Галерея 2973673

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O-MV-T-TSK-FS for EEG-Based Driver Drowsiness Estimation
Abstract: In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibra... View more
In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.
Date of Publication: 27 February 2020
References is not available for this document.

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Traffic accidents are one of the serious social problems facing the world at present. They have been recognized as the first public hazard that threatens the safety of human life in the world today. At least 500,000 people die each year due to traffic accidents [48]. In the statistics of the causes of traffic accidents in various countries, drowsy driving all occupies a large proportion [1]. According to the statistics from the Ministry of Transport of China in 2001 that about 48% of highway traffic accidents were caused by drowsy driving [48]. In the United States, 4,121 people were killed in road traffic accidents caused by drowsy driving between 2011 and 2015 [45]. In addition, the National Sleep Foundation poll reported that 4% of drivers admitted that they had an accident or near-accident because they were dozing off or too tired to drive [46]. Some researchers estimated by modeling that 15%–33% fatal accidents might involve drowsy drivers [47].
2018 International Joint Conference on Neural Networks (IJCNN)
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

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All Books Conferences Courses Journals & Magazines Standards Authors Citations
O-MV-T-TSK-FS for EEG-Based Driver Drowsiness Estimation
Abstract: In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibra... View more
In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.
Date of Publication: 27 February 2020
References is not available for this document.

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Traffic accidents are one of the serious social problems facing the world at present. They have been recognized as the first public hazard that threatens the safety of human life in the world today. At least 500,000 people die each year due to traffic accidents [48]. In the statistics of the causes of traffic accidents in various countries, drowsy driving all occupies a large proportion [1]. According to the statistics from the Ministry of Transport of China in 2001 that about 48% of highway traffic accidents were caused by drowsy driving [48]. In the United States, 4,121 people were killed in road traffic accidents caused by drowsy driving between 2011 and 2015 [45]. In addition, the National Sleep Foundation poll reported that 4% of drivers admitted that they had an accident or near-accident because they were dozing off or too tired to drive [46]. Some researchers estimated by modeling that 15%–33% fatal accidents might involve drowsy drivers [47].
2018 International Joint Conference on Neural Networks (IJCNN)
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

София Смит показывает свою фигуру прямо на кухне
Медсестра с красивыми титьками ебется с пациентом
Крепкий негр ублажил скучающую соседку

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