being at the minimum. The essence of the method of dataset analysis was described in many studies (e.g., Yuan et al 2023), and the specifics of using this method during the study of risks for companies were reflected in the work of Popkova and Sergi (2021) and Sozinova and Popkova (2023). The authors' datasets, formed based on the official international statistics of respectable sources-Fortune (2023) and IMD (2023)-can be found in a separate file, submitted with this paper.…”

Section: Methodsmentioning

confidence: 99%

Risks for Companies during the COVID-19 Crisis: Dataset Modelling and Management through Digitalisation

Skryl,

Gerasimova,

Chutcheva

et al. 2023

Risks

View full text Add to dashboard Cite

The goal is to create a systemic risk profile of companies during the COVID-19 crisis, which reflects their cause-and-effect relationships and risk management. The research objects are the following types of risks for companies listed in “Global-500” (Fortune) and the top 55 most competitive digital economies of the world (IMD) in 2017–2022: (1) risk of reduction in competitiveness (rank), (2) risk of reduction in revenue, and (3) risk of reduction in profit. The research methodology is based on the method of structural equation modelling (SEM), which allowed for exploring the cause-and-effect relationships between risk changes and digital risk management for companies during the COVID-19 crisis. As a result, based on the SEM model, it was proven that risks for companies during the COVID-19 crisis only slightly increased compared with that at the pre-crisis level. It was determined that companies faced large risks during the COVID-19 crisis in developed countries. It was discovered that, due to successful adaptation, risk management of companies assuaged the manifestations of the COVID-19 crisis in the economy. The key conclusion is that, under the conditions of a crisis of a non-economic nature (e.g., the COVID-19 crisis), companies independently and successfully manage their risks with the help of measures of digitalisation: corporate risk management with the limitation of state intervention is preferable. The contribution to the literature consists of the development of the concept of risks for companies by clarifying the specifics of risks and risk management of companies during the COVID-19 crisis. The theoretical significance lies in the fact that the authors’ conclusions rethought the risks for companies under the conditions of a crisis given the special context of a crisis of a non-economic nature (via the example of the COVID-19 crisis). The practical significance is that the developed novel approach to risk management of companies through digitalisation, which is based on the experience of the COVID-19 crisis, will be useful for risk management of companies under the conditions of future crises of non-economic nature caused by epidemics/pandemics and/or environmental disasters.

show abstract

Section: Methodsmentioning

confidence: 99%

Risks for Companies during the COVID-19 Crisis: Dataset Modelling and Management through Digitalisation

Skryl,

Gerasimova,

Chutcheva

et al. 2023

Risks

View full text Add to dashboard Cite

show abstract

Research on the application of the Sleep EEG Net model based on domain adaptation transfer in the detection of driving fatigue

Wang,

Gu,

Yao

2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Application Improvement of Deep Learning Algorithm in Small-Sized Fittings, Voltage Balancing Ring and Bare Conductor Detection of Transmission Lines

Zhou,

Tai,

Zhang

et al. 2023

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

As technological advancements progress and energy conservation and emission reduction policies gain traction, an increasing amount of clean energy is being integrated into the power grid system. This influx of new energy imposes stringent demands on the transmission lines within the power grid system. In recent years, the State Grid has implemented a plethora of intelligent transmission line inspection strategies, with the intelligent inspection of Unmanned Aerial Vehicle (UAV) transmission lines receiving significant promotion and widespread application. However, practical application has revealed that the prevalent transmission line detection algorithms yield a substantial quantity of false detections, particularly in the detection of nut defects in small-sized metallic fittings, voltage balancing ring defects, and defects in uninsulated conductors. To address these issues, this paper employs deep learning algorithms for target detection, critical point detection, and instance segmentation, focusing on aspects such as algorithmic logic, algorithmic models, and data processing. The aim is to enhance the precision of these three types of defect detection, diminish the rate of false detections, and augment the practicality of intelligent grid inspection.

show abstract

A regression method for EEG-based cross-dataset fatigue detection

Cited by 4 publications

References 43 publications

Risks for Companies during the COVID-19 Crisis: Dataset Modelling and Management through Digitalisation

Risks for Companies during the COVID-19 Crisis: Dataset Modelling and Management through Digitalisation

Research on the application of the Sleep EEG Net model based on domain adaptation transfer in the detection of driving fatigue

Application Improvement of Deep Learning Algorithm in Small-Sized Fittings, Voltage Balancing Ring and Bare Conductor Detection of Transmission Lines

Contact Info

Product

Resources

About